This patch renames AffineParallelNormalize to AffineLoopNormalize to make it
more generic and be able to hold more loop normalization transformations in
the future for affine.for and affine.parallel ops. Eventually, it could also be
extended to support scf.for and scf.parallel. As a starting point for affine.for,
the patch also adds support for removing single iteration affine.for ops to the
the pass.
Differential Revision: https://reviews.llvm.org/D90267
This revision refactors the base Op/AbstractOperation classes to reduce the amount of generated code size when defining a new operation. The current scheme involves taking the address of functions defined directly on Op and Trait classes. This is problematic because even when these functions are empty/unused we still result in these functions being defined in the main executable. In this revision, we switch to using SFINAE and template type filtering to remove remove functions that are not needed/used. For example, if an operation does not define a custom `print` method we shouldn't define a templated `printAssembly` method for it. The same applies to parsing/folding/verification/etc. This dropped MLIR code size for a large downstream library by ~10%(~1 mb in an opt build).
Differential Revision: https://reviews.llvm.org/D90196
- Add standard dialect operations to define global variables with memref types and to
retrieve the memref for to a named global variable
- Extend unit tests to test verification for these operations.
Differential Revision: https://reviews.llvm.org/D90337
This is the most basic possible finalizing bufferization pass, which I
also think is sufficient for most new use cases. The more concentrated
nature of this pass also greatly clarifies the invariants that it
requires on its input to safely transform the program (see the
pass description in Passes.td).
With this pass, I have now upstreamed practically all of the
bufferizations from npcomp (the exception being std.constant, which can
be upstreamed when std.global_memref lands:
https://llvm.discourse.group/t/rfc-global-variables-in-mlir/2076/16 )
Differential Revision: https://reviews.llvm.org/D90205
Leaking macros isn't a good practice when defining headers. This
requires to duplicate the macro definition in every header though, but
that seems like a better tradeoff right now.
Differential Revision: https://reviews.llvm.org/D90633
This pass allows removing getResultConversionKind from
BufferizeTypeConverter. This pass replaces the AppendToArgumentsList
functionality. As far as I could tell, the only use of this functionlity
is to perform the transformation that is implemented in this pass.
Future patches will remove the getResultConversionKind machinery from
BufferizeTypeConverter, but sending this patch for individual review for
clarity.
Differential Revision: https://reviews.llvm.org/D90071
This commit adds a new library that merges/combines a number of spv
modules into a combined one. The library has a single entry point:
combine(...).
To combine a number of MLIR spv modules, we move all the module-level ops
from all the input modules into one big combined module. To that end, the
combination process can proceed in 2 phases:
(1) resolving conflicts between pairs of ops from different modules
(2) deduplicate equivalent ops/sub-ops in the merged module. (TODO)
This patch implements only the first phase.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D90477
The bufferization patterns are moved to the .cpp file, which is
preferred in the codebase when it makes sense.
The LinalgToStandard patterns are kept a header because they are
expected to be used individually. However, they are moved to
LinalgToStandard.h which is the file corresponding to where they are
defined.
This also removes TensorCastOpConverter, which is handled by
populateStdBufferizePatterns now. Eventually, the constant op lowering
will be handled as well, but it there are currently holdups on moving
it (see https://reviews.llvm.org/D89916).
Differential Revision: https://reviews.llvm.org/D90254
This commit adds a new library that merges/combines a number of spv
modules into a combined one. The library has a single entry point:
combine(...).
To combine a number of MLIR spv modules, we move all the module-level ops
from all the input modules into one big combined module. To that end, the
combination process can proceed in 2 phases:
(1) resolving conflicts between pairs of ops from different modules
(2) deduplicate equivalent ops/sub-ops in the merged module. (TODO)
This patch implements only the first phase.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D90477
This commit adds a new library that merges/combines a number of spv
modules into a combined one. The library has a single entry point:
combine(...).
