This is useful for several reasons:
* In some situations the user can guarantee that thread-safety isn't necessary and don't want to pay the cost of synchronization, e.g., when parsing a very large module.
* For things like logging threading is not desirable as the output is not guaranteed to be in stable order.
This flag also subsumes the pass manager flag for multi-threading.
Differential Revision: https://reviews.llvm.org/D79266
Summary: std::function has a notoriously large amount of malloc traffic, whereas function_ref is a cheaper and more efficient alternative.
Differential Revision: https://reviews.llvm.org/D77959
Summary: This avoids the need for having global static initializers within the JITRunner support library, and only constructs the options when the runner is invoked.
Differential Revision: https://reviews.llvm.org/D77760
Summary:
This revision performs several cleanups on the translation infra:
* Removes the TranslateCLParser library and consolidates into Translation
- This was a weird library that existed in Support, and didn't really justify being a standalone library.
* Cleans up the internal registration and consolidates all of the translation functions within one registry.
Differential Revision: https://reviews.llvm.org/D77514
Summary: It is a very common user trap to think that the location printed along with the diagnostic is the same as the current operation that caused the error. This revision changes the behavior to always print the current operation, except for when diagnostics are being verified. This is achieved by moving the command line flags in IR/ to be options on the MLIRContext.
Differential Revision: https://reviews.llvm.org/D77095
Summary:
This file only contains references to test passes, and was never removed when the test passes were moved to the test/ directory.
Differential Revision: https://reviews.llvm.org/D76553
Putting this up mainly for discussion on
how this should be done. I am interested in MLIR from
the Julia side and we currently have a strong preference
to dynamically linking against the LLVM shared library,
and would like to have a MLIR shared library.
This patch adds a new cmake function add_mlir_library()
which accumulates a list of targets to be compiled into
libMLIR.so. Note that not all libraries make sense to
be compiled into libMLIR.so. In particular, we want
to avoid libraries which primarily exist to support
certain tools (such as mlir-opt and mlir-cpu-runner).
Note that the resulting libMLIR.so depends on LLVM, but
does not contain any LLVM components. As a result, it
is necessary to link with libLLVM.so to avoid linkage
errors. So, libMLIR.so requires LLVM_BUILD_LLVM_DYLIB=on
FYI, Currently it appears that LLVM_LINK_LLVM_DYLIB is broken
because mlir-tblgen is linked against libLLVM.so and
and independent LLVM components.
Previous version of this patch broke depencies on TableGen
targets. This appears to be because it compiled all
libraries to OBJECT libraries (probably because cmake
is generating different target names). Avoiding object
libraries results in correct dependencies.
(updated by Stephen Neuendorffer)
Differential Revision: https://reviews.llvm.org/D73130
CMake allows calling target_link_libraries() without a keyword,
but this usage is not preferred when also called with a keyword,
and has surprising behavior. This patch explicitly specifies a
keyword when using target_link_libraries().
Differential Revision: https://reviews.llvm.org/D75725
A printer refactoring removed automatic newline printing in the printer
of a ModuleOp. As a consequence, mlir-opt no longer printed a newline
after the closing brace of a module, which made it hard to distinguish
when used from command line. Print the newline character explicitly in
mlir-opt.
Putting this up mainly for discussion on
how this should be done. I am interested in MLIR from
the Julia side and we currently have a strong preference
to dynamically linking against the LLVM shared library,
and would like to have a MLIR shared library.
This patch adds a new cmake function add_mlir_library()
which accumulates a list of targets to be compiled into
libMLIR.so. Note that not all libraries make sense to
be compiled into libMLIR.so. In particular, we want
to avoid libraries which primarily exist to support
certain tools (such as mlir-opt and mlir-cpu-runner).
Note that the resulting libMLIR.so depends on LLVM, but
does not contain any LLVM components. As a result, it
is necessary to link with libLLVM.so to avoid linkage
errors. So, libMLIR.so requires LLVM_BUILD_LLVM_DYLIB=on
FYI, Currently it appears that LLVM_LINK_LLVM_DYLIB is broken
because mlir-tblgen is linked against libLLVM.so and
and independent LLVM components.
