This fixes a subtle issue, described in the comment starting with
"Clone the op without the regions and inline the regions from the old op",
which prevented this conversion from working on non-trivial examples.
Differential Revision: https://reviews.llvm.org/D90203
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
When compiling for code size, the use of a vtable causes a destructor(and constructor in certain cases) to be generated for the class. Interface models don't need a complex constructor or a destructor, so this can lead to many megabytes of code size increase(even in opt). This revision switches to a simpler struct of function pointers approach that accomplishes the same API requirements as before. This change requires no updates to user code, or any other code aside from the generator, as the user facing API is still exactly the same.
Differential Revision: https://reviews.llvm.org/D90085
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
This patch adds support for fusing linalg.indexed_generic op with
linalg.tensor_reshape op by expansion, i.e.
- linalg.indexed_generic op -> linalg.tensor_reshape op when the
latter is expanding.
- linalg.tensor_reshape op -> linalg.indexed_generic op when the
former is folding.
Differential Revision: https://reviews.llvm.org/D90082
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.
* Still rough edges that need more sugar but the bones are there. Notes left in the test case for things that can be improved.
* Does not actually yield custom OpViews yet for traversing. Will rework that in a followup.
Differential Revision: https://reviews.llvm.org/D89932
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
The TypeID instance was moved in D89153.
It wasn't caught that it broke MLIR pretty printers because pre-merge checks don't run check-debuginfo.
Avoid disabling all MLIR printers in case this happens again by catching the exception.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D90191
Using an Identifier is much more efficient for attribute lookups because it uses pointer comparison as opposed to string comparison.
Differential Revision: https://reviews.llvm.org/D89660
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
Adds support for
- Dropping unit dimension loops for indexed_generic ops.
- Folding consecutive folding (or expanding) reshapes when the result
(or src) is a scalar.
- Fixes to indexed_generic -> generic fusion when zero-dim tensors are
involved.
Differential Revision: https://reviews.llvm.org/D90118
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
This dependency was already existing indirectly, but is now more direct
since the registration relies on a inline function. This fixes the
link of the tools with BFD.
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
Sean Silva