To combine a number of MLIR spv modules, we move all the module-level ops
from all the input modules into one big combined module. To that end, the
combination process can proceed in 2 phases:
(1) resolving conflicts between pairs of ops from different modules
(2) deduplicate equivalent ops/sub-ops in the merged module. (TODO)
This patch implements only the first phase.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D90022
This op returns a boolean value indicating whether 2 ops are
broadcastable or not. This follows the same logic as the other ops with
broadcast in their names in the shape dialect.
Concretely, shape.is_broadcastable returning true implies that
shape.broadcast will not give an error, and shape.cstr_broadcastable
will not result in an assertion failure. Similarly, false implies an
error or assertion failure.
Previously they were separated into "instance" and "kind" aliases, and also required that the dialect know ahead of time all of the instances that would have a corresponding alias. This approach was very clunky and not ergonomic to interact with. The new approach is to provide the dialect with an instance of an attribute/type to provide an alias for, fully replacing the original split approach.
Differential Revision: https://reviews.llvm.org/D89354
* Removes index based insertion. All insertion now happens through the insertion point.
* Introduces thread local context managers for implicit creation relative to an insertion point.
* Introduces (but does not yet use) binding the Context to the thread local context stack. Intent is to refactor all methods to take context optionally and have them use the default if available.
* Adds C APIs for mlirOperationGetParentOperation(), mlirOperationGetBlock() and mlirBlockGetTerminator().
* Removes an assert in PyOperation creation that was incorrectly constraining. There is already a TODO to rework the keepAlive field that it was guarding and without the assert, it is no worse than the current state.
Differential Revision: https://reviews.llvm.org/D90368
This is a roll-forward of rGec7780ebdab4, now that the remaining
gpu.launch_func have been converted to custom form in rGb22f111023ba.
Reviewed By: antiagainst
Differential Revision: https://reviews.llvm.org/D90420
Linalg "tile-and-fuse" is currently exposed as a Linalg pass "-linalg-fusion" but only the mechanics of the transformation are currently relevant.
Instead turn it into a "-test-linalg-greedy-fusion" pass which performs canonicalizations to enable more fusions to compose.
This allows dropping the OperationFolder which is not meant to be used with the pattern rewrite infrastructure.
Differential Revision: https://reviews.llvm.org/D90394
Update op is modelling the update directive (2.14.4) from the OpenACC specs.
An if condition and a device_type list can be attached to the directive. This patch add
these two information to the current op.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D90310
* Check region count for unknown symbol tables first, as it is a faster check
* Add an accessor to MutableDictionaryAttr to get the internal dictionary without creating a new one if it is empty. This avoids an otherwise unnecessary lookup of an MLIRContext.
Often times the legality of inlining can change depending on if the callable is going to be inlined in-place, or cloned. For example, some operations are not allowed to be duplicated and can only be inlined if the original callable will cease to exist afterwards. The new `wouldBeCloned` flag allows for dialects to hook into this when determining legality.
Differential Revision: https://reviews.llvm.org/D90360
In certain situations it isn't legal to inline a call operation, but this isn't something that is possible(at least not easily) to prevent with the current hooks. This revision adds a new hook so that dialects with call operations that shouldn't be inlined can prevent it.
Differential Revision: https://reviews.llvm.org/D90359
This patch fixes a bug [[ https://bugs.llvm.org/show_bug.cgi?id=46091 | 46091 ]]
Raw data for the `dense-element attribute` is written in little endian (LE) format.
This commit converts the format to big endian (BE) in ʻAttribute Parser` on the
BE machine. Also, when outputting on a BE machine, the BE format is converted
to LE in "AsmPrinter".
Differential Revision: https://reviews.llvm.org/D80695
Getting the body of a Module is a common need which justifies a
dedicated accessor instead of forcing users to go through the
region->blocks->front unwrapping manually.
Differential Revision: https://reviews.llvm.org/D90287
This commit changes to use plain values instead of references.
