The StringAttr version doesn't need a context, so we can just use the
existing `SymbolRefAttr::get` form. The StringRef version isn't preferred
so we want to encourage people to use StringAttr.
There is an additional form of getSymbolRefAttr that takes a (SymbolTrait
implementing) operation. This should also be moved, but I'll do that as
a separate patch.
Differential Revision: https://reviews.llvm.org/D108922
SymbolRefAttr is fundamentally a base string plus a sequence
of nested references. Instead of storing the string data as
a copies StringRef, store it as an already-uniqued StringAttr.
This makes a lot of things simpler and more efficient because:
1) references to the symbol are already stored as StringAttr's:
there is no need to copy the string data into MLIRContext
multiple times.
2) This allows pointer comparisons instead of string
comparisons (or redundant uniquing) within SymbolTable.cpp.
3) This allows SymbolTable to hold a DenseMap instead of a
StringMap (which again copies the string data and slows
lookup).
This is a moderately invasive patch, so I kept a lot of
compatibility APIs around. It would be nice to explore changing
getName() to return a StringAttr for example (right now you have
to use getNameAttr()), and eliminate things like the StringRef
version of getSymbol.
Differential Revision: https://reviews.llvm.org/D108899
Needed to switch to extract to support tosa.reverse using dynamic shapes.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D108744
Includes the quantized version of average pool lowering to linalg dialect.
This includes a lit test for the transform. It is not 100% correct as the
multiplier / shift should be done in i64 however this is negligable rounding
difference.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D108676
Lowering to table was incorrect as it did not apply a 128 offset before
extracting the value from the table. Fixed and correct tensor length on input
table.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D108436
When padding quantized operations, the padding needs to equal the zero point
of the input value. Corrected the pass to change the padding value if quantized.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D108440
This revision adds native ODS support for VariadicOfVariadic operand
groups. An example of this is the SwitchOp, which has a variadic number
of nested operand ranges for each of the case statements, where the
number of case statements is variadic. Builtin ODS support allows for
generating proper accessors for the nested operand ranges, builder
support, and declarative format support. VariadicOfVariadic operands
are supported by providing a segment attribute to use to store the
operand groups, mapping similarly to the AttrSizedOperand trait
(but with a user defined attribute name).
`build` methods for VariadicOfVariadic operand expect inputs of the
form `ArrayRef<ValueRange>`. Accessors for the variadic ranges
return a new `OperandRangeRange` type, which represents a
contiguous range of `OperandRange`. In the declarative assembly
format, VariadicOfVariadic operands and types are by default
formatted as a comma delimited list of value lists:
`(<value>, <value>), (), (<value>)`.
Differential Revision: https://reviews.llvm.org/D107774
Presently, the lowering of nested scf.parallel loops to OpenMP creates one omp.parallel region, with two (nested) OpenMP worksharing loops on the inside. When lowered to LLVM and executed, this results in incorrect results. The reason for this is as follows:
An OpenMP parallel region results in the code being run with whatever number of threads available to OpenMP. Within a parallel region a worksharing loop divides up the total number of requested iterations by the available number of threads, and distributes accordingly. For a single ws loop in a parallel region, this works as intended.
Now consider nested ws loops as follows:
omp.parallel {
A: omp.ws %i = 0...10 {
B: omp.ws %j = 0...10 {
code(%i, %j)
}
}
}
Suppose we ran this on two threads. The first workshare loop would decide to execute iterations 0, 1, 2, 3, 4 on thread 0, and iterations 5, 6, 7, 8, 9 on thread 1. The second workshare loop would decide the same for its iteration. This means thread 0 would execute i \in [0, 5) and j \in [0, 5). Thread 1 would execute i \in [5, 10) and j \in [5, 10). This means that iterations i in [5, 10), j in [0, 5) and i in [0, 5), j in [5, 10) never get executed, which is clearly wrong.
This permits two options for a remedy:
1) Change the semantics of the omp.wsloop to be distinct from that of the OpenMP runtime call or equivalently #pragma omp for. This could then allow some lowering transformation to remedy the aforementioned issue. I don't think this is desirable for an abstraction standpoint.
2) When lowering an scf.parallel always surround the wsloop with a new parallel region (thereby causing the innermost wsloop to use the number of threads available only to it).
This PR implements the latter change.
Reviewed By: jdoerfert
Differential Revision: https://reviews.llvm.org/D108426
Tosa rescale can contain uint8 types. Added support for these types
using an unrealized conversion cast. Optimistically it would be better to
use bitcast however it does not support unsigned integers.
Differential Revision: https://reviews.llvm.org/D108427
Its possible for the clamp to have invalid min/max values on its range. To fix
this we validate the range of the min/max and clamp to a valid range.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D108256
LLVM considers global variables marked as externals to be defined within the module if it is initialized (including to an undef). Other external globals are considered as being defined externally and imported into the current translation unit. Lowering of MLIR Global Ops does not properly propagate undefined initializers, resulting in a global which is expected to be defined within the current TU, not being defined.
Differential Revision: https://reviews.llvm.org/D108252
Existing linalg.conv2d is not well optimized for performance. Changed to a
version that is more aligned for optimziation. Include the corresponding
transposes to use this optimized version.
This also splits the conv and depthwise conv into separate implementations
to avoid overly complex lowerings.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D107504
The conversion is a straightforward one-to-one mapping with optional unrolling
for nD vectors, similarly to other cast operations.
Depends On D107889
Reviewed By: cota, akuegel
Differential Revision: https://reviews.llvm.org/D107891
Dilation only requires increasing the padding on the left/right side of the
input, and including dilation in the convolution. This implementation still
lacks support for strided convolutions.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D107680
These ops were not ported to the nD vector conversion when it was introduced
and nobody needed them so far.
