The module flag to indicate use of hostcall is insufficient to catch
all cases where hostcall might be in use by a kernel. This is now
replaced by a function attribute that gets propagated to top-level
kernel functions via their respective call-graph.
If the attribute "amdgpu-no-hostcall-ptr" is absent on a kernel, the
default behaviour is to emit kernel metadata indicating that the
kernel uses the hostcall buffer pointer passed as an implicit
argument.
The attribute may be placed explicitly by the user, or inferred by the
AMDGPU attributor by examining the call-graph. The attribute is
inferred only if the function is not being sanitized, and the
implictarg_ptr does not result in a load of any byte in the hostcall
pointer argument.
Reviewed By: jdoerfert, arsenm, kpyzhov
Differential Revision: https://reviews.llvm.org/D119216
When lowering to memrefCopy call, the size for i1 type was calculated as 0.
Instead of using getTypeSizeInBits() and dividing by 8, we should just use getTypeSize().
Differential Revision: https://reviews.llvm.org/D119540
The lowering creates llvm.insertvalue with the rank value, so it needs to use
index type instead of 64 bit integer type. Otherwise, we get an error:
llvm.insertvalue' op Type mismatch: cannot insert 'i64' into '!llvm.struct<(i32, ptr<i8>)>'
Differential Revision: https://reviews.llvm.org/D119534
This patch simply adds an optional garbage collector attribute to LLVMFuncOp which maps 1:1 to the "gc" property of functions in LLVM.
Differential Revision: https://reviews.llvm.org/D119492
This allows operations to control the block ids used by the printer in nested regions.
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D115849
Previously, OpDSL did not support rank polymorphism, which required a separate implementation of linalg.fill. This revision extends OpDSL to support rank polymorphism for a limited class of operations that access only scalars and tensors of rank zero. At operation instantiation time, it scales these scalar computations to multi-dimensional pointwise computations by replacing the empty indexing maps with identity index maps. The revision does not change the DSL itself, instead it adapts the Python emitter and the YAML generator to generate different indexing maps and and iterators depending on the rank of the first output.
Additionally, the revision introduces a `linalg.fill_tensor` operation that in a future revision shall replace the current handwritten `linalg.fill` operation. `linalg.fill_tensor` is thus only temporarily available and will be renamed to `linalg.fill`.
Reviewed By: nicolasvasilache, stellaraccident
Differential Revision: https://reviews.llvm.org/D119003
Add new operations to the gpu dialect to represent device side
asynchronous copies. This also add the lowering of those operations to
nvvm dialect.
Those ops are meant to be low level and map directly to llvm dialects
like nvvm or rocdl.
We can further add higher level of abstraction by building on top of
those operations.
This has been discuss here:
https://discourse.llvm.org/t/modeling-gpu-async-copy-ampere-feature/4924
Differential Revision: https://reviews.llvm.org/D119191
These functions allow for defining pattern fragments usable within the `match` and `rewrite` sections of a pattern. The main structure of Constraints and Rewrites functions are the same, and are similar to functions in other languages; they contain a signature (i.e. name, argument list, result list) and a body:
```pdll
// Constraint that takes a value as an input, and produces a value:
Constraint Cst(arg: Value) -> Value { ... }
// Constraint that returns multiple values:
Constraint Cst() -> (result1: Value, result2: ValueRange);
```
When returning multiple results, each result can be optionally be named (the result of a Constraint/Rewrite in the case of multiple results is a tuple).
These body of a Constraint/Rewrite functions can be specified in several ways:
* Externally
In this case we are importing an external function (registered by the user outside of PDLL):
```pdll
Constraint Foo(op: Op);
Rewrite Bar();
```
* In PDLL (using PDLL constructs)
In this case, the body is defined using PDLL constructs:
```pdll
Rewrite BuildFooOp() {
// The result type of the Rewrite is inferred from the return.
return op<my_dialect.foo>;
}
// Constraints/Rewrites can also implement a lambda/expression
// body for simple one line bodies.