We need to copy it anyway. References forbid using temporary
values generated from expressions.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D90277
At this point, these methods are just carbon copies of OpBuilder::create and aren't necessary given that PatternRewriter inherits from OpBuilder.
Differential Revision: https://reviews.llvm.org/D90087
An InterfaceMap is generated for every single operation type, and is responsible for a large amount of the code size from MLIR given that its internals highly utilize templates. This revision refactors the internal implementation to use bare malloc/free for interface instances as opposed to static variables and moves as much code out of templates as possible. This led to a decrease of over >1mb (~12% of total MLIR related code size) for a downstream MLIR library with a large amount of operations.
Differential Revision: https://reviews.llvm.org/D90086
All InterfaceMethods will have a corresponding entry in the interface model, and by extension have an implementation generated for every operation type. This can result in large binary size increases when a large amount of operations use an interface, such as the side effect interface.
Differential Revision: https://reviews.llvm.org/D90084
These logically belong together since it's a base commit plus
followup fixes to less common build configurations.
The patches are:
Revert "CfgInterface: rename interface() to getInterface()"
This reverts commit a74fc48158.
Revert "Wrap CfgTraitsFor in namespace llvm to please GCC 5"
This reverts commit f2a06875b6.
Revert "Try to make GCC5 happy about the CfgTraits thing"
This reverts commit 03a5f7ce12.
Revert "Introduce CfgTraits abstraction"
This reverts commit c0cdd22c72.
A recent commit introduced a new syntax for specifying builder arguments in
ODS, which is better amenable to automated processing, and deprecated the old
form. Transition all dialects as well as Linalg ODS generator to use the new
syntax.
Add a deprecation notice to ODS generator.
Reviewed By: rriddle, jpienaar
Differential Revision: https://reviews.llvm.org/D90038
This revisions implements sharding in the storage of parametric instances to decrease lock contention by sharding out the allocator/mutex/etc. to use for a specific storage instance based on the hash key. This is a somewhat common approach to reducing lock contention on data structures, and is used by the concurrent hashmaps provided by folly/java/etc. For several compilations tested, this removed all/most lock contention from profiles and reduced compile time by several seconds.
Differential Revision: https://reviews.llvm.org/D89659
This class represents a rewrite pattern list that has been frozen, and thus immutable. This replaces the uses of OwningRewritePatternList in pattern driver related API, such as dialect conversion. When PDL becomes more prevalent, this API will allow for optimizing a set of patterns once without the need to do this per run of a pass.
Differential Revision: https://reviews.llvm.org/D89104
There are several pieces of pattern rewriting infra in IR/ that really shouldn't be there. This revision moves those pieces to a better location such that they are easier to evolve in the future(e.g. with PDL). More concretely this revision does the following:
* Create a Transforms/GreedyPatternRewriteDriver.h and move the apply*andFold methods there.
The definitions for these methods are already in Transforms/ so it doesn't make sense for the declarations to be in IR.
* Create a new lib/Rewrite library and move PatternApplicator there.
This new library will be focused on applying rewrites, and will also include compiling rewrites with PDL.
Differential Revision: https://reviews.llvm.org/D89103
The Pattern class was originally intended to be used for solely matching operations, but that use never materialized. All of the pattern infrastructure uses RewritePattern, and the infrastructure for pure matching(Matchers.h) is implemented inline. This means that this class isn't a useful abstraction at the moment, so this revision refactors it to solely encapsulate the "metadata" of a pattern. The metadata includes the various state describing a pattern; benefit, root operation, etc. The API on PatternApplicator is updated to now operate on `Pattern`s as nothing special from `RewritePattern` is necessary.
This refactoring is also necessary for the upcoming use of PDL patterns alongside C++ rewrite patterns.
Differential Revision: https://reviews.llvm.org/D86258
The conversion between PDL and the interpreter is split into several different parts.
