| Commit message (Collapse) | Author | Age | Files | Lines |
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- turn canonicalizeMapAndOperands into a template that works on both
sets and maps, and use it to introduce a utility to canonicalize an
affine integer set and its operands
- add pattern to canonicalize affine if op's.
- rename IntegerSet::getNumOperands -> IntegerSet::getNumInputs to be
consistent with AffineMap
- add missing accessors for IntegerSet
Doesn't need extensive testing since canonicalizeSetAndOperands just
reuses canonicalizeMapAndOperands' logic, and the latter is tested on
affine.apply map + operands; the new method works the same way on an
integer set + operands of an affine if op for example.
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closes tensorflow/mlir#112
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/112 from bondhugula:set-canonicalize eff72f23250b96fa7d9f5caff3877440f5de2cec
PiperOrigin-RevId: 267532876
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This commit defines an initial implementation of the DialectInlinerInterface for the AffineOps dialect. This change allows for affine operations to be inlined into any region that is not an affine region. Inlining into affine regions requires special handling for dimension/symbol identifiers that will be added in followups.
PiperOrigin-RevId: 267467078
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SPIR-V can explicitly declare structured control-flow constructs using merge
instructions. These explicitly declare a header block before the control
flow diverges and a merge block where control flow subsequently converges.
These blocks delimit constructs that must nest, and can only be entered
and exited in structured ways.
Instead of having a `spv.LoopMerge` op to directly model loop merge
instruction for indicating the merge and continue target, we use regions
to delimit the boundary of the loop: the merge target is the next op
following the `spv.loop` op and the continue target is the block that
has a back-edge pointing to the entry block inside the `spv.loop`'s region.
This way it's easier to discover all blocks belonging to a construct and
it plays nicer with the MLIR system.
Updated the SPIR-V.md doc.
PiperOrigin-RevId: 267431010
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This defines a set of initial utilities for inlining a region(or a FuncOp), and defines a simple inliner pass for testing purposes.
A new dialect interface is defined, DialectInlinerInterface, that allows for dialects to override hooks controlling inlining legality. The interface currently provides the following hooks, but these are just premilinary and should be changed/added to/modified as necessary:
* isLegalToInline
- Determine if a region can be inlined into one of this dialect, *or* if an operation of this dialect can be inlined into a given region.
* shouldAnalyzeRecursively
- Determine if an operation with regions should be analyzed recursively for legality. This allows for child operations to be closed off from the legality checks for operations like lambdas.
* handleTerminator
- Process a terminator that has been inlined.
This cl adds support for inlining StandardOps, but other dialects will be added in followups as necessary.
PiperOrigin-RevId: 267426759
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It is generally beneficial to pass less arguments to a kernel, so cloning constants
into the kernel is beneficial.
PiperOrigin-RevId: 267139084
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This CL adds support for proper cloning of Linalg ops that have regions (i.e. the generic linalg op). This is used to properly implement tiling and fusion for such ops. Adequate tests are added.
PiperOrigin-RevId: 267027176
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This will allow us to use MLIR's folding infrastructure to deduplicate
SPIR-V constants.
This CL also changed isValidSPIRVType in SPIRVDialect to a static method.
PiperOrigin-RevId: 266984403
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Remove unused variables and attributes from BaseViewConversionHelper
on mlir/lib/Dialect/Linalg/Transforms/LowerToLLVMDialect.cpp
Closes tensorflow/mlir#116
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/116 from alexst07:fix-warnings 5f638e4677492cf71a9cc040eeb6b57427d32e06
PiperOrigin-RevId: 266972082
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Some of the operations in the LLVM dialect are required to model the LLVM IR in
MLIR, for example "constant" operations are needed to declare a constant value
since MLIR, unlike LLVM, does not support immediate values as operands. To
avoid confusion with actual LLVM operations, we prefix such axuiliary
operations with "mlir.".
