This commit fixes `Tensor_InsertSliceOp` `sizes` inputs/attributes
description.
Before this commit, the description says the `sizes` inputs/attributes
denote the size of the return type. But according to the
`InsertSliceOpConstantArgumentFolder` in
`lib/Dialect/Tensor/IR/TensorOps.cpp`, the `sizes` inputs/attributes
actually denote the size of the source type.
I had an off-line discussion with the authors of `TensorOps.td` and
`TensorOps.cpp`. We concluded that it was a typo in the Op description.
This commit updates the Op description to match the actual usage.
Differential Revision: https://reviews.llvm.org/D126264
This patch adds support for obtaining a set corresponding to the domain/range
of the relation.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D126326
This commit breaks down diagnostic string literals so that the attribute
name and enumurator names can be shared with the stringify utility
function and the "expected ", " to be one of ", and ", " can be shared
between different enum-related diagnostic.
Differential Revision: https://reviews.llvm.org/D125938
Add lowering for cases where the reduction dimension is fully unrolled.
It is common to unroll the reduction dimension, therefore we would want
to lower the contractions to an elementwise vector op in this case.
Differential Revision: https://reviews.llvm.org/D126120
No longer pass static dim sizes as an attribute. This was redundant and required extra checks in the verifier. This change also makes the op symmetrical to memref::AllocOp.
Differential Revision: https://reviews.llvm.org/D126178
This change adds a new op `alloc_tensor` to the bufferization dialect. During bufferization, this op is always lowered to a buffer allocation (unless it is "eliminated" by a pre-processing pass). It is useful to have such an op in tensor land, because it allows users to model tensor SSA use-def chains (which drive bufferization decisions) and because tensor SSA use-def chains can be analyzed by One-Shot Bufferize, while memref values cannot.
This change also replaces all uses of linalg.init_tensor in bufferization-related code with bufferization.alloc_tensor.
linalg.init_tensor and bufferization.alloc_tensor are similar, but the purpose of the former one is just to carry a shape. It does not indicate a memory allocation.
linalg.init_tensor is not suitable for modelling SSA use-def chains for bufferization purposes, because linalg.init_tensor is marked as not having side effects (in contrast to alloc_tensor). As such, it is legal to move linalg.init_tensor ops around/CSE them/etc. This is not desirable for alloc_tensor; it represents an explicit buffer allocation while still in tensor land and such allocations should not suddenly disappear or get moved around when running the canonicalizer/CSE/etc.
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Differential Revision: https://reviews.llvm.org/D126003
This changes adds the option to lower to NvGpu dialect ops during the
VectorToGPU convsersion pass. Because this transformation reuses
existing VectorToGPU logic, a seperate VectorToNvGpu conversion pass is
not created. The option `use-nvgpu` is added to the VectorToGPU pass.
When this is true, the pass will attempt to convert slices rooted at
`vector.contract` operations into `nvgpu.mma.sync` ops, and
`vector.transfer_read` ops are converted to either `nvgpu.ldmatrix` or
one or more `vector.load` operations. The specific data loaded will
depend on the thread id within a subgroup (warp). These index
calculations depend on data type and shape of the MMA op
according to the downstream PTX specification. The code for supporting
these details is separated into `NvGpuSupport.cpp|h`.
Differential Revision: https://reviews.llvm.org/D122940
The approach I took was to define a dialect 'extern' attribute that a GlobalOp can take as a value to signify external linkage. I think this approach should compose well and should also work with wherever the OpaqueElements work goes in the future (since that is just another kind of attribute). I special cased the GlobalOp parser/printer for this case because it is significantly easier on the eyes.
In the discussion, Jeff Niu had proposed an alternative syntax for GlobalOp that I ended up not taking. I did try to implement it but a) I don't think it made anything easier to read in the common case, and b) it made the parsing/printing logic a lot more complicated (I think I would need a completely custom parser/printer to do it well). Please have a look at the common cases where the global type and initial value type match: I don't think how I have it is too bad. The less common cases seem ok to me.
I chose to only implement the direct, constant load op since that is non side effecting and there was still discussion pending on that.