** The Matcher:
The matching section of all incoming pdl.pattern operations is converted into a predicate tree and merged. Each pattern is first converted into an ordered list of predicates starting from the root operation. A predicate is composed of three distinct parts:
* Position
- A position refers to a specific location on the input DAG, i.e. an
existing MLIR entity being matched. These can be attributes, operands,
operations, results, and types. Each position also defines a relation to
its parent. For example, the operand `[0] -> 1` has a parent operation
position `[0]` (the root).
* Question
- A question refers to a query on a specific positional value. For
example, an operation name question checks the name of an operation
position.
* Answer
- An answer is the expected result of a question. For example, when
matching an operation with the name "foo.op". The question would be an
operation name question, with an expected answer of "foo.op".
After the predicate lists have been created and ordered(based on occurrence of common predicates and other factors), they are formed into a tree of nodes that represent the branching flow of a pattern match. This structure allows for efficient construction and merging of the input patterns. There are currently only 4 simple nodes in the tree:
* ExitNode: Represents the termination of a match
* SuccessNode: Represents a successful match of a specific pattern
* BoolNode/SwitchNode: Branch to a specific child node based on the expected answer to a predicate question.
Once the matcher tree has been generated, this tree is walked to generate the corresponding interpreter operations.
** The Rewriter:
The rewriter portion of a pattern is generated in a very straightforward manor, similarly to lowerings in other dialects. Each PDL operation that may exist within a rewrite has a mapping into the interpreter dialect. The code for the rewriter is generated within a FuncOp, that is invoked by the interpreter on a successful pattern match. Referenced values defined in the matcher become inputs the generated rewriter function.
An example lowering is shown below:
```mlir
// The following high level PDL pattern:
pdl.pattern : benefit(1) {
%resultType = pdl.type
%inputOperand = pdl.input
%root, %results = pdl.operation "foo.op"(%inputOperand) -> %resultType
pdl.rewrite %root {
pdl.replace %root with (%inputOperand)
}
}
// is lowered to the following:
module {
// The matcher function takes the root operation as an input.
func @matcher(%arg0: !pdl.operation) {
pdl_interp.check_operation_name of %arg0 is "foo.op" -> ^bb2, ^bb1
^bb1:
pdl_interp.return
^bb2:
pdl_interp.check_operand_count of %arg0 is 1 -> ^bb3, ^bb1
^bb3:
pdl_interp.check_result_count of %arg0 is 1 -> ^bb4, ^bb1
^bb4:
%0 = pdl_interp.get_operand 0 of %arg0
pdl_interp.is_not_null %0 : !pdl.value -> ^bb5, ^bb1
^bb5:
%1 = pdl_interp.get_result 0 of %arg0
pdl_interp.is_not_null %1 : !pdl.value -> ^bb6, ^bb1
^bb6:
// This operation corresponds to a successful pattern match.
pdl_interp.record_match @rewriters::@rewriter(%0, %arg0 : !pdl.value, !pdl.operation) : benefit(1), loc([%arg0]), root("foo.op") -> ^bb1
}
module @rewriters {
// The inputs to the rewriter from the matcher are passed as arguments.
func @rewriter(%arg0: !pdl.value, %arg1: !pdl.operation) {
pdl_interp.replace %arg1 with(%arg0)
pdl_interp.return
}
}
}
```
Differential Revision: https://reviews.llvm.org/D84580
Substitues `Type` by `Attribute` in the declaration of AttributeInterface. It
looks like the code was written by copy-pasting the definition of TypeInterface,
but the substitution of Type by Attribute was missing at some places.
Reviewed By: rriddle, ftynse
Differential Revision: https://reviews.llvm.org/D90138
The alignment attribute in the 'alloca' op treats the '0' value as 'unset'.
When parsing the custom form of the 'alloca' op, ignore the alignment attribute
with if its value is '0' instead of actually creating it and producing a
slightly different textually yet equivalent semantically form in the output.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D90179
Based on discourse discussion, fix the doc string and remove examples with
wrong semantic. Also fix insert_map semantic by adding missing operand for
vector we are inserting into.
