Thus far DRR always invokes the separate-parameter builder (i.e., requiring
a separate parameter for each result-type/operand/attribute) for creating
ops, no matter whether we can auto-generate a builder with type-deduction
ability or not.
This CL changes the path for ops that we can auto-generate type-deduction
builders, i.e., with SameOperandsAndResultType/FirstAttrDerivedResultType
traits. Now they are going through a aggregate-parameter builder (i.e.,
requiring one parameter for all result-types/operands/attributes).
attributes.)
It is expected this approach will be more friendly for future shape inference
function autogen and calling those autogen'd shape inference function without
excessive packing and repacking operand/attribute lists.
Also, it would enable better support for creating ops with optional attributes
because we are not required to provide an Attribute() as placeholder for
an optional attribute anymore.
PiperOrigin-RevId: 280654800
Refactoring the conversion from StandardOps/GPU dialect to SPIR-V
dialect:
1) Move the SPIRVTypeConversion and SPIRVOpLowering class into SPIR-V
dialect.
2) Add header files that expose functions to add patterns for the
dialects to SPIR-V lowering, as well as a pass that does the
dialect to SPIR-V lowering.
3) Make SPIRVOpLowering derive from OpLowering class.
PiperOrigin-RevId: 280486871
Since VariableOp is serialized during processBlock, we add two more fields,
`functionHeader` and `functionBody`, to collect instructions for a function.
After all the blocks have been processed, we append them to the `functions`.
Also, fix a bug in processGlobalVariableOp. The global variables should be
encoded into `typesGlobalValues`.
PiperOrigin-RevId: 280105366
This CL adds an extra pointer to the memref descriptor to allow specifying alignment.
In a previous implementation, we used 2 types: `linalg.buffer` and `view` where the buffer type was the unit of allocation/deallocation/alignment and `view` was the unit of indexing.
After multiple discussions it was decided to use a single type, which conflates both, so the memref descriptor now needs to carry both pointers.
This is consistent with the [RFC-Proposed Changes to MemRef and Tensor MLIR Types](https://groups.google.com/a/tensorflow.org/forum/#!searchin/mlir/std.view%7Csort:date/mlir/-wKHANzDNTg/4K6nUAp8AAAJ).
PiperOrigin-RevId: 279959463
MLIR translation tools can emit diagnostics and we want to be able to check if
it is indeed the case in tests. Reuse the source manager error handlers
provided for mlir-opt to support the verification in mlir-translate. This
requires us to change the signature of the functions that are registered to
translate sources to MLIR: it now takes a source manager instead of a memory
buffer.
PiperOrigin-RevId: 279132972
This makes the generated doc easier to read and it is also
more friendly to certain markdown parsers like kramdown.
Fixestensorflow/mlir#221
PiperOrigin-RevId: 278643469
BitEnumAttr is a mechanism for modelling attributes whose value is
a bitfield. It should not be scoped to the SPIR-V dialect and can
be used by other dialects too.
This CL is mostly shuffling code around and adding tests and docs.
Functionality changes are:
* Fixed to use `getZExtValue()` instead of `getSExtValue()` when
getting the value from the underlying IntegerAttr for a case.
* Changed to auto-detect whether there is a case whose value is
all bits unset (i.e., zero). If so handle it specially in all
helper methods.
PiperOrigin-RevId: 277964926
Previously DRR assumes attributes to appear after operands. This was the
previous requirements on ODS, but that has changed some time ago. Fix
DRR to also support interleaved operands and attributes.
PiperOrigin-RevId: 275983485
Otherwise, we'll see the following warning when compiling with GCC 8:
warning: this ?for? clause does not guard... [-Wmisleading-indentation]
PiperOrigin-RevId: 275735925
NativeCodeCall is handled differently than normal op creation in RewriterGen
(because its flexibility). It will only be materialized to output stream if
it is used. But when using it for auxiliary patterns, we still want the side
effect even if it is not replacing matched root op's results.