Previous version of this patch broke depencies on TableGen
targets. This appears to be because it compiled all
libraries to OBJECT libraries (probably because cmake
is generating different target names). Avoiding object
libraries results in correct dependencies.
(updated by Stephen Neuendorffer)
Differential Revision: https://reviews.llvm.org/D73130
When compiling libLLVM.so, add_llvm_library() manipulates the link libraries
being used. This means that when using add_llvm_library(), we need to pass
the list of libraries to be linked (using the LINK_LIBS keyword) instead of
using the standard target_link_libraries call. This is preparation for
properly dealing with creating libMLIR.so as well.
Differential Revision: https://reviews.llvm.org/D74864
Putting this up mainly for discussion on
how this should be done. I am interested in MLIR from
the Julia side and we currently have a strong preference
to dynamically linking against the LLVM shared library,
and would like to have a MLIR shared library.
This patch adds a new cmake function add_mlir_library()
which accumulates a list of targets to be compiled into
libMLIR.so. Note that not all libraries make sense to
be compiled into libMLIR.so. In particular, we want
to avoid libraries which primarily exist to support
certain tools (such as mlir-opt and mlir-cpu-runner).
Note that the resulting libMLIR.so depends on LLVM, but
does not contain any LLVM components. As a result, it
is necessary to link with libLLVM.so to avoid linkage
errors. So, libMLIR.so requires LLVM_BUILD_LLVM_DYLIB=on
FYI, Currently it appears that LLVM_LINK_LLVM_DYLIB is broken
because mlir-tblgen is linked against libLLVM.so and
and independent LLVM components
(updated by Stephen Neuendorffer)
Differential Revision: https://reviews.llvm.org/D73130
When compiling libLLVM.so, add_llvm_library() manipulates the link libraries
being used. This means that when using add_llvm_library(), we need to pass
the list of libraries to be linked (using the LINK_LIBS keyword) instead of
using the standard target_link_libraries call. This is preparation for
properly dealing with creating libMLIR.so as well.
Differential Revision: https://reviews.llvm.org/D74864
Previously, lib/Support/JitRunner.cpp was essentially a complete application,
performing all library initialization, along with dealing with command line
arguments and actually running passes. This differs significantly from
mlir-opt and required a dependency on InitAllDialects.h. This dependency
is significant, since it requires a dependency on all of the resulting
libraries.
This patch refactors the code so that tools are responsible for library
initialization, including registering all dialects, prior to calling
JitRunnerMain. This places the concern about what dialect to support
with the end application, enabling more extensibility at the cost of
a small amount of code duplication between tools. It also fixes
BUILD_SHARED_LIBS=on.
Differential Revision: https://reviews.llvm.org/D75272
In the previous state, we were relying on forcing the linker to include
all libraries in the final binary and the global initializer to self-register
every piece of the system. This change help moving away from this model, and
allow users to compose pieces more freely. The current change is only "fixing"
the dialect registration and avoiding relying on "whole link" for the passes.
The translation is still relying on the global registry, and some refactoring
is needed to make this all more convenient.
Differential Revision: https://reviews.llvm.org/D74461
* Rename CMake target MLIROptMain to MLIROptLib:
The target provides the main library
* Rename CMake target MLIRMlirOptLib to MLIRMlirOptMain:
The target provides the main() entry function
At the moment, the Bazel configuration of TenorFlow maps the target
MlirOptLib to "lib/Support/MlirOptMain.cpp" and MlirOptMain to
"tools/mlir-opt/mlir-opt.cpp". This is the other way around in the CMake
configuration. As discussed in the context of the pull request
https://github.com/tensorflow/tensorflow/pull/36301, it seems useful to
revise the naming in the MLIR repo.
Differential Revision: https://reviews.llvm.org/D73778
Summary:
This patch is a step towards enabling BUILD_SHARED_LIBS=on, which
builds most libraries as DLLs instead of statically linked libraries.