Differential Revision: https://reviews.llvm.org/D89563
This revision allows the fusion of the producer of input tensors in the consumer under a tiling transformation (which produces subtensors).
Many pieces are still missing (e.g. support init_tensors, better refactor LinalgStructuredOp interface support, try to merge implementations and reuse code) but this still allows getting started.
The greedy pass itself is just for testing purposes and will be extracted in a separate test pass.
Differential revision: https://reviews.llvm.org/D89491
This patch introduces a SPIR-V runner. The aim is to run a gpu
kernel on a CPU via GPU -> SPIRV -> LLVM conversions. This is a first
prototype, so more features will be added in due time.
- Overview
The runner follows similar flow as the other runners in-tree. However,
having converted the kernel to SPIR-V, we encode the bind attributes of
global variables that represent kernel arguments. Then SPIR-V module is
converted to LLVM. On the host side, we emulate passing the data to device
by creating in main module globals with the same symbolic name as in kernel
module. These global variables are later linked with ones from the nested
module. We copy data from kernel arguments to globals, call the kernel
function from nested module and then copy the data back.
- Current state
At the moment, the runner is capable of running 2 modules, nested one in
another. The kernel module must contain exactly one kernel function. Also,
the runner supports rank 1 integer memref types as arguments (to be scaled).
- Enhancement of JitRunner and ExecutionEngine
To translate nested modules to LLVM IR, JitRunner and ExecutionEngine were
altered to take an optional (default to `nullptr`) function reference that
is a custom LLVM IR module builder. This allows to customize LLVM IR module
creation from MLIR modules.
Reviewed By: ftynse, mravishankar
Differential Revision: https://reviews.llvm.org/D86108
This patch introduces a pass for running
`mlir-spirv-cpu-runner` - LowerHostCodeToLLVMPass.
This pass emulates `gpu.launch_func` call in LLVM dialect and lowers
the host module code to LLVM. It removes the `gpu.module`, creates a
sequence of global variables that are later linked to the varables
in the kernel module, as well as a series of copies to/from
them to emulate the memory transfer to/from the host or to/from the
device sides. It also converts the remaining Standard dialect into
LLVM dialect, emitting C wrappers.
Reviewed By: mravishankar
Differential Revision: https://reviews.llvm.org/D86112
The current pattern for vector unrolling takes the native shape to
unroll to at pattern instantiation time, but the native shape might
defer based on the types of the operand. Introduce a
UnrollVectorOptions struct which allows for using a function that will
return the native shape based on the operation. Move other options of
unrolling like `filterConstraints` into this struct.
Differential Revision: https://reviews.llvm.org/D89744
Add folder for the case where ExtractStridedSliceOp source comes from a chain
of InsertStridedSliceOp. Also add a folder for the trivial case where the
ExtractStridedSliceOp is a no-op.
Differential Revision: https://reviews.llvm.org/D89850
This patch provides C API for MLIR affine expression.
- Implement C API for methods of AffineExpr class.
- Implement C API for methods of derived classes (AffineBinaryOpExpr, AffineDimExpr, AffineSymbolExpr, and AffineConstantExpr).
Differential Revision: https://reviews.llvm.org/D89856
Added optimization pass to convert heap-based allocs to stack-based allocas in
buffer placement. Added the corresponding test file.
Differential Revision: https://reviews.llvm.org/D89688
This reverts commit 4986d5eaff with
proper patches to CMakeLists.txt:
- Add MLIRAsync as a dependency to MLIRAsyncToLLVM
- Add Coroutines as a dependency to MLIRExecutionEngine
Lower from Async dialect to LLVM by converting async regions attached to `async.execute` operations into LLVM coroutines (https://llvm.org/docs/Coroutines.html):
1. Outline all async regions to functions
2. Add LLVM coro intrinsics to mark coroutine begin/end
3. Use MLIR conversion framework to convert all remaining async types and ops to LLVM + Async runtime function calls
All `async.await` operations inside async regions converted to coroutine suspension points. Await operation outside of a coroutine converted to the blocking wait operations.