PiperOrigin-RevId: 275265467
It's usually hard to understand what went wrong if mlir-tblgen
crashes on some input. This CL adds a few useful LLVM_DEBUG
statements so that we can use mlir-tblegn -debug to figure
out the culprit for a crash.
PiperOrigin-RevId: 275253532
Add a pass to decorate the composite types used by
composite objects in the StorageBuffer, PhysicalStorageBuffer,
Uniform, and PushConstant storage classes with layout information.
Closestensorflow/mlir#156
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/156 from denis0x0D:sandbox/layout_info_decoration 7c50840fd38ca169a2da7ce9886b52b50c868b84
PiperOrigin-RevId: 273634140
MLIR uses symbol references to model references to many global entities, such as functions/variables/etc. Before this change, there is no way to actually reason about the uses of such entities. This change provides a walker for symbol references(via SymbolTable::walkSymbolUses), as well as 'use_empty' support(via SymbolTable::symbol_use_empty). It also resolves some deficiencies in the LangRef definition of SymbolRefAttr, namely the restrictions on where a SymbolRefAttr can be stored, ArrayAttr and DictionaryAttr, and the relationship with operations containing the SymbolTable trait.
PiperOrigin-RevId: 273549331
Originally, we were attaching attributes containing CUBIN blobs to the kernel
function called by `gpu.launch_func`. This kernel is now contained in a nested
module that is used as a compilation unit. Attach compiled CUBIN blobs to the
module rather than to the function since we were compiling the module. This
also avoids duplication of the attribute on multiple kernels within the same
module.
PiperOrigin-RevId: 273497303
Now that the accessor function is a trivial getter of the global variable, it
makes less sense to have the getter generation as a separate pass. Move the
getter generation into the lowering of `gpu.launch_func` to CUDA calls. This
change is mostly code motion, but the process can be simplified further by
generating the addressof inplace instead of using a call. This is will be done
in a follow-up.
PiperOrigin-RevId: 273492517
Add new `typeDescription` (description was already used by base constraint class) field to type to allow writing longer descriptions about a type being defined. This allows for providing additional information/rationale for a defined type. This currently uses `description` as the heading/name for the type in the generated documentation.
PiperOrigin-RevId: 273299332
This makes the name of the conversion pass more consistent with the naming
scheme, since it actually converts from the Loop dialect to the Standard
dialect rather than working with arbitrary control flow operations.
PiperOrigin-RevId: 272612112
This is a follow-up to the PRtensorflow/mlir#146 which introduced the ROCDL Dialect. This PR introduces a pass to lower GPU Dialect to the ROCDL Dialect. As with the previous PR, this one builds on the work done by @whchung, and addresses most of the review comments in the original PR.
Closestensorflow/mlir#154
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/154 from deven-amd:deven-lower-gpu-to-rocdl 809893e08236da5ab6a38e3459692fa04247773d
PiperOrigin-RevId: 272390729
Add DeclareOpInterfaceFunctions to enable specifying whether OpInterfaceMethods
for an OpInterface should be generated automatically. This avoids needing to
declare the extra methods, while also allowing adding function declaration by way of trait/inheritance.
Most of this change is mechanical/extracting classes to be reusable.
PiperOrigin-RevId: 272042739
This CL finishes the implementation of the lowering part of the [strided memref RFC](https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
Strided memrefs correspond conceptually to the following templated C++ struct:
```
template <typename Elem, size_t Rank>
struct {
Elem *ptr;
int64_t offset;
int64_t sizes[Rank];
int64_t strides[Rank];
};
```
The linearization procedure for address calculation for strided memrefs is the same as for linalg views:
`base_offset + SUM_i index_i * stride_i`.
The following CL will unify Linalg and Standard by removing !linalg.view in favor of strided memrefs.
PiperOrigin-RevId: 272033399
The strided MemRef RFC discusses a normalized descriptor and interaction with library calls (https://groups.google.com/a/tensorflow.org/forum/#!topic/mlir/MaL8m2nXuio).
