Prior to this change there were a number of places where the allocation and deallocation of SparseTensorCOO objects were not cleanly paired, leading to inconsistencies regarding whether each function released its tensor/coo arguments or not, as well as making it easy to run afoul of memory leaks, use-after-free, or double-free errors. This change cleans up the codegen vs runtime boundary to resolve those issues. Now, the only time the runtime library frees an object is either (a) because it's a function explicitly designed to do so, or (b) because the allocated object is entirely local to the function and would be a memory leak if not released. Thus, now the codegen takes complete responsibility for releasing any objects it caused to be allocated.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D122435
I'm using "shape" to mean the compile-time object, where zeros indicate sizes which are compile-time dynamic; and using "sizes" to mean the run-time object, where zeros indicate a dimension with no coordinates (hence resulting in trivial storage). Because their semantics differ on zeros, it's important to keep them distinguished. Although we do not define separate C++ types to capture the distinction, we can at least use variable names to do so.
This is (tangential) work towards fixing: https://github.com/llvm/llvm-project/issues/51652
Depends On D122057
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D122058
This is the more logical place for the function to live. If/when we factor out a separate class for just the `Coordinates` themselves, then the definition should be moved to `Coordinates::lexOrder` (and `Element::lexOrder` would become a thin wrapper delegating to that function).
This is (tangentially) work towards fixing: https://github.com/llvm/llvm-project/issues/51652
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D122057
This removes any potential confusion with the `getType` accessors
which correspond to SSA results of an operation, and makes it
clear what the intent is (i.e. to represent the type of the function).
Differential Revision: https://reviews.llvm.org/D121762
The enableObjectCache option was added in
https://reviews.llvm.org/rG06e8101034e, defaulting to false. However,
the init code added there got its logic reversed
(cache(enableObjectCache ? nullptr : new SimpleObjectCache()), which was
fixed in https://reviews.llvm.org/rGd1186fcb04 by setting the default to
true, thereby preserving the existing behavior even if it was
unintentional.
Default now the object cache to false as it was originally intended.
While at it, mention in enableObjectCache's documentation how the
cache can be dumped.
Reviewed-by: mehdi_amini
Differential Revision: https://reviews.llvm.org/D121291
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
There is no reason for this file to be at the top-level, and
its current placement predates the Parser/ folder's existence.
Differential Revision: https://reviews.llvm.org/D121024
Mark `parseSourceFile()` deprecated. The functions will be removed two weeks after landing this change.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D121075
This clarifies that these methods only work in append mode, not for general insertions. This is a prospective change towards https://github.com/llvm/llvm-project/issues/51652 which also performs random-access insertions, so we want to avoid confusion.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D120929
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
By specifying a sectionMemoryMapper, users can control how
memory for JIT code is allocated.
In particular, I need this in order to use a named memory
region so that profilers such as perf(1) can correctly label
execution cycles coming from JIT'ed code.
Reviewed-by: ezhulenev
Differential Revision: https://reviews.llvm.org/D120415
Its number of optional parameters has grown too large,
which makes adding new optional parameters quite a chore.
Fix this by using an options struct.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D120380
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
This op is added to allow MLIR code running on multi-GPU systems to
select the GPU they want to execute operations on when no GPU is
otherwise specified.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D119883
While testing LLVM 14.0.0 rc1 on Solaris, I ran into a compile failure:
from /var/llvm/llvm-14.0.0-rc1/rc1/llvm-project/mlir/lib/ExecutionEngine/SparseTensorUtils.cpp:22:
/usr/include/sys/types.h:103:16: error: conflicting declaration ‘typedef short int index_t’
103 | typedef short index_t;
| ^~~~~~~
In file included from
/var/llvm/llvm-14.0.0-rc1/rc1/llvm-project/mlir/lib/ExecutionEngine/SparseTensorUtils.cpp:17:
/var/llvm/llvm-14.0.0-rc1/rc1/llvm-project/mlir/include/mlir/ExecutionEngine/SparseTensorUtils.h:26:7:
note: previous declaration as ‘using index_t = uint64_t’
26 | using index_t = uint64_t;
| ^~~~~~~
The same issue had already occured in the past and fixed in D72619
<https://reviews.llvm.org/D72619>. More detailed explanation can also be
found there.
Tested on `amd64-pc-solaris2.11` and `sparcv9-solaris2.11`.
Differential Revision: https://reviews.llvm.org/D119323
Rationale:
Although file I/O is a bit alien to MLIR itself, we provide two convenient ways
for sparse tensor I/O. The input part was already there (behind the swiss army
knife sparse_tensor.new). Now we have a sparse_tensor.out to write out data. As
before, the ops are kept vague and may change in the future. For now this
allows us to compare TACO vs MLIR very easily.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D117850
This guarantees the preconditions of fromCOO; whereas prior to this, one could call the constructor directly with an unsorted tensor, which would cause fromCOO to misbehave.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D117167
Depends On D115008
This change opens the way for D115012, and removes some corner cases in `CodegenUtils.cpp`. The `SparseTensorAttrDefs.td` already specifies that we allow `0` bitwidth for the two overhead types and that it is interpreted to mean the architecture's native width.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D115010
By default, the listeners do nothing unless linked in.
This revision allows the "Perf" and "Intel" Jit event listeners to be used.
The "OProfile" event listener is not enabled at this time,
the associated library structure is not well-isolated.
Differential Revision: https://reviews.llvm.org/D116552
data point using the 3-dim tensor nell-2.tns
MLIR:
READ FILE INTO COO: 24424.369294 ms ---> improves to ----> 9638.501044 ms
SORT COO BEFORE PACK: 762.834831 ms
PACK COO TO TENSOR: 1243.376245 ms
TACO:
b file read: 13270.9 ms
b pack: 7137.74 ms
b size: (12092 x 9184 x 28818), 925300328 bytes
https://github.com/llvm/llvm-project/issues/52679
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D115696
Add convertFromMLIRSparseTensor to the supporting C shared library to convert
SparseTensorStorage to COO-flavor format.
Add Python routine sparse_tensor_to_coo_tensor to convert sparse tensor storage
pointer to numpy values for COO-flavor format tensor.
Add a Python test for sparse tensor output.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D115557
This revision implements sparse outputs (from scratch) in all cases where
the loops can be reordered with all but one parallel loops outer. If the
inner parallel loop appears inside one or more reductions loops, then an
access pattern expansion is required (aka. workspaces in TACO speak).
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D115091