Implement simple runtime to support concurrent execution of coroutines.
Reviewed By: herhut
Differential Revision: https://reviews.llvm.org/D89292
* Adds a new MlirOpPrintingFlags type and supporting accessors.
* Adds a new mlirOperationPrintWithFlags function.
* Adds a full featured python Operation.print method with all options and the ability to print directly to files/stdout in text or binary.
* Adds an Operation.get_asm which delegates to print and returns a str or bytes.
* Reworks Operation.__str__ to be based on get_asm.
Differential Revision: https://reviews.llvm.org/D89848
A "structural" type conversion is one where the underlying ops are
completely agnostic to the actual types involved and simply need to update
their types. An example of this is shape.assuming -- the shape.assuming op
and the corresponding shape.assuming_yield op need to update their types
accordingly to the TypeConverter, but otherwise don't care what type
conversions are happening.
Also, the previous conversion code would not correctly materialize
conversions for the shape.assuming_yield op. This should have caused a
verification failure, but shape.assuming's verifier wasn't calling
RegionBranchOpInterface::verifyTypes (which for reasons can't be called
automatically as part of the trait verification, and requires being
called manually). This patch also adds that verification.
Differential Revision: https://reviews.llvm.org/D89833
A "structural" type conversion is one where the underlying ops are
completely agnostic to the actual types involved and simply need to update
their types. An example of this is scf.if -- the scf.if op and the
corresponding scf.yield ops need to update their types accordingly to the
TypeConverter, but otherwise don't care what type conversions are happening.
To test the structural type conversions, it is convenient to define a
bufferize pass for a dialect, which exercises them nicely.
Differential Revision: https://reviews.llvm.org/D89757
The documentation claims that an op with the trait FunctionLike has a
single region containing the blocks that corresponding to the body of
the function. It then goes on to say that the absence of a region
corresponds to an external function when, in fact, this is represented
by a single empty region. This patch changes the wording in the
documentation to match the implementation.
Signed-off-by: Frej Drejhammar <frej.drejhammar@gmail.com>
Co-authored-by: Frej Drejhammar <frej.drejhammar@gmail.com>
Co-authored-by: Klas Segeljakt <klasseg@kth.se>
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D89868
Historically, custom builder specification in OpBuilder has been accepting the
formal parameter list for the builder method as a raw string containing C++.
While this worked well to connect the signature and the body, this became
problematic when ODS needs to manipulate the parameter list, e.g. to inject
OpBuilder or to trim default values when generating the definition. This has
also become inconsistent with other method declarations, in particular in
interface definitions.
Introduce the possibility to define OpBuilder formal parameters using a
TableGen dag similarly to other methods. Additionally, introduce a mechanism to
declare parameters with default values using an additional class. This
mechanism can be reused in other methods. The string-based builder signature
declaration is deprecated and will be removed after a transition period.
Reviewed By: jpienaar
Differential Revision: https://reviews.llvm.org/D89470
Values are ubiquitous in the IR, in particular block argument and operation
results are Values. Define Python classes for BlockArgument, OpResult and their
common ancestor Value. Define pseudo-container classes for lists of block
arguments and operation results, and use these containers to access the
corresponding values in blocks and operations.
Differential Revision: https://reviews.llvm.org/D89778
The CfgTraits abstraction simplfies writing algorithms that are
generic over the type of CFG, and enables writing such algorithms
as regular non-template code that operates on opaque references
to CFG blocks and values.
Implementations of CfgTraits provide operations on the concrete
CFG types, e.g. `IrCfgTraits::BlockRef` is `BasicBlock *`.