Lowering of nested LLVM structs as value types does not play nicely with externally compiled C/C++ functions due to ABI issues.
Solving the ABI problem generally is a very complex problem and most likely involves taking
a dependence on clang that we do not want atm.
A simple workaround is to pass pointers to memref descriptors at function boundaries, which this CL implement.
PiperOrigin-RevId: 271591708
This commit introduces the ROCDL Dialect (i.e. the ROCDL ops + the code to lower those ROCDL ops to LLWM intrinsics/functions). Think of ROCDL Dialect as analogous to the NVVM Dialect, but for AMD GPUs. This patch contains just the essentials needed to get a simple example up and running. We expect to make further additions to the ROCDL Dialect.
This is the first of 3 commits, the follow-up will be:
* add a pass that lowers GPU Dialect to ROCDL Dialect
* add a "mlir-rocm-runner" utility
Closestensorflow/mlir#146
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/146 from deven-amd:deven-rocdl-dialect e78e8005c75a78912631116c78dc844fcc4b0de9
PiperOrigin-RevId: 271511259
The support for functions taking and returning memrefs of floats was introduced
in the first version of the runner, created before MLIR had reliable lowering
of allocation/deallocation to library calls. It forcibly runs MLIR
transformation convering affine, loop and standard dialects into the LLVM
dialect, unlike the other runner flows that accept the LLVM dialect directly.
Memref support leads to more complex layering and is generally fragile. Drop
it in favor of functions returning a scalar, or library-based function calls to
print memrefs and other data structures.
PiperOrigin-RevId: 271330839
1) Process and ignore the following debug instructions: OpSource,
OpSourceContinued, OpSourceExtension, OpString, OpModuleProcessed.
2) While processing OpTypeInt instruction, ignore the signedness
specification. Currently MLIR doesnt make a distinction between signed
and unsigned integer types.
3) Process and ignore BufferBlock decoration (similar to Buffer
decoration). StructType needs to be enhanced to track this attribute
since its needed for proper validation checks.
4) Report better error for unhandled instruction during
deserialization.
PiperOrigin-RevId: 271057060
This change adds support for documenting interfaces and their methods. A tablegen generator for the interface documentation is also added(gen-op-interface-doc).
Documentation is added to an OpInterface via the `description` field:
def MyOpInterface : OpInterface<"MyOpInterface"> {
let description = [{
My interface is very interesting.
}];
}
Documentation is added to an InterfaceMethod via a new `description` field that comes right before the optional body:
InterfaceMethod<"void", "foo", (ins), [{
This is the foo method.
}]>,
PiperOrigin-RevId: 270965485
Similar to mlir-opt, having a -split-input-file mode is quite useful
in mlir-translate. It allows to put logically related tests in the
same test file for better organization.
PiperOrigin-RevId: 270805467
Roll forward of commit 5684a12.
When outlining GPU kernels, put the kernel function inside a nested module. Then use a nested pipeline to generate the cubins, independently per kernel. In a final pass, move the cubins back to the parent module.
PiperOrigin-RevId: 270639748
When outlining GPU kernels, put the kernel function inside a nested module. Then use a nested pipeline to generate the cubins, independently per kernel. In a final pass, move the cubins back to the parent module.
PiperOrigin-RevId: 269987720
This CL changes translation functions to take MemoryBuffer
as input and raw_ostream as output. It is generally better to
avoid handling files directly in a library (unless the library
is specifically for file manipulation) and we can unify all
file handling to the mlir-translate binary itself.
PiperOrigin-RevId: 269625911
A generic mechanism for (de)serialization of extended instruction sets
is added with this CL. To facilitate this, a new class
"SPV_ExtendedInstSetOp" is added which is a base class for all
operations corresponding to extended instruction sets. The methods to
(de)serialization such ops as well as its dispatch is generated
automatically.
The behavior controlled by autogenSerialization and hasOpcode is also
slightly modified to enable this. They are now decoupled.