Differential Revision: https://reviews.llvm.org/D124318
The current behaviour of `useDefaultTypePrinterParser` and `useDefaultAttributePrinterParser` is that they are set by default, but the dialect generator only generates the declarations for the parsing and printing hooks if it sees dialect types and attributes. Same goes for the definitions generated by the AttrOrTypeDef generator.
This can lead to confusing and undesirable behaviour if the dialect generator doesn't see the definitions of the attributes and types, for example, if they are sensibly separated into different files: `Dialect.td`, `Ops.td`, `Attributes.td`, and `Types.td`.
Now, these bits are unset by default. Setting them will always result in the dialect generator emitting the declarations for the parsing hooks. And if the AttrOrTypeDef generator sees it set, it will generate the default implementations.
Reviewed By: rriddle, stellaraccident
Differential Revision: https://reviews.llvm.org/D125809
Lowering through libm gives us a baseline version, even though it's not
going to be particularly fast. This is similar to what we do for some
math dialect ops.
Differential Revision: https://reviews.llvm.org/D125550
This patch cleans up multiple getMaybeValue functions to take an IdKind instead
of special functions.
Reviewed By: arjunp
Differential Revision: https://reviews.llvm.org/D125617
This patch changes `FlatAffineValueConstraints` to only allow attaching
values to non-local identifiers.
The reasoning for this change is:
1. Information attached to local identifiers can be lost since local identifiers
can be removed for output size optimizations.
2. There are no current use cases for attaching values to Local identifiers.
3. Attaching a value to a local identifier does not make sense since a local
identifier represents existential quantification.
This patch also adds some additional asserts to the affected functions.
Reviewed By: arjunp, bondhugula
Differential Revision: https://reviews.llvm.org/D125613
Added handling rounding behavior in 32-bits for when possible. This
avoids kernel compilation generating scalarized code on platforms where
64-bit vectors are not available.
As the 48-bit lowering requires 64-bit anyway, we added a full 64-bit
solution simplifying the old path.
Reviewed By: dcaballe, mravishankar
Differential Revision: https://reviews.llvm.org/D125583
Before this fix, the bufferization implementation made the incorrect assumption that the values yielded from the "before" region must match with the values yielded from the "after" region.
Differential Revision: https://reviews.llvm.org/D125835
This diff updates the LLVMIR dialect Fastmath flags attribute to use recently
added features of `BitEnum` attributes. Specifically, this diff uses the bit
enum "group" case to represent the `fast` value as an alias for a combination
of other values (`ninf`, `nnan`, ...), instead of using a separate integer
value. (This is in line with LLVM's fastmath flags representation.) This diff
also leverages the `printBitEnumPrimaryGroups` `tblgen` field for concise
enum printing.
The `BitEnum` features were developed for an upcoming diff that adds `fastmath`
support to the arithmetic dialect. This diff simply applies some of the relevant
new features to the LLVM dialect attribute.
Reviewed By: ftynse, Mogball
Differential Revision: https://reviews.llvm.org/D124720
Previously, GEPOp relies on `findKnownStructIndices` to check if a GEP
index should be static. The truth is, `findKnownStructIndices` can only
tell you a GEP index _might_ be indexing into a struct (which should use
a static GEP index). But GEPOp::build and GEPOp::verify are falsely
taking this information as a certain answer, which creates many false
alarms like the one depicted in
`test/Target/LLVMIR/Import/dynamic-gep-index.ll`.
The solution presented here adopts a new verification scheme: When we're
recursively checking the child element types of a struct type, instead
of checking every child types, we only check the one dictated by the
(static) GEP index value. We also combine "refinement" logics --
refine/promote struct index mlir::Value into constants -- into the very
verification process since they have lots of logics in common. The
resulting code is more concise and less brittle.
We also hide GEPOp::findKnownStructIndices since most of the
aforementioned logics are already encapsulated within GEPOp::build and
GEPOp::verify, we found little reason for findKnownStructIndices (or the
new findStructIndices) to be public.
Differential Revision: https://reviews.llvm.org/D124935
The support for this has been added by 946311b893
but then ignored by bc22b5c9a2.