CfgInterface is an abstract base class which provides operations
on opaque types CfgBlockRef and CfgValueRef. Those opaque types
encapsulate a `void *`, but the meaning depends on the concrete
CFG type. For example, MachineCfgTraits -- for use with MachineIR
in SSA form -- encodes a Register inside CfgValueRef. Converting
between concrete references and opaque/generic ones is done by
CfgTraits::{fromGeneric,toGeneric}. Convenience methods
CfgTraits::{un}wrap{Iterator,Range} are available as well.
Writing algorithms in terms of CfgInterface adds some overhead
(virtual method calls, plus in same cases it removes the
opportunity to inline iterators), but can be much more convenient
since generic algorithms can be written as non-templates.
This patch adds implementations of CfgTraits for all CFGs on
which dominator trees are calculated, so that the dominator
tree can be ported to this machinery. Only IrCfgTraits (LLVM IR)
and MachineCfgTraits (Machine IR in SSA form) are complete, the
other implementations are limited to the absolute minimum
required to make the upcoming dominator tree changes work.
v5:
- fix MachineCfgTraits::blockdef_iterator and allow it to iterate over
the instructions in a bundle
- use MachineBasicBlock::printName
v6:
- implement predecessors/successors for all CfgTraits implementations
- fix error in unwrapRange
- rename toGeneric/fromGeneric into wrapRef/unwrapRef to have naming
that is consistent with {wrap,unwrap}{Iterator,Range}
- use getVRegDef instead of getUniqueVRegDef
v7:
- std::forward fix in wrapping_iterator
- fix typos
v8:
- cleanup operators on CfgOpaqueType
- address other review comments
Change-Id: Ia75f4f268fded33fca11218a7d578c9aec1f3f4d
Differential Revision: https://reviews.llvm.org/D83088
The Value hierarchy consists of BlockArgument and OpResult, both of which
derive Value. Introduce IsA functions and functions specific to each class,
similarly to other class hierarchies. Also, introduce functions for
pointer-comparison of Block and Operation that are necessary for testing and
are generally useful.
Reviewed By: stellaraccident, mehdi_amini
Differential Revision: https://reviews.llvm.org/D89714
It's unfortunate that this requires adding a dependency on scf dialect
to std bufferization (and hence all of std transforms). This is a bit
perilous. We might want a lib/Transforms/Bufferize/ with a separate
bufferization library per dialect?
Differential Revision: https://reviews.llvm.org/D89667
Move the class to where all base classes are defined.
Also remove all the builders since they are definted in subclasses anyway.
Differential Revision: https://reviews.llvm.org/D89620
The current BufferPlacement transformation contains several concepts for
hoisting allocations. However, more advanced hoisting techniques should not be
integrated into the BufferPlacement transformation. Hence, this CL refactors the
current BufferPlacement pass into three separate pieces: BufferDeallocation and
BufferAllocation(Loop)Hoisting. Moreover, it extends the hoisting functionality
by allowing to move allocations out of loops.
Differential Revision: https://reviews.llvm.org/D87756
LLVM dialect has been defining Op arguments by deriving the `Arguments` ODS
class. This has arguably worse readability due to large indentation caused by
multiple derivations, and is inconsistent with other ODS files. Use the `let
arguments` form instead.
Reviewed By: rriddle
Differential Revision: https://reviews.llvm.org/D89560
Usage of nested parallel regions were not working correctly and leading
to assertion failures. Fix contains the following changes,
1) Don't set the insertion point in the body callback.
2) Save the continuation IP in a stack and set the branch to
continuationIP at the terminator.
Reviewed By: SouraVX, jdoerfert, ftynse
Differential Revision: https://reviews.llvm.org/D88720
AllReduceLowering is currently the only GPU rewrite pattern, but more are coming. This is a preparation change.
Reviewed By: herhut
Differential Revision: https://reviews.llvm.org/D89370
This transforms the symbol lookups to O(1) from O(NM), greatly speeding up both passes. For a large MLIR module this shaved seconds off of the compilation time.