1) Setting hasOpcode=1 means the operation has a corresponding
opcode in SPIR-V binary format, and its dispatch for
(de)serialization is automatically generated.
2) Setting autogenSerialization=1 generates the function for
(de)serialization automatically.
So now it is possible to have hasOpcode=0 and autogenSerialization=1
(for example SPV_ExtendedInstSetOp).
Since the dispatch functions is also auto-generated, the input file
needs to contain all operations. To this effect, SPIRVGLSLOps.td is
included into SPIRVOps.td. This makes the previously added
SPIRVGLSLOps.h and SPIRVGLSLOps.cpp unnecessary, and are deleted.
The SPIRVUtilsGen.cpp is also changed to make better use of
formatv,making the code more readable.
PiperOrigin-RevId: 269456263
Certain enum classes in SPIR-V, like function/loop control and memory
access, are bitmasks. This CL introduces a BitEnumAttr to properly
model this and drive auto-generation of verification code and utility
functions. We still store the attribute using an 32-bit IntegerAttr
for minimal memory footprint and easy (de)serialization. But utility
conversion functions are adjusted to inspect each bit and generate
"|"-concatenated strings for the bits; vice versa.
Each such enum class has a "None" case that means no bit is set. We
need special handling for "None". Because of this, the logic is not
general anymore. So right now the definition is placed in the SPIR-V
dialect. If later this turns out to be useful for other dialects,
then we can see how to properly adjust it and move to OpBase.td.
Added tests for SPV_MemoryAccess to check and demonstrate.
PiperOrigin-RevId: 269350620
This allows for explicitly specifying the pipeline to add to the pass manager. This includes the nesting structure, as well as the passes/pipelines to run. A textual pipeline string is defined as a series of names, each of which may in itself recursively contain a nested pipeline description. A name is either the name of a registered pass, or pass pipeline, (e.g. "cse") or the name of an operation type (e.g. "func").
For example, the following pipeline:
$ mlir-opt foo.mlir -cse -canonicalize -lower-to-llvm
Could now be specified as:
$ mlir-opt foo.mlir -pass-pipeline='func(cse, canonicalize), lower-to-llvm'
This will allow for running pipelines on nested operations, like say spirv modules. This does not remove any of the current functionality, and in fact can be used in unison. The new option is available via 'pass-pipeline'.
PiperOrigin-RevId: 268954279
This is done via a new set of instrumentation hooks runBeforePipeline/runAfterPipeline, that signal the lifetime of a pass pipeline on a specific operation type. These hooks also provide the parent thread of the pipeline, allowing for accurate merging of timers running on different threads.
PiperOrigin-RevId: 267909193
This change generalizes the structure of the pass manager to allow arbitrary nesting pass managers for other operations, at any level. The only user visible change to existing code is the fact that a PassManager must now provide an MLIRContext on construction. A new class `OpPassManager` has been added that represents a pass manager on a specific operation type. `PassManager` will remain the top-level entry point into the pipeline, with OpPassManagers being nested underneath. OpPassManagers will still be implicitly nested if the operation type on the pass differs from the pass manager. To explicitly build a pipeline, the 'nest' methods on OpPassManager may be used:
// Pass manager for the top-level module.
PassManager pm(ctx);
// Nest a pipeline operating on FuncOp.
OpPassManager &fpm = pm.nest<FuncOp>();
fpm.addPass(...);
// Nest a pipeline under the FuncOp pipeline that operates on spirv::ModuleOp
OpPassManager &spvModulePM = pm.nest<spirv::ModuleOp>();
// Nest a pipeline on FuncOps inside of the spirv::ModuleOp.
OpPassManager &spvFuncPM = spvModulePM.nest<FuncOp>();
To help accomplish this a new general OperationPass is added that operates on opaque Operations. This pass can be inserted in a pass manager of any type to operate on any operation opaquely. An example of this opaque OperationPass is a VerifierPass, that simply runs the verifier opaquely on the current operation.
