In order to run these integration tests, it is required access to an
SVE-enabled CPU or and emulator with SVE support. In case of using
an emulator, aarch64 versions of lli and the MLIR C Runner Utils Library
are also required.
Differential Revision: https://reviews.llvm.org/D104517
This commit moves FuncOp out of the builtin dialect, and into the Func
dialect. This move has been planned in some capacity from the moment
we made FuncOp an operation (years ago). This commit handles the
functional aspects of the move, but various aspects are left untouched
to ease migration: func::FuncOp is re-exported into mlir to reduce
the actual API churn, the assembly format still accepts the unqualified
`func`. These temporary measures will remain for a little while to
simplify migration before being removed.
Differential Revision: https://reviews.llvm.org/D121266
PyTACO DSL doesn't support the use of index values as in A[i] = B[i]+ i.
We extend the DSL to support such a use in MLIR-PyTACO.
Remove an obsolete unit test. Add unit tests and PyTACO tests.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D121716
Define IndexExpr before IndexVar. This is to prepare for the next change
to support the use of index values in tensor expressions.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D121649
The revision removes the linalg.fill operation and renames the OpDSL generated linalg.fill_tensor operation to replace it. After the change, all named structured operations are defined via OpDSL and there are no handwritten operations left.
A side-effect of the change is that the pretty printed form changes from:
```
%1 = linalg.fill(%cst, %0) : f32, tensor<?x?xf32> -> tensor<?x?xf32>
```
changes to
```
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<?x?xf32>) -> tensor<?x?xf32>
```
Additionally, the builder signature now takes input and output value ranges as it is the case for all other OpDSL operations:
```
rewriter.create<linalg::FillOp>(loc, val, output)
```
changes to
```
rewriter.create<linalg::FillOp>(loc, ValueRange{val}, ValueRange{output})
```
All other changes remain minimal. In particular, the canonicalization patterns are the same and the `value()`, `output()`, and `result()` methods are now implemented by the FillOpInterface.
Depends On D120726
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D120728
Add operations -, abs, ceil and floor to the index notation.
Add test cases.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D121388
This revision adds support for the linalg.index to the sparse compiler
pipeline. In essence, this adds the ability to refer to indices in
the tensor index expression, as illustrated below:
Y[i, j, k, l, m] = T[i, j, k, l, m] * i * j
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D121251
These passes generally don't rely on any special aspects of FuncOp, and moving allows
for these passes to be used in many more situations. The passes that obviously weren't
relying on invariants guaranteed by a "function" were updated to be generic pass, the
rest were updated to be FunctionOpinterface InterfacePasses.
The test updates are NFC switching from implicit nesting (-pass -pass2) form to
the -pass-pipeline form (generic passes do not implicitly nest as op-specific passes do).
Differential Revision: https://reviews.llvm.org/D121190
This is to align with the PyTACO API better.
Modify an existing unit test to test the new routines.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D121083
The current StandardToLLVM conversion patterns only really handle
the Func dialect. The pass itself adds patterns for Arithmetic/CFToLLVM, but
those should be/will be split out in a followup. This commit focuses solely
on being an NFC rename.
Aside from the directory change, the pattern and pass creation API have been renamed:
* populateStdToLLVMFuncOpConversionPattern -> populateFuncToLLVMFuncOpConversionPattern
* populateStdToLLVMConversionPatterns -> populateFuncToLLVMConversionPatterns
* createLowerToLLVMPass -> createConvertFuncToLLVMPass
Differential Revision: https://reviews.llvm.org/D120778
These unit tests resides in an internal repository. Porting the tests to the
public repository.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D121021
sparsity values.
Previously, we can't properly handle input tensors with a dimension
ordering that is different from the natural ordering or with a mixed of
compressed and dense dimensions. This change fixes the problems by
passing the dimension ordering and sparsity values to the runtime
routine.
Modify an existing test to test the situation.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120777
The last remaining operations in the standard dialect all revolve around
FuncOp/function related constructs. This patch simply handles the initial
renaming (which by itself is already huge), but there are a large number
of cleanups unlocked/necessary afterwards:
* Removing a bunch of unnecessary dependencies on Func
* Cleaning up the From/ToStandard conversion passes
* Preparing for the move of FuncOp to the Func dialect
See the discussion at https://discourse.llvm.org/t/standard-dialect-the-final-chapter/6061
Differential Revision: https://reviews.llvm.org/D120624
Previously, convertToMLIRSparseTensor assumes identity storage ordering and all
compressed dimensions. This change extends the function with two parameters for
users to specify the storage ordering and the sparsity of each dimension.
Modify PyTACO to reflect this change.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120643
The PyTACO DSL doesn't support reduction to scalars. This change
enhances the MLIR-PyTACO implementation to support reduction to scalars.
Extend an existing test to show the syntax of reduction to scalars and
two methods to retrieve the scalar values.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120572
Fix MLIR-PyTACO and some tests to use np.array_equal to compare integer
values.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120526
Split arithmetic function into unary and binary functions. The revision prepares the introduction of unary and binary function attributes that work similar to type function attributes.