Differential Revision: https://reviews.llvm.org/D89522
The initial goal of this interface is to fix the current problems with verifying symbol user operations, but can extend beyond that in the future. The current problems with the verification of symbol uses are:
* Extremely inefficient:
Most current symbol users perform the symbol lookup using the slow O(N) string compare methods, which can lead to extremely long verification times in large modules.
* Invalid/break the constraints of verification pass
If the symbol reference is not-flat(and even if it is flat in some cases) a verifier for an operation is not permitted to touch the referenced operation because it may be in the process of being mutated by a different thread within the pass manager.
The new SymbolUserOpInterface exposes a method `verifySymbolUses` that will be invoked from the parent symbol table to allow for verifying the constraints of any referenced symbols. This method is passed a `SymbolTableCollection` to allow for O(1) lookups of any necessary symbol operation.
Differential Revision: https://reviews.llvm.org/D89512
This revision contains two optimizations related to symbol checking:
* Optimize SymbolOpInterface to only check for a name attribute if the operation is an optional symbol.
This removes an otherwise unnecessary attribute lookup from a majority of symbols.
* Add a new SymbolTableCollection class to represent a collection of SymbolTables.
This allows for perfoming non-flat symbol lookups in O(1) time by caching SymbolTables for symbol table operations. This class is very useful for algorithms that operate on multiple symbol tables, either recursively or not.
Differential Revision: https://reviews.llvm.org/D89505
(Note: This is a reland of D82597)
This class allows for defining thread local objects that have a set non-static lifetime. This internals of the cache use a static thread_local map between the various different non-static objects and the desired value type. When a non-static object destructs, it simply nulls out the entry in the static map. This will leave an entry in the map, but erase any of the data for the associated value. The current use cases for this are in the MLIRContext, meaning that the number of items in the static map is ~1-2 which aren't particularly costly enough to warrant the complexity of pruning. If a use case arises that requires pruning of the map, the functionality can be added.
This is especially useful in the context of MLIR for implementing thread-local caching of context level objects that would otherwise have very high lock contention. This revision adds a thread local cache in the MLIRContext for attributes, identifiers, and types to reduce some of the locking burden. This led to a speedup of several seconds when compiling a somewhat large mlir module.
Differential Revision: https://reviews.llvm.org/D89504
This trait simply adds a fold of f(f(x)) = f(x) when an operation is labelled as idempotent
Reviewed By: rriddle, andyly
Differential Revision: https://reviews.llvm.org/D89421
* Also fixes the const-ness of the various DenseElementsAttr construction functions.
* Both issues identified when trying to use the DenseElementsAttr functions.
Differential Revision: https://reviews.llvm.org/D89517
Added an underlying matcher for generic constant ops. This
included a rewriter of RewriterGen to make variable use more
clear.
Differential Revision: https://reviews.llvm.org/D89161
Adding unroll support for transfer read and transfer write operation. This
allows to pick the ideal size for the memory access for a given target.
Differential Revision: https://reviews.llvm.org/D89289
The opposite of tensor_to_memref is tensor_load.
- Add some basic tensor_load/tensor_to_memref folding.
- Add source/target materializations to BufferizeTypeConverter.
- Add an example std bufferization pattern/pass that shows how the
materialiations work together (more std bufferization patterns to come
in subsequent commits).
- In coming commits, I'll document how to write composable
bufferization passes/patterns and update the other in-tree
bufferization passes to match this convention. The populate* functions
will of course continue to be exposed for power users.
The naming on tensor_load/tensor_to_memref and their pretty forms are
not very intuitive. I'm open to any suggestions here. One key
observation is that the memref type must always be the one specified in
the pretty form, since the tensor type can be inferred from the memref
type but not vice-versa.
With this, I've been able to replace all my custom bufferization type
converters in npcomp with BufferizeTypeConverter!
Part of the plan discussed in:
https://llvm.discourse.group/t/what-is-the-strategy-for-tensor-memref-conversion-bufferization/1938/17
Differential Revision: https://reviews.llvm.org/D89437
Alexander Belyaev