Reviewed By: gysit
Differential Revision: https://reviews.llvm.org/D107750
If the source value to load is bool, and we have native storage
capability support for the source bitwidth, we still cannot directly
rewrite uses; we need to perform casting to bool first.
Reviewed By: hanchung
Differential Revision: https://reviews.llvm.org/D107119
If the source value to store is bool, and we have native storage
capability support for the target bitwidth, we still cannot directly
store; we need to perform casting to match the target memref
element's bitwidth.
Reviewed By: hanchung
Differential Revision: https://reviews.llvm.org/D107114
Make broadcastable needs the output shape to determine whether the operation
includes additional broadcasting. Include some canonicalizations for TOSA
to remove unneeded reshape.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D106846
The verifier of the llvm.call operation was not checking for mismatches between
the number of operation results and the number of results in the signature of
the callee. Furthermore, it was possible to construct an llvm.call operation
producing an SSA value of !llvm.void type, which should not exist. Add the
verification and treat !llvm.void result type as absence of call results.
Update the GPU conversions to LLVM that were mistakenly assuming that it was
fine for llvm.call to produce values of !llvm.void type and ensure these calls
do not produce results.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D106937
- Fixed symbol insertion into `symNameToModuleMap`. Insertion
needs to happen whether symbols are renamed or not.
- Added check for the VCE triple and avoid dropping it.
- Disabled function deduplication. It requires more careful
rules. Right now it can remove different functions.
- Added tests for symbol rename listener.
- And some other code/comment cleanups.
Reviewed By: ergawy
Differential Revision: https://reviews.llvm.org/D106886
Includes a version of a quantized conv2D operations with a lowering from TOSA
to linalg with corresponding test. We keep the quantized and quantized variants
as separate named ops to avoid the additional operations for non-quantized
convolutions.
Differential Revision: https://reviews.llvm.org/D106407
Type conversion and argument materialization are context-free: there is no available information on which op / branch is currently being converted.
As a consequence, bare ptr convention cannot be handled as an argument materialization: it would apply irrespectively of the parent op.
This doesn't typecheck in the case of non-funcOp and we would see cases where a memref descriptor would be inserted in place of the pointer in another memref descriptor.
For now the proper behavior is to revert to a specific BarePtrFunc implementation and drop the blanket argument materialization logic.
This reverts the relevant piece of the conversion to LLVM to what it was before https://reviews.llvm.org/D105880 and adds a relevant test and documentation to avoid the mistake by whomever attempts this again in the future.
Reviewed By: arpith-jacob
Differential Revision: https://reviews.llvm.org/D106495
The unstrided transposed conv can be represented as a regular convolution.
Lower to this variant to handle the basic case. This includes transitioning from
the TC defined convolution operation and a yaml defined one.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D106389
Added the named op variants for quantized matmul and quantized batch matmul
with the necessary lowerings/tests from tosa's matmul/fully connected ops.
Current version does not use the contraction op interface as its verifiers
are not compatible with scalar operations.
Differential Revision: https://reviews.llvm.org/D105063
This deletes all the pooling ops in LinalgNamedStructuredOpsSpec.tc. All the
uses are replaced with the yaml pooling ops.
Reviewed By: gysit, rsuderman
Differential Revision: https://reviews.llvm.org/D106181
This simplifies the vector to LLVM lowering. Previously, both vector.load/store and vector.transfer_read/write lowered directly to LLVM. With this commit, there is a single path to LLVM vector load/store instructions and vector.transfer_read/write ops must first be lowered to vector.load/store ops.
* Remove vector.transfer_read/write to LLVM lowering.
* Allow non-unit memref strides on all but the most minor dimension for vector.load/store ops.
* Add maxTransferRank option to populateVectorTransferLoweringPatterns.
* vector.transfer_reads with changing element type can no longer be lowered to LLVM. (This functionality is needed only for SPIRV.)
Differential Revision: https://reviews.llvm.org/D106118
The dialect-specific cast between builtin (ex-standard) types and LLVM
dialect types was introduced long time before built-in support for
unrealized_conversion_cast. It has a similar purpose, but is restricted
to compatible builtin and LLVM dialect types, which may hamper
progressive lowering and composition with types from other dialects.
Replace llvm.mlir.cast with unrealized_conversion_cast, and drop the
operation that became unnecessary.
Also make unrealized_conversion_cast legal by default in
LLVMConversionTarget as the majority of convesions using it are partial
conversions that actually want the casts to persist in the IR. The
standard-to-llvm conversion, which is still expected to run last, cleans
up the remaining casts standard-to-llvm conversion, which is still
expected to run last, cleans up the remaining casts
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D105880
After the Math has been split out of the Standard dialect, the
conversion to the LLVM dialect remained as a huge monolithic pass.
This is undesirable for the same complexity management reasons as having
a huge Standard dialect itself, and is even more confusing given the
existence of a separate dialect. Extract the conversion of the Math
dialect operations to LLVM into a separate library and a separate
conversion pass.
Reviewed By: silvas
Differential Revision: https://reviews.llvm.org/D105702
After the MemRef has been split out of the Standard dialect, the
conversion to the LLVM dialect remained as a huge monolithic pass.
This is undesirable for the same complexity management reasons as having
a huge Standard dialect itself, and is even more confusing given the
existence of a separate dialect. Extract the conversion of the MemRef
dialect operations to LLVM into a separate library and a separate
conversion pass.
Reviewed By: herhut, silvas
Differential Revision: https://reviews.llvm.org/D105625
This class and classes that extend it are general utilities for any dialect
that is being converted into the LLVM dialect. They are in no way specific to
Standard-to-LLVM conversion and should not make their users depend on it.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D105542