/// Pass to verify an operation and signal failure if necessary.
class VerifierPass : public OperationPass<VerifierPass> {
void runOnOperation() override {
Operation *op = getOperation();
if (failed(verify(op)))
signalPassFailure();
markAllAnalysesPreserved();
}
};
PiperOrigin-RevId: 266840344
- the list of passes run by mlir-cpu-runner included -lower-affine and
-lower-to-llvm but was missing -lower-to-cfg (because -lower-affine at
some point used to lower straight to CFG); add -lower-to-cfg in
between. IR with affine ops can now be run by mlir-cpu-runner.
- update -lower-to-cfg to be consistent with other passes (create*Pass methods
were changed to return unique ptrs, but -lower-to-cfg appears to have been
missed).
- mlir-cpu-runner was unable to parse custom form of affine op's - fix
link options
- drop unnecessary run options from test/mlir-cpu-runner/simple.mlir
(none of the test cases had loops)
- -convert-to-llvmir was changed to -lower-to-llvm at some point, but the
create pass method name wasn't updated (this pass converts/lowers to LLVM
dialect as opposed to LLVM IR). Fix this.
(If we prefer "convert", the cmd-line options could be changed to
"-convert-to-llvm/cfg" then.)
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#115
PiperOrigin-RevId: 266666909
Similar to enum, added a generator for structured data. This provide Dictionary that stores a fixed set of values and guarantees the values are valid. It is intended to store a fixed number of values by a given name.
PiperOrigin-RevId: 266437460
Instead of lowering the program in two steps (Standard->LLVM followed
by GPU->NVVM), leading to invalid IR inbetween, the runner now uses
one pattern based rewrite step to go directly from Standard+GPU to
LLVM+NVVM.
PiperOrigin-RevId: 265861934
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
This CL extends declarative rewrite rules to support matching and
generating ops with variadic operands/results. For this, the
generated `matchAndRewrite()` method for each pattern now are
changed to
* Use "range" types for the local variables used to store captured
values (`operand_range` for operands, `ArrayRef<Value *>` for
values, *Op for results). This allows us to have a unified way
of handling both single values and value ranges.
* Create local variables for each operand for op creation. If the
operand is variadic, then a `SmallVector<Value*>` will be created
to collect all values for that operand; otherwise a `Value*` will
be created.
* Use a collective result type builder. All result types are
specified via a single parameter to the builder.
We can use one result pattern to replace multiple results of the
matched root op. When that happens, it will require specifying
types for multiple results. Add a new collective-type builder.
PiperOrigin-RevId: 264588559
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
This CL is step 3/n towards building a simple, programmable and portable vector abstraction in MLIR that can go all the way down to generating assembly vector code via LLVM's opt and llc tools.
This CL adds support for converting MLIR n-D vector types to (n-1)-D arrays of 1-D LLVM vectors and a conversion VectorToLLVM that lowers the `vector.extractelement` and `vector.outerproduct` instructions to the proper mix of `llvm.vectorshuffle`, `llvm.extractelement` and `llvm.mulf`.
This has been independently verified to produce proper avx2 code.
Input:
```
func @vec_1d(%arg0: vector<4xf32>, %arg1: vector<8xf32>) -> vector<8xf32> {
%2 = vector.outerproduct %arg0, %arg1 : vector<4xf32>, vector<8xf32>
%3 = vector.extractelement %2[0 : i32]: vector<4x8xf32>
return %3 : vector<8xf32>
}
```
Command:
```
mlir-opt vector-to-llvm.mlir -vector-lower-to-llvm-dialect --disable-pass-threading | mlir-opt -lower-to-cfg -lower-to-llvm | mlir-translate --mlir-to-llvmir | opt -O3 | llc -O3 -march=x86-64 -mcpu=haswell -mattr=fma,avx2
```
Output:
```
vec_1d: # @vec_1d
# %bb.0:
vbroadcastss %xmm0, %ymm0
vmulps %ymm1, %ymm0, %ymm0
retq
```
PiperOrigin-RevId: 262895929
In declarative rewrite rules, a symbol can be bound to op arguments or
results in the source pattern, and it can be bound to op results in the
result pattern. This means given a symbol in the pattern, it can stands
for different things: op operand, op attribute, single op result,
op result pack. We need a better way to model this complexity so that
we can handle according to the specific kind a symbol corresponds to.