The main effect of this is that incremental build times are greatly
reduced, since usually only one library need be relinked in response
to isolated code changes.
The bulk of this patch is fixing incorrect usage of cmake, where library
dependencies are listed under add_dependencies rather than under
target_link_libraries or under the LINK_LIBS tag. Correct usage should be
like this:
add_dependencies(MLIRfoo MLIRfooIncGen)
target_link_libraries(MLIRfoo MLIRlib1 MLIRlib2)
A separate issue is that in cmake, dependencies between static libraries
are automatically included in dependencies. In the above example, if MLIBlib1
depends on MLIRlib2, then it is sufficient to have only MLIRlib1 in the
target_link_libraries. When compiling with shared libraries, it is necessary
to have both MLIRlib1 and MLIRlib2 specified if MLIRfoo uses symbols from both.
Reviewers: mravishankar, antiagainst, nicolasvasilache, vchuravy, inouehrs, mehdi_amini, jdoerfert
Reviewed By: nicolasvasilache, mehdi_amini
Subscribers: Joonsoo, merge_guards_bot, jholewinski, mgorny, mehdi_amini, rriddle, jpienaar, burmako, shauheen, antiagainst, csigg, arpith-jacob, mgester, lucyrfox, herhut, aartbik, liufengdb, llvm-commits
Tags: #llvm
Differential Revision: https://reviews.llvm.org/D73653
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
The ExecutionEngine was updated recently to only take the LLVM dialect as
input. Memrefs are no longer expected in the signature of the entry point
function by the executor so there is no need to allocate and free them. The
code in MemRefUtils is therefore dead and furthermore out of sync with the
recent evolution of memref type to support strides. Drop it.
PiperOrigin-RevId: 276272302
This function-like operation allows one to define functions that have wrapped
LLVM IR function type, in particular variadic functions. The operation was
added in parallel to the existing lowering flow, this commit only switches the
flow to use it.
Using a custom function type makes the LLVM IR dialect type system more
consistent and avoids complex conversion rules for functions that previously
had to use the built-in function type instead of a wrapped LLVM IR dialect type
and perform conversions during the analysis.
PiperOrigin-RevId: 273910855
This allows individual passes to define options structs and for these options to be parsed per instance of the pass while building the pass pipeline from the command line provided textual specification.
The user can specify these per-instance pipeline options like so:
```
struct MyPassOptions : public PassOptions<MyPassOptions> {
Option<int> exampleOption{*this, "flag-name", llvm:🆑:desc("...")};
List<int> exampleListOption{*this, "list-flag-name", llvm:🆑:desc("...")};
};
static PassRegistration<MyPass, MyPassOptions> pass("my-pass", "description");
```
PiperOrigin-RevId: 273650140
linalg_integration_test.mlir and simple.mlir were temporarily disabled due to an OSS-only failure.
The issue is that, once created, an llvm::Error must be explicitly checked before it can be discarded or overwritten.
This CL fixes the issue and reenable the test.
PiperOrigin-RevId: 271589651
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
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
These two operation interfaces will be used in a followup to support building a callgraph:
* CallOpInterface
- Operations providing this interface are call-like, and have a "call" target. A call target may be a symbol reference, via SymbolRefAttr, or a SSA value.
* CallableOpInterface
- Operations providing this interfaces define destinations to call-like operations, e.g. FuncOp. These operations may define any number of callable regions.
PiperOrigin-RevId: 270723300
Existing translations are either from MLIR or to MLIR. To support
cases like round-tripping some external format via MLIR, one must
chain two mlir-translate invocations together using pipes. This
can be problematic to support -split-input-file in mlir-translate
given that it won't work across pipes.
Motivated by the above, this CL adds another translation category
that allows file to file. This gives users more freedom.
PiperOrigin-RevId: 269636438
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
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
- the JIT codegen was being run at the default -O0 level; instead,
propagate the opt level from the cmd line.