PiperOrigin-RevId: 266942838
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The SelectOp models the semantics of OpSelect from SPIR-V spec.
PiperOrigin-RevId: 266849559
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This interface will allow for providing hooks to interrop with operation folding. The first hook, 'shouldMaterializeInto', will allow for controlling which region to insert materialized constants into. The folder will generally materialize constants into the top-level isolated region, this allows for materializing into a lower level ancestor region if it is more profitable/correct.
PiperOrigin-RevId: 266702972
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AffineForOp themselves are pure and can be removed if there are no internal operations.
PiperOrigin-RevId: 266481293
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This CL just covers the op definition, its parsing, printing,
and verification. (De)serialization is to be implemented
in a subsequent CL.
PiperOrigin-RevId: 266431077
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This change refactors and cleans up the implementation of the operation walk methods. After this refactoring is that the explicit template parameter for the operation type is no longer needed for the explicit op walks. For example:
op->walk<AffineForOp>([](AffineForOp op) { ... });
is now accomplished via:
op->walk([](AffineForOp op) { ... });
PiperOrigin-RevId: 266209552
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We should consider both signed and narrow_range cases.
PiperOrigin-RevId: 266167366
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- extend canonicalizeMapAndOperands to propagate constant operands into
the map's expressions (and thus drop those operands).
- canonicalizeMapAndOperands previously only dropped duplicate and
unused operands; however, operands that were constants were
retained.
This change makes IR maps/expressions generated by various
utilities/passes even simpler; also makes some of the test checks more
accurate and simpler -- for eg., 0' instead of symbol(%{{.*}}).
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closes tensorflow/mlir#107
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/107 from bondhugula:canonicalize-maps c889a51486d14fbf7db489f224f881e7e1ff7d72
PiperOrigin-RevId: 266085289
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PiperOrigin-RevId: 266073204
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PiperOrigin-RevId: 266022088
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This change adds definitions, parsing and verification for both ops.
PiperOrigin-RevId: 265954051
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block-wide reduce.
PiperOrigin-RevId: 265720077
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Each basic block in SPIR-V must start with an OpLabel instruction.
We don't support control flow yet, so this CL just makes sure that
the entry block follows this rule and is valid.
PiperOrigin-RevId: 265718841
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To support a conversion of a simple load-compute-store kernel from GPU
dialect to SPIR-V dialect, the conversion of operations like
"gpu.block_dim", "gpu.thread_id" which allow threads to get the launch
conversion is needed. In SPIR-V these are specified as global
variables with builin attributes. This CL adds support to specify
builtin variables in SPIR-V conversion framework. This is used to
convert the relevant operations from GPU dialect to SPIR-V dialect.
Also add support for conversion of load/store operation in Standard
dialect to SPIR-V dialect.
To simplify the conversion add a method to build a spv.AccessChain
operation that automatically determines the return type based on the
base pointer type and the indices provided.
PiperOrigin-RevId: 265718525
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Add Block decoration for top-level spv.struct.
Closes tensorflow/mlir#102
PiperOrigin-RevId: 265716241
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Add an extra RewritePattern that does not convert types to rewrite a CopyOp that has non-identity permutations into a sequence of TransposeOp followed by a CopyOp without such permutations.
This RewitePattern is made to fail in the non-permutation case so that the conversion pattern can kick in to lower to LLVM.
This is an instance of A->A->B lowering where A->A is done by a RewritePattern in case_1 and A->B is done by a ConversionPatternRewriter when not(case_1).
PiperOrigin-RevId: 265171380
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Add a conversion pattern that transforms a linalg.transpose op into:
1. A function entry `alloca` operation to allocate a ViewDescriptor.
2. A load of the ViewDescriptor from the pointer allocated in 1.
3. Updates to the ViewDescriptor to introduce the data ptr, offset, size
and stride. Size and stride are permutations of the original values.
4. A store of the resulting ViewDescriptor to the alloca'ed pointer.
The linalg.transpose op is replaced by the alloca'ed pointer.
