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Differential Revision: https://reviews.llvm.org/D125852
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The approach I took was to define a dialect 'extern' attribute that a GlobalOp can take as a value to signify external linkage. I think this approach should compose well and should also work with wherever the OpaqueElements work goes in the future (since that is just another kind of attribute). I special cased the GlobalOp parser/printer for this case because it is significantly easier on the eyes.
In the discussion, Jeff Niu had proposed an alternative syntax for GlobalOp that I ended up not taking. I did try to implement it but a) I don't think it made anything easier to read in the common case, and b) it made the parsing/printing logic a lot more complicated (I think I would need a completely custom parser/printer to do it well). Please have a look at the common cases where the global type and initial value type match: I don't think how I have it is too bad. The less common cases seem ok to me.
I chose to only implement the direct, constant load op since that is non side effecting and there was still discussion pending on that.
Differential Revision: https://reviews.llvm.org/D124318
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Lowering through libm gives us a baseline version, even though it's not
going to be particularly fast. This is similar to what we do for some
math dialect ops.
Differential Revision: https://reviews.llvm.org/D125550
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This is the first implementation of complex (f64 and f32) support
in the sparse compiler, with complex add/mul as first operations.
Note that various features are still TBD, such as other ops, and
reading in complex values from file. Also, note that the
std::complex<float> had a bit of an ABI issue when passed as
single argument. It is still TBD if better solutions are possible.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D125596
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This pass is to handle computationally complex operations like
tensor.pad which are not simply lowered to the exact same operation in
the memref dialect.
Differential Revision: https://reviews.llvm.org/D125384
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This patch fixes the includes for the new UInt class so that the api
test now passes, additionally it fixes the bazel files to account for
the new dependencies.
Differential Revision: https://reviews.llvm.org/D125490
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Add lowering of the vector.warp_execute_on_lane_0 into scf.if plus memory
transfer for the operands and yield values.
This also add an integration test running on GPU warp. The same tests can be
later re-used with different comment lines to tests distribution
transformations.
This is mostly from @springerm contribution.
Differential Revision: https://reviews.llvm.org/D125430
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This lets linalg.dot and friends lower to a complex muladd using ops
from the complex dialect.
Differential Revision: https://reviews.llvm.org/D125461
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TF already carries a patch for this.
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While executing the test suite for Tensorflow(v2.8.0), we encountered multiple TC failures with the below error
```
'z14' is not a recognized processor for this target
```
This patch adds the s390x target to the build target list. It fixes TC failures in multiple modules of Tensorflow on s390x arch. It is also tested to have no effect on x86 machines.
Reviewed By: GMNGeoffrey
Differential Revision: https://reviews.llvm.org/D125096
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Move async copy operations to NVGPU as they only exist on NV target and are
designed to match ptx semantic. This allows us to also add more fine grain
caching hint attribute to the op.
Add hint to bypass L1 and hook it up to NVVM op.
Differential Revision: https://reviews.llvm.org/D125244
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This ensures that attributes such as the index bitwidth propagate
correctly to the AMDGPUToROCDL patterns.
Differential Revision: https://reviews.llvm.org/D125320
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By analogy with the NVGPU dialect, introduce an AMDGPU dialect for
AMD-specific intrinsic wrappers.
The dialect initially includes wrappers around the raw buffer intrinsics.
On AMD GPUs, a memref can be converted to a "buffer descriptor" that
allows more precise control of memory access, such as by allowing for
out of bounds loads/stores to be replaced by 0/ignored without adding
additional conditional logic, which is important for performance.
The repository currently contains a limited conversion from
transfer_read/transfer_write to Mubuf intrinsics, which are an older,
deprecated intrinsic for the same functionality.
The new amdgpu.raw_buffer_* ops allow these operations to be used
explicitly and for including metadata such as whether the target
chipset is an RDNA chip or not (which impacts the interpretation of
some bits in the buffer descriptor), while still maintaining an
MLIR-like interface.
(This change also exposes the floating-point atomic add intrinsic.)
Reviewed By: ThomasRaoux
Differential Revision: https://reviews.llvm.org/D122765
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Differential Revision: https://reviews.llvm.org/D124819
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This commit adds a tiling op to the transform dialect as an external op.
Differential Revision: https://reviews.llvm.org/D124661
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Differential Revision: https://reviews.llvm.org/D124515
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When building with layering_check enabled, there needs to be a
dependency for each header include.
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The Bazel layering_check feature compiles libraries with `-fmodule-name=X
-fmodules-strict-decluse` which require #include to be in deps.
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after D124298
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Currently, the sequence of Transform dialect operations only supports a single
use of each operand (verified by the `transform.sequence` operation). This was
originally motivated by the need to guard against accessing a payload IR
operation associated with a transform IR value after this operation has likely
been rewritten by a transformation. However, not all Transform dialect
operations rewrite payload IR, in particular the "navigation" operation such as
`transform.pdl_match` do not.
Introduce memory effects to the Transform dialect operations to describe their
effect on the payload IR and the mapping between payload IR opreations and
transform IR values. Use these effects to replace the single-use rule, allowing
repeated reads and disallowing use-after-free, where operations with the "free"
effect are considered to "consume" the transform IR value and rewrite the
corresponding payload IR operations). As an additional improvement, this
enables code motion transformation on the transform IR itself.
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D124181
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Add shape func op for use (primarily) in shape function_library op. Allows
setting default dialect for some simpler authoring. This is a minimal version
of the ops needed.