Rewrite BuildFooOp() => op<my_dialect.foo>;
```
* In PDLL (using a native/C++ code block)
In this case the body is specified using a C++(or potentially other language at some point) code block. When building PDLL in AOT mode this will generate a native constraint/rewrite and register it with the PDL bytecode.
```pdll
Rewrite BuildFooOp() -> Op<my_dialect.foo> [{
return rewriter.create<my_dialect::FooOp>(...);
}];
```
Differential Revision: https://reviews.llvm.org/D115836
This allows for defining simple patterns in a single line. The lambda
body of a Pattern expects a single operation rewrite statement:
```
Pattern => replace op<my_dialect.foo>(operands: ValueRange) with operands;
```
Differential Revision: https://reviews.llvm.org/D115835
Having clarified that executing the SerializeToHsaco pass can
depend on a ROCm installation, switch from calling lld as a library to
using the copy of lld guaranteed to be included in a ROCm install.
This removes the workaround introduced in D119277
Reviewed By: whchung
Differential Revision: https://reviews.llvm.org/D119463
If the result operand has a unit leading dim it is removed from all operands.
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D119206
This patch factors out space information from IntegerPolyhedron, PresburgerSet
and PWMAFunction to PresburgerSpace and its extension with local variables,
PresburgerLocalSpace.
Generally any new data structure additions in Presburger library will require
space information. This patch removes the need to duplicate the space
information.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D119280
Fix fold-memref-subview-ops for affine.load/store. We need to expand out
the affine apply on its operands.
Differential Revision: https://reviews.llvm.org/D119402
Move expandAffineMap and expandAffineApplyExpr out to AffineUtils. This
is a useful method. The child revision uses it. NFC.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D119401
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
TypeIDAllocator enables the allocation of new TypeIDs at runtime,
that are unique during the lifetime of the allocator.
NonMovableTypeIDOwner is a class used to define a new TypeID for each instance
of a class, using the instance address. This class cannot be copied or moved.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D104534
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
This makes getAliasingOpResult symmetric to getAliasingOpOperand. The previous implementation was confusing for users and implemented in such a way only because there are currently no bufferizable ops that have multiple aliasing OpResults.
Differential Revision: https://reviews.llvm.org/D119259
They used to be classes with a virtual `run` function. This was inconvenient because post analysis steps are stored in BufferizationOptions. Because of this design choice, BufferizationOptions were not copyable.
Differential Revision: https://reviews.llvm.org/D119258
Supports whitespace elements: ` ` and `\\n` as well as the "empty" whitespace `` that removes an otherwise printed space.
Depends on D118208
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D118210
Reuse the higher precision F32 approximation for the F16 one (by expanding and
truncating). This is partly RFC as I'm not sure what the expectations are here
(e.g., these are only for F32 and should not be expanded, that reusing
higher-precision ones for lower precision is undesirable due to increased
compute cost and only approximations per exact type is preferred, or this is
appropriate [at least as fallback] but we need to see how to make it more
generic across all the patterns here).
Differential Revision: https://reviews.llvm.org/D118968
Implements optional attribute or type parameters, including support for such parameters in the assembly format `struct` directive. Also implements optional groups.
Depends on D117971
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D118208
For 0-D as well as 1-D vectors, both these patterns should
return a failure as there is no need to collapse the shape
of the source. Currently, only 1-D vectors were handled. This
patch handles the 0-D case as well.
Reviewed By: Benoit, ThomasRaoux
Differential Revision: https://reviews.llvm.org/D119202
There are a few different test passes that check elementwise fusion in
Linalg. Consolidate them to a single pass controlled by different pass
options (in keeping with how `TestLinalgTransforms` exists).
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
The conversion to the new ControlFlow dialect didn't change the
GPUToROCDL pass - this commit fixes this issue.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D119188
Add support for computing an overapproximation of the number of integer points
in a polyhedron. The returned result is actually the number of integer points
one gets by computing the "rational shadow" obtained by projecting out the
local IDs, finding the minimal axis-parallel hyperrectangular approximation
of the shadow, and returning the number of integer points in that. This does
not currently support symbols.
Reviewed By: Groverkss
Differential Revision: https://reviews.llvm.org/D119228
Matthias Springer