Created SymbolInfo class for maintaining the information regarding a
symbol. Also created a companion SymbolInfoMap class for a map of
such symbols, providing insertion and querying depending on use cases.
PiperOrigin-RevId: 262675515
The translation code predates the introduction of LogicalResult and was relying
on the obsolete LLVM convention of returning false on success. Change it to
use MLIR's LogicalResult abstraction instead. NFC.
PiperOrigin-RevId: 262589432
Previously we are emitting separate match() and rewrite()
methods, which requires conveying a match state struct
in a unique_ptr across these two methods. Changing to
emit matchAndRewrite() simplifies the picture.
PiperOrigin-RevId: 261906804
Instead of setting the attributes for decorations one by one
after constructing the op, this CL changes to attach all
the attributes for decorations to the attribute vector for
constructing the op. This should be simpler and more
efficient.
PiperOrigin-RevId: 261905578
This allows for proper forward declaration, as opposed to leaking the internal implementation via a using directive. This also allows for all pattern building to go through 'insert' methods on the OwningRewritePatternList, replacing uses of 'push_back' and 'RewriteListBuilder'.
PiperOrigin-RevId: 261816316
verifyUnusedValue is a bit strange given that it is specified in a
result pattern but used to generate match statements. Now we are
able to support multi-result ops better, we can retire it and replace
it with a HasNoUseOf constraint. This reduces the number of mechanisms.
PiperOrigin-RevId: 261166863
We allow to generate more ops than what are needed for replacing
the matched root op. Only the last N static values generated are
used as replacement; the others serve as auxiliary ops/values for
building the replacement.
With the introduction of multi-result op support, an op, if used
as a whole, may be used to replace multiple static values of
the matched root op. We need to consider this when calculating
the result range an generated op is to replace.
For example, we can have the following pattern:
```tblgen
def : Pattern<(ThreeResultOp ...),
[(OneResultOp ...), (OneResultOp ...), (OneResultOp ...)]>;
// Two op to replace all three results
def : Pattern<(ThreeResultOp ...),
[(TwoResultOp ...), (OneResultOp ...)]>;
// One op to replace all three results
def : Pat<(ThreeResultOp ...), (ThreeResultOp ...)>;
def : Pattern<(ThreeResultOp ...),
[(AuxiliaryOp ...), (ThreeResultOp ...)]>;
```
PiperOrigin-RevId: 261017235
Previously we use one single method with lots of branches to
generate multiple builders. This makes the method difficult
to follow and modify. This CL splits the method into multiple
dedicated ones, by extracting common logic into helper methods
while leaving logic specific to each builder in their own
methods.
PiperOrigin-RevId: 261011082
During serialization, the operand number must be used to get the
values assocaited with an operand. Using the argument number in Op
specification was wrong since some of the elements in the arguments
list might be attributes on the operation. This resulted in a segfault
during serialization.
Add a test that exercise that path.
PiperOrigin-RevId: 260977758
All non-argument attributes specified for an operation are treated as
decorations on the result value and (de)serialized using OpDecorate
instruction. An error is generated if an attribute is not an argument,
and the name doesn't correspond to a Decoration enum. Name of the
attributes that represent decoerations are to be the snake-case-ified
version of the Decoration name.
Add utility methods to convert to snake-case and camel-case.
PiperOrigin-RevId: 260792638
Add a missed library that needs to be linked with mlir-opt. This
results in a test failure in the MLIR due to the pass
`-convert-gpu-to-spirv` not being found.
PiperOrigin-RevId: 260773067
This CL adds an initial implementation for translation of kernel
function in GPU Dialect (used with a gpu.launch_kernel) op to a
spv.Module. The original function is translated into an entry
function.