PiperOrigin-RevId: 265169112
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A linalg.transpose op is a pure metadata operation that takes a view + permutation map and produces
another view of the same underlying data, with a different reindexing. This is a
pure metadata operation that does not touch the underlying data.
Example:
```
%t = linalg.transpose %v (i, j) -> (j, i) : !linalg.view<?x?xf32>
```
PiperOrigin-RevId: 265139429
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This CL extends support for lowering of linalg to external C++ libraries with CopyOp. Currently this can only work when the permutation maps in the copies are identity. Future support for permutations will be added later.
PiperOrigin-RevId: 265093025
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This CL is also purely moving code around for better file organization.
PiperOrigin-RevId: 265092566
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Only a few important KHR extensions are registered to the
SPIR-V dialect for now.
PiperOrigin-RevId: 264939428
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linalg.subview used to lower to a slice with a bounded range resulting in correct bounded accesses. However linalg.slice could still index out of bounds. This CL moves the bounding to linalg.slice.
LLVM select and cmp ops gain a more idiomatic builder.
PiperOrigin-RevId: 264897125
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This CL pulls in capabilities defined in the spec and adds
support for (de)serialize capabilities of a spv.module.
PiperOrigin-RevId: 264877413
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PiperOrigin-RevId: 264827908
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Operation interfaces generally require a bit of boilerplate code to connect all of the pieces together. This cl introduces mechanisms in the ODS to allow for generating operation interfaces via the 'OpInterface' class.
Providing a definition of the `OpInterface` class will auto-generate the c++
classes for the interface. An `OpInterface` includes a name, for the c++ class,
along with a list of interface methods. There are two types of methods that can be used with an interface, `InterfaceMethod` and `StaticInterfaceMethod`. They are both comprised of the same core components, with the distinction that `StaticInterfaceMethod` models a static method on the derived operation.
An `InterfaceMethod` is comprised of the following components:
* ReturnType
- A string corresponding to the c++ return type of the method.
* MethodName
- A string corresponding to the desired name of the method.
* Arguments
- A dag of strings that correspond to a c++ type and variable name
respectively.
* MethodBody (Optional)
- An optional explicit implementation of the interface method.
def MyInterface : OpInterface<"MyInterface"> {
let methods = [
// A simple non-static method with no inputs.
InterfaceMethod<"unsigned", "foo">,
// A new non-static method accepting an input argument.
InterfaceMethod<"Value *", "bar", (ins "unsigned":$i)>,
// Query a static property of the derived operation.
StaticInterfaceMethod<"unsigned", "fooStatic">,
// Provide the definition of a static interface method.
// Note: `ConcreteOp` corresponds to the derived operation typename.
StaticInterfaceMethod<"Operation *", "create",
(ins "OpBuilder &":$builder, "Location":$loc), [{
return builder.create<ConcreteOp>(loc);
}]>,
// Provide a definition of the non-static method.
// Note: `op` corresponds to the derived operation variable.
InterfaceMethod<"unsigned", "getNumInputsAndOutputs", (ins), [{
return op.getNumInputs() + op.getNumOutputs();
}]>,
];
PiperOrigin-RevId: 264754898
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PiperOrigin-RevId: 264742130
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LowerToLLVMDialect.cpp - NFC
PiperOrigin-RevId: 264740014
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Previously Module and Function are builtinn constructs in MLIR.
Due to the structural requirements we must wrap the SPIR-V
module inside a Function inside a Module. Now the requirement
is lifted and we can remove the wrapping function! :)
PiperOrigin-RevId: 264736051
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for the operation result.
This generalizes the current special handling for constant operations(they get named 'cst'/'true'/'false'/etc.)
PiperOrigin-RevId: 264723379
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PiperOrigin-RevId: 264612014
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In SPIR-V binary format, constants are placed at the module level
and referenced by instructions inside functions using their result
<id>s. To model this natively (using SSA values for result <id>s),
it means we need to have implicit capturing functions. We will
lose the ability to have function passes if going down that path.