This enables one to write generic code that can be instantiated for both
specific operation classes and the common base class without
specialization. Examples include functions that take/return ops, such
as:
```mlir
template <typename FnTy>
void applyIf(FnTy &&lambda, ...) {
for (Operation *op : ...) {
auto specific = dyn_cast<function_traits<FnTy>::template arg_t<0>>(op);
if (specific)
lambda(specific);
}
}
```
that would otherwise need to rely on template specialization to support
lambdas that take specific operations and those that take `Operation *`.
Differential Revision: https://reviews.llvm.org/D125543
Reviewed by: rriddle
This follows the same general structure of the MLIR and PDLL language
servers. This commits adds the basic functionality for setting up the server,
and initially only supports providing diagnostics. Followon commits will
build out more comprehensive behavior.
Realistically this should eventually live in llvm/, but building in MLIR is an easier
initial step given that:
* All of the necessary LSP functionality is already here
* It allows for proving out useful language features (e.g. compilation databases)
without affecting wider scale tablegen users
* MLIR has a vscode extension that can immediately take advantage of it
Differential Revision: https://reviews.llvm.org/D125440
In the overwhelmingly majority of cases only one dialect is generated at a time
anyways, and this restriction more easily catches user error when multiple
dialects might be generated. We hit this semi-recently with the PDL dialect,
and circt+other downstream users are also actively hitting this as well.
Differential Revision: https://reviews.llvm.org/D125651
Op registration mechanism does not allow for ops with the same name to be
re-registered. This is okay to avoid name conflicts and debug
double-registration, but may be problematic for dialect extensions that may get
registered several times (unlike dialects that are deduplicated in the
registry). When registering ops through the Transform dialect extension
mechanism, check first if the ops are already registered and only complain in
the case of repeated registration with the same name but different TypeID.
Differential Revision: https://reviews.llvm.org/D125554
This patch adds a topological sort utility and pass. A topological sort reorders
the operations in a block without SSA dominance such that, as much as possible,
users of values come after their producers.
The utility function sorts topologically the operation range in a given block
with an optional user-provided callback that can be used to virtually break cycles.
The toposort pass itself recursively sorts graph regions under the target op.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D125063
This is the first implementation of complex (f64 and f32) support
in the sparse compiler, with complex add/mul as first operations.
Note that various features are still TBD, such as other ops, and
reading in complex values from file. Also, note that the
std::complex<float> had a bit of an ABI issue when passed as
single argument. It is still TBD if better solutions are possible.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D125596
This changes replaces the `fully-dynamic-layout-maps` options (which was badly named) with two new options:
* `unknown-type-conversion` controls the layout maps on buffer types for which no layout map can be inferred.
* `function-boundary-type-conversion` controls the layout maps on buffer types inside of function signatures.
Differential Revision: https://reviews.llvm.org/D125615
Instead of recomputing memref types from tensor types, try to infer them when possible. This results in more precise layout maps.
Differential Revision: https://reviews.llvm.org/D125614
Erase gpu.memcpy op when only uses of dest are
the memcpy op in question, its allocation and deallocation
ops.
Reviewed By: bondhugula, csigg
Differential Revision: https://reviews.llvm.org/D124257
There are a lot of cases where we accidentally ignored the result of some
parsing hook. Mark ParseResult as LLVM_NODISCARD just like ParseResult is.
This exposed some stuff to clean up, so do.
Differential Revision: https://reviews.llvm.org/D125549
This pass is to handle computationally complex operations like
tensor.pad which are not simply lowered to the exact same operation in
the memref dialect.
Differential Revision: https://reviews.llvm.org/D125384
This commit refactors the current pass manager support to allow for
operation agnostic pass managers. This allows for a series of passes
to be executed on any viable pass manager root operation, instead
of one specific operation type. Op-agnostic/generic pass managers
only allow for adding op-agnostic passes.
These types of pass managers are extremely useful when constructing
pass pipelines that can apply to many different types of operations,
e.g., the default inliner simplification pipeline. With the advent of
interface/trait passes, this support can be used to define FunctionOpInterface
pass managers, or other pass managers that effectively operate on
specific interfaces/traits/etc (see #52916 for an example).
Differential Revision: https://reviews.llvm.org/D123536
Matthias Springer