Differential Revision: https://reviews.llvm.org/D124055
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* Move Module Bufferization to the bufferization dialect. The implementation is split into `OneShotModuleBufferize.cpp` and `FuncBufferizableOpInterfaceImpl.cpp`, so that the external model implementation can be easily moved to the func dialect in the future.
* Split and clean up test cases. A few test cases are still remaining in Linalg and will be updated separately.
* `linalg.inplaceable` is renamed to `bufferization.writable` to accurately reflect its current usage.
* Attributes and their verifiers are moved from the Linalg dialect to the Bufferization dialect.
* Expand documentation.
* Add a new flag to One-Shot Bufferize to allow for function boundary bufferization.
Differential Revision: https://reviews.llvm.org/D122229
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This introduces a pair of ops to the Transform dialect that connect it to PDL
patterns. Transform dialect relies on PDL for matching the Payload IR ops that
are about to be transformed. For this purpose, it provides a container op for
patterns, a "pdl_match" op and transform interface implementations that call
into the pattern matching infrastructure.
To enable the caching of compiled patterns, this also provides the extension
mechanism for TransformState. Extensions allow one to store additional
information in the TransformState and thus communicate it between different
Transform dialect operations when they are applied. They can be added and
removed when applying transform ops. An extension containing a symbol table in
which the pattern names are resolved and a pattern compilation cache is
introduced as the first client.
Depends On D123664
Reviewed By: Mogball
Differential Revision: https://reviews.llvm.org/D124007
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The legacy passes are deprecated now and would be removed in near
future. This patch tries to remove legacy passes in coroutines.
Reviewed By: aeubanks
Differential Revision: https://reviews.llvm.org/D123918
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Sequence is an important transform combination primitive that just indicates
transform ops being applied in a row. The simplest version requires fails
immediately if any transformation in the sequence fails. Introducing this
operation allows one to start placing transform IR within other IR.
Depends On D123135
Reviewed By: Mogball, rriddle
Differential Revision: https://reviews.llvm.org/D123664
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LICM is refactored into a utility that is application on any region. The implementation is moved to Transform/Utils.
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This reverts commit 3131f808243abe3746280e016ab9459c14d9e53b.
This commit broke the Windows mlir bot:
https://lab.llvm.org/buildbot/#/builders/13/builds/19745
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LICM is refactored into a utility that is application on any region. The implementation is moved to Transform/Utils.
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Differential Revision: https://reviews.llvm.org/D123868
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Add vector op warp_execute_on_lane_0 that will be used to do incremental
vector distribution in order to target warp level vector programming for
architectures with GPU-like SIMT programming model.
The idea behing the op is discussed further on discourse:
https://discourse.llvm.org/t/vector-vector-distribution-large-vector-to-small-vector/1983/23
Differential Revision: https://reviews.llvm.org/D123703
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This introduce a new dialect for vendro specific ptx operations. This
also adds the first operation ldmatrix as an example. More operations
will be added in follow up patches.
This new dialect is meant to be a bridge between GPU and Vector
dialectis and NVVM dialect.
This is based on the RFC proposed here:
https://discourse.llvm.org/t/rfc-add-nv-gpu-dialect-hw-specific-extension-of-gpu-dialect-for-nvidia-gpus/61466/8
Differential Revision: https://reviews.llvm.org/D123266
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This dialect provides operations that can be used to control transformation of
the IR using a different portion of the IR. It refers to the IR being
transformed as payload IR, and to the IR guiding the transformation as
transform IR.
The main use case for this dialect is orchestrating fine-grain transformations
on individual operations or sets thereof. For example, it may involve finding
loop-like operations with specific properties (e.g., large size) in the payload
IR, applying loop tiling to those and only those operations, and then applying
loop unrolling to the inner loops produced by the previous transformations. As
such, it is not intended as a replacement for the pass infrastructure, nor for
the pattern rewriting infrastructure. In the most common case, the transform IR
will be processed and applied to payload IR by a pass. Transformations
expressed by the transform dialect may be implemented using the pattern
infrastructure or any other relevant MLIR component.
This dialect is designed to be extensible, that is, clients of this dialect are
allowed to inject additional operations into this dialect using the newly
introduced in this patch `TransformDialectExtension` mechanism. This allows the
dialect to avoid a dependency on the implementation of the transformation as
well as to avoid introducing dialect-specific transform dialects.
See https://discourse.llvm.org/t/rfc-interfaces-and-dialects-for-precise-ir-transformation-control/60927.
Reviewed By: nicolasvasilache, Mogball, rriddle
Differential Revision: https://reviews.llvm.org/D123135
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Move the operations that correspond to LLVM IR intrinsics in a separate .td
file. This makes it easier to maintain the intrinsics and decreases the compile
time of LLVMDialect.cpp by ~25%.
Depends On D123310
Reviewed By: wsmoses, jacquesguan
Differential Revision: https://reviews.llvm.org/D123315
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Differential Revision: https://reviews.llvm.org/D120203
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Matches official cmake build.
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Differential Revision: https://reviews.llvm.org/D123570
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Adding annotations on as-needed bases, currently only for memrefCopy, but in general all C API functions that take pointers to memory allocated/initialized inside the jit-compiled code must be annotated, to be able to run with msan.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D123557
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Differential Revision: https://reviews.llvm.org/D123558
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The only user was removed by d351f54a076edf24c2a2bfda7cc7e3313ee3eecf.
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