Instead, this CL changes to materialize constants at their use
sites in deserialization. It's cheap to copy constants in MLIR
given that attributes is uniqued to MLIRContext. By localizing
constants into functions, we can preserve isolated functions.
PiperOrigin-RevId: 264582532
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PiperOrigin-RevId: 264482571
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Similar to global variables, specialization constants also live
in the module scope and can be referenced by instructions in
functions in native SPIR-V. A direct modelling would be to allow
functions in the SPIR-V dialect to implicit capture, but it means
we are losing the ability to write passes for Functions. While
in SPIR-V normally we want to process the module as a whole,
it's not common to see multiple functions get used so we'd like
to leave the door open for those cases. Therefore, similar to
global variables, we introduce spv.specConstant to model three
SPIR-V instructions: OpSpecConstantTrue, OpSpecConstantFalse,
and OpSpecConstant. They do not return SSA value results;
instead they have symbols and can only be referenced by the
symbols. To use it in a function, we need to have another op
spv._reference_of to turn the symbol into an SSA value. This
breaks the tie and makes functions still explicit capture.
Previously specialization constants were handled similarly as
normal constants. That is incorrect given that specialization
constant actually acts more like variable (without need to
load and store). E.g., they cannot be de-duplicated like normal
constants.
This CL also refines various documents and comments.
PiperOrigin-RevId: 264455172
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Support (de)serialization of spv.struct with offset decorations.
Closes tensorflow/mlir#94
PiperOrigin-RevId: 264421427
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This conversion has been using a stack-allocated array of i8 to store the
null-terminated kernel name in order to pass it to the CUDA wrappers expecting
a C string because the LLVM dialect was missing support for globals. Now that
the suport is introduced, use a global instead.
Refactor global string construction from GenerateCubinAccessors into a common
utility function living in the LLVM namespace.
PiperOrigin-RevId: 264382489
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This CL allows binary operations on n-D vector types to be lowered to LLVMIR by performing an (n-1)-D extractvalue, 1-D vector operation and an (n-1)-D insertvalue.
PiperOrigin-RevId: 264339118
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PiperOrigin-RevId: 264277760
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Change the prining/parsing of spv.globalVariable to print the type of
the variable after the ':' to be consistent with MLIR convention.
The spv._address_of should print the variable type after the ':'. It was
mistakenly printing the address of the return value. Add a (missing)
test that should have caught that.
Also move spv.globalVariable and spv._address_of tests to
structure-ops.mlir.
PiperOrigin-RevId: 264204686
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PiperOrigin-RevId: 264193915
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This CL adds the spv.ReturnValue op and its tests. Also adds a
InFunctionScope trait to make sure that the op stays inside
a function. To be consistent, ModuleOnly trait is changed to
InModuleScope.
PiperOrigin-RevId: 264193081
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Switch to C++14 standard method as llvm::make_unique has been removed (
https://reviews.llvm.org/D66259). Also mark some targets as c++14 to ease next
integrates.
PiperOrigin-RevId: 263953918
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FuncOps in MLIR use explicit capture. So global variables defined in
module scope need to have a symbol name and this should be used to
refer to the variable within the function. This deviates from SPIR-V
spec, which assigns an SSA value to variables at all scopes that can
be used to refer to the variable, which requires SPIR-V functions to
allow implicit capture. To handle this add a new op,
spirv::GlobalVariableOp that can be used to define module scope
variables.
Since instructions need an SSA value, an new spirv::AddressOfOp is
added to convert a symbol reference to an SSA value for use with other
instructions.
This also means the spirv::EntryPointOp instruction needs to change to
allow initializers to be specified using symbol reference instead of
SSA value
The current spirv::VariableOp which returns an SSA value (as defined
by SPIR-V spec) can still be used to define function-scope variables.
PiperOrigin-RevId: 263951109
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