Most of the heavy lifting is done by adding TypeConversion and other
utility functions/classes that provide most of the functionality to
translate from Standard Dialect to SPIR-V Dialect. These are intended
to be reusable in implementation of different dialect conversion
pipelines.
Note : Some of the files for have been renamed to be consistent with
the norm used by the other Conversion frameworks.
PiperOrigin-RevId: 260759165
RewriterGen was emitting invalid C++ code if the pattern required to create a
zero-result operation due to the absence of a special case that would avoid
generating a spurious comma. Handle this case. Also add rewriter tests for
zero-argument operations.
PiperOrigin-RevId: 260576998
Automatic generation of spirv::AccessChainOp (de)serialization needs
the (de)serialization emitters to handle argument specified as
Variadic<...>. To handle this correctly, this argument can only be
the last entry in the arguments list.
Add a test to (de)serialize spirv::AccessChainOp
PiperOrigin-RevId: 260532598
It's quite common that we want to put further constraints on the matched
multi-result op's specific results. This CL enables referencing symbols
bound to source op with the `__N` syntax.
PiperOrigin-RevId: 260122401
Per tacit agreement, individual dialects should now live in lib/Dialect/Name
with headers in include/mlir/Dialect/Name and tests in test/Dialect/Name.
PiperOrigin-RevId: 259896851
* Let them return `LogicalResult` so we can chain them together
with other functions returning `LogicalResult`.
* Added "Into" as the suffix to the function name and made the
`binary` as the first parameter so that it reads more naturally.
PiperOrigin-RevId: 259311636
We already have two levels of controls in SPIRVBase.td: hasOpcode and
autogenSerialization. The former controls whether to add an entry to
the dispatch table, while the latter controls whether to autogenerate
the op's (de)serialization method specialization. This is enough for
our cases. Remove the indirection from processOp to processOpImpl
to simplify the picture.
PiperOrigin-RevId: 259308711
Since the serialization of EntryPointOp contains the name of the
function as well, the function serialization emits the function name
using OpName instruction, which is used during deserialization to get
the correct function name.
PiperOrigin-RevId: 259158784
This specific PatternRewriter will allow for exposing hooks in the future that are only useful for the conversion framework, e.g. type conversions.
PiperOrigin-RevId: 258818122
For ops in SPIR-V dialect that are a direct mirror of SPIR-V
operations, the serialization/deserialization methods can be
automatically generated from the Op specification. To enable this an
'autogenSerialization' field is added to SPV_Ops. When set to
non-zero, this will enable the automatic (de)serialization function
generation
Also adding tests that verify the spv.Load, spv.Store and spv.Variable
ops are serialized and deserialized correctly. To fully support these
tests also add serialization and deserialization of float types and
spv.ptr types
PiperOrigin-RevId: 258684764
These ops should not belong to the std dialect.
This CL extracts them in their own dialect and updates the corresponding conversions and tests.
PiperOrigin-RevId: 258123853
following SPIRV Instructions serializaiton/deserialization are added
as well
OpFunction
OpFunctionParameter
OpFunctionEnd
OpReturn
PiperOrigin-RevId: 257869806
This CL splits the lowering of affine to LLVM into 2 parts:
1. affine -> std
2. std -> LLVM
The conversions mostly consists of splitting concerns between the affine and non-affine worlds from existing conversions.
Short-circuiting of affine `if` conditions was never tested or exercised and is removed in the process, it can be reintroduced later if needed.
LoopParametricTiling.cpp is updated to reflect the newly added ForOp::build.
PiperOrigin-RevId: 257794436
cuMemHostRegister expects the size of registered memory in bytes whereas the
memref descriptor in memref_t contains the number of elements. Get the actual
size in bytes instead.
PiperOrigin-RevId: 257589116
JSON spec into the SPIRBase.td file. This is done incrementally to
only import those opcodes that are needed, through use of the script
define_opcode.sh added.
PiperOrigin-RevId: 257517343
Eric Schweitz