Depends On D120108
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120109
This change allows the use of scalar tensors with index 0 in tensor index
expressions. In this case, the scalar value is broadcast to match the
dimensions of other tensors in the same expression.
Using scalar tensors as a destination in tensor index expressions is not
supported in the PyTACO DSL.
Add a PyTACO test to show the use of scalar tensors.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120524
Previously, OpDSL operation used hardcoded type conversion operations (cast or cast_unsigned). Supporting signed and unsigned casts thus meant implementing two different operations. Type function attributes allow us to define a single operation that has a cast type function attribute which at operation instantiation time may be set to cast or cast_unsigned. We may for example, defina a matmul operation with a cast argument:
```
@linalg_structured_op
def matmul(A=TensorDef(T1, S.M, S.K), B=TensorDef(T2, S.K, S.N), C=TensorDef(U, S.M, S.N, output=True),
cast=TypeFnAttrDef(default=TypeFn.cast)):
C[D.m, D.n] += cast(U, A[D.m, D.k]) * cast(U, B[D.k, D.n])
```
When instantiating the operation the attribute may be set to the desired cast function:
```
linalg.matmul(lhs, rhs, outs=[out], cast=TypeFn.cast_unsigned)
```
The revsion introduces a enum in the Linalg dialect that maps one-by-one to the type functions defined by OpDSL.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D119718
Previously, we only support float64. We now support float32 and float64. When
constructing a tensor without providing a data type, the default is float32.
Fix the tests to data type consistency. All PyTACO application tests now use
float32 to match the default data type of TACO. Other tests may use float32 or
float64.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120356
Now that sparse tensor types are first-class citizens and the sparse compiler
is taking shape, it is time to make sure other compiler optimizations compose
well with sparse tensors. Mostly, this should be completely transparent (i.e.,
dense and sparse take the same path). However, in some cases, optimizations
only make sense in the context of sparse tensors. This is a first example of
such an optimization, where fusing a sampled elt-wise multiplication only makes
sense when the resulting kernel has a potential lower asymptotic complexity due
to the sparsity.
As an extreme example, running SDDMM with 1024x1024 matrices and a sparse
sampling matrix with only two elements runs in 463.55ms in the unfused
case but just 0.032ms in the fused case, with a speedup of 14485x that
is only possible in the exciting world of sparse computations!
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D120429
These routines will need to be specialized a lot more based on value types,
index types, pointer types, and permutation/dimension ordering. This is a
careful first step, providing some functionality needed in PyTACO bridge.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D120154
It is time to compose Linalg related optimizations with SparseTensor
related optimizations. This is a careful first start by adding some
general Linalg optimizations "upstream" of the sparse compiler in the
full sparse compiler pipeline. Some minor changes were needed to make
those optimizations aware of sparsity.
Note that after this, we will add a sparse specific fusion rule,
just to demonstrate the power of the new composition.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D119971
This test shows that when access patterns do not match (e.g. transposing
a row-wise sparse matrix into another row-wise sparse matrix), a conversion
operation in between can enable codegen (i.e. avoid cycle in iteration graph).
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D119864
The only method to create a true dense tensor (i.e un-annotated) in MLIR-PyTACO
is through the from_array method. However, the annotated all dense tensors are
also implemented as true dense tensor currently. The PR fixes the
implementation to support annotated all dense sparse tensors.
Extend the tensor init method to support the construction of a tensor without
any sparsity annotation.
Change the tensor to_file method to only support writing unpacked sparse
tensors to file through the MLIR sparse tensor dialect.
Add unit tests for true dense tensors and all dense sparse tensors.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D119500
removed obsoleted TODO
removed strange Fp precision for coordinates
lined up meta data testing code for readability
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D119377
Add a Python method, output_sparse_tensor, to use sparse_tensor.out to write
a sparse tensor value to a file.
Modify the method that evaluates a tensor expression to return a pointer of the
MLIR sparse tensor for the result to delay the extraction of the coordinates and
non-zero values.
Implement the Tensor to_file method to evaluate the tensor assignment and write
the result to a file.
Add unit tests. Modify test golden files to reflect the change that TNS outputs
now have a comment line and two meta data lines.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D118956
Replace the Python implementation for reading tensor input data from files with
create_sparse_tensor that uses sparse_tensor.new.
The MLIR TNS format has two extra meta data lines. Add the extra meta data to a
test data file.
Implement TACO tensor methods evaluate and unpack.
Add unit tests.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D118803
This is part of the larger effort to split the standard dialect. This will also allow for pruning some
additional dependencies on Standard (done in a followup).
Differential Revision: https://reviews.llvm.org/D118202
Move the functions that retrieve the supporting C library, compile an MLIR
module and build a JIT execution engine to mlir_pytaco_utils.
Add a function to create an MLIR sparse tensor from a file and return a pointer
to the MLIR sparse tensor as well as the shape of the sparse tensor.
Add unit tests.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D118496