Signed-off-by: Uday Bondhugula <uday@polymagelabs.com>
Closestensorflow/mlir#123
COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/mlir/pull/123 from bondhugula:jit-runner 3b055e47f94c9a48bf487f6400787478738cda02
PiperOrigin-RevId: 267778586
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
This commit introduces the bits to be able to dump JIT-compile
objects to external files by passing an object cache to OrcJit.
The new functionality is tested in mlir-cpu-runner under the flag
`dump-object-file`.
Closestensorflow/mlir#95
PiperOrigin-RevId: 266439265
JitRunner can use as entry points functions that produce either a single
'!llvm.f32' value or a list of memrefs. Memref support is legacy and was
introduced before MLIR could lower memref allocation and deallocation to
malloc/free calls so as to allocate the memory externally, and is likely to be
dropped in the future since it unconditionally runs affine+standard-to-llvm
lowering on the module instead of accepting the LLVM dialect. CUDA runner
relies on memref-based flow in the runner without actually returning anything.
Introduce a runner flow to use functions that return void as entry points.
PiperOrigin-RevId: 264381686
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 commit improves JitRunner so that it creates a target machine
for the current CPU host which is used to properly initialize LLVM's
TargetTransformInfo for such a target. This will enable optimizations
such as vectorization in LLVM when using JitRunner. Please, note that,
as part of this work, JITTargetMachineBuilder::detectHost() has been
extended to include the host CPU name and sub-target features as part of
the host CPU detection (https://reviews.llvm.org/D65760).
Closestensorflow/mlir#71
PiperOrigin-RevId: 262452525
Many LLVM transformations benefits from knowing the targets. This enables optimizations,
especially in a JIT context when the target is (generally) well-known.
Closestensorflow/mlir#49
PiperOrigin-RevId: 261840617
The TypeUtilities.{cpp,h}, currently living in {lib,include/mlir}/Support, do
not belong to the Support library. Instead, they form a separate utility
library that depends on the IR library. The operations it provides relate to
standard types (tensors, memrefs) as well as to operation manipulation, making
them a better fit for the main IR library.
PiperOrigin-RevId: 259108314
The API on TupleType::getFlattenedTypes follows our normal conventions by accepting an output parameter, but requires callers to allocate their own storage and lends itself to use in an imperative style. This makes it difficult to use in tablegen. The current solution is to define a lambda that is immediately called, but it's cleaner to extract that into a helper.
PiperOrigin-RevId: 258672046
As Functions/Modules becomes operations, these methods will conflict with the 'verify' hook already on derived operation types.
PiperOrigin-RevId: 256246112
As with Functions, Module will soon become an operation, which are value-typed. This eases the transition from Module to ModuleOp. A new class, OwningModuleRef is provided to allow for owning a reference to a Module, and will auto-delete the held module on destruction.
PiperOrigin-RevId: 256196193
Split out class to command line parser for translate methods into standalone
class. Similar to splitting up mlir-opt to reuse functionality with different
initialization.
PiperOrigin-RevId: 255225790
Enable reusing the real mlir-opt main from unit tests and in case where
additional initialization needs to happen before main is invoked (e.g., when
using different command line flag libraries).
PiperOrigin-RevId: 254764575
These are useful utility iterators helping use to get the element types of
operands/results of shaped types.
Also defined ranges for these iterators.
PiperOrigin-RevId: 254180888
This function returns the element type of the underlying type or the input type itself. This removes some of the casting and code from ODS and into C++ code.
I've not converted all the call sites (as this requires a new include and target) and wanted to run it past folks first.
PiperOrigin-RevId: 251978156
Multiple binaries have the needs to open input files. Use this function
to de-duplicate the code.
Also changed openOutputFile() to return errors using std::string since
it is a library call and accessing I/O in library call is not friendly.
PiperOrigin-RevId: 228878221
This has been a long-standing TODO in the build system. Now that we need to
share the non-inlined implementation of file utilities for translators, create
a separate library for support functionality. Move Support/* headers to the
new library in the build system.
PiperOrigin-RevId: 222398880
River Riddle