| 文件 | 最后提交记录 | 最后更新时间 |
|---|---|---|
[MLIR][Python] Add arg_attrs and res_attrs to gpu func (#168475) I missed these attributes when I added the wrapper for GPUFuncOp in fbdd98f74f0d. | 8 个月前 | |
[mlir][linalg][python] Add Python Bindings for Inferring Contraction Dimensions from Affine Maps (#167587) This PR exposes linalg::inferContractionDims(ArrayRef<AffineMap>) to Python, allowing users to infer contraction dimensions (batch/m/n/k) directly from a list of affine maps without needing an operation. --------- Signed-off-by: Bangtian Liu <liubangtian@gmail.com> | 8 个月前 | |
[mlir python] Port in-tree dialects to nanobind. (#119924) This is a companion to #118583, although it can be landed independently because since #117922 dialects do not have to use the same Python binding framework as the Python core code. This PR ports all of the in-tree dialect and pass extensions to nanobind, with the exception of those that remain for testing pybind11 support. This PR also: * removes CollectDiagnosticsToStringScope from NanobindAdaptors.h. This was overlooked in a previous PR and it is duplicated in Diagnostics.h. --------- Co-authored-by: Jacques Pienaar <jpienaar@google.com> | 1 年前 | |
[mlir][Affine] Let affine.[de]linearize_index omit outer bounds (#116103) The affine.delinearize_index and affine.linearize_index operations, as currently defined, require providing a length N basis to [de]linearize N values. The first value in this basis is never used during lowering and is unused during lowering. (Note that, even though it isn't used during lowering it can still be used to, for example, remove length-1 outputs from a delinearize). This dead value makes sense in the original context of these operations, which is linearizing or de-linearizing indexes to memref<>s, vector<>s, and other shaped types, where that outer bound is avaliable and may be useful for analysis. However, other usecases exist where the outer bound is not known. For example: %thread_id_x = gpu.thread_id x : index %0:3 = affine.delinearize_index %thread_id_x into (4, 16) : index,index, index In this code, we don't know the upper bound of the thread ID, but we do want to construct the ?x4x16 grid of delinearized values in order to further partition the GPU threads. In order to support such usecases, we broaden the definition of affine.delinearize_index and affine.linearize_index to make the outer bound optional. In the case of affine.delinearize_index, where the number of results is a function of the size of the passed-in basis, we augment all existing builders with a hasOuterBound argument, which, for backwards compatibilty and to preserve the natural usage of the op, defaults to true. If this flag is true, the op returns one result per basis element, if it is false, it returns one extra result in position 0. We also update existing canonicalization patterns (and move one of them into the folder) to handle these cases. Note that disagreements about the outer bound now no longer prevent delinearize/linearize cancelations. | 1 年前 | |
[MLIR][AMDGPU] Use Attr for resetOffset + boundsCheck in RawBufferCastOp (#149939) In order to access and modify resetOffset and boundsCheck of RawBufferCastOp in pythonic binding, we will have to use Attrs instead of Property. This is because we do not have python binding support for property yet. We should move back to property once we add pythonic binding support for it. --------- Signed-off-by: Stanley Winata <stanley.winata@amd.com> | 1 年前 | |
Reapply "[mlir][py] better support for arith.constant construction" (#84142) Arithmetic constants for vector types can be constructed from objects implementing Python buffer protocol such as array.array. Note that until Python 3.12, there is no typing support for buffer protocol implementers, so the annotations use array explicitly. Reverts llvm/llvm-project#84103 | 2 年前 | |
[mlir][arith] Add overflow flags support to arith ops (#78376) Add overflow flags support to the following ops: * arith.addi * arith.subi * arith.muli Example of new syntax: %res = arith.addi %arg1, %arg2 overflow<nsw> : i64 Similar to existing LLVM dialect syntax %res = llvm.add %arg1, %arg2 overflow<nsw> : i64 Tablegen canonicalization patterns updated to always drop flags, proper support with tests will be added later. Updated LLVMIR translation as part of this commit as it currenly written in a way that it will crash when new attributes added to arith ops otherwise. Also lower arith overflow flags to corresponding SPIR-V op decorations Discussion https://discourse.llvm.org/t/rfc-integer-overflow-flags-support-in-arith-dialect/76025 This effectively rolls forward #77211, #77700 and #77714 while adding a test to ensure the Python usage is not broken. More follow up needed but unrelated to the core change here. The changes here are minimal and just correspond to "textual namespacing" ODS side, no C++ or Python changes were needed. --------- --------- Co-authored-by: Ivan Butygin <ivan.butygin@gmail.com>, Yi Wu <yi.wu2@arm.com> | 2 年前 | |
[mlir][python] Fix generation of Python bindings for async dialect (#75960) The Python bindings generated for "async" dialect didn't include any of the "async" dialect ops. This PR fixes issues with generation of Python bindings for "async" dialect and adds a test case to use them. | 2 年前 | |
| 1 年前 | ||
[mlir][CAPI, python bindings] Expose Operation::setSuccessor (#67922) This is useful for emitting (using the python bindings) cf.br to blocks that are declared lexically post block creation. | 2 年前 | |
[NFC][Py Reformat] Reformat python files in mlir subdir This is an ongoing series of commits that are reformatting our Python code. Reformatting is done with black. If you end up having problems merging this commit because you have made changes to a python file, the best way to handle that is to run git checkout --ours <yourfile> and then reformat it with black. If you run into any problems, post to discourse about it and we will try to help. RFC Thread below: https://discourse.llvm.org/t/rfc-document-and-standardize-python-code-style Differential Revision: https://reviews.llvm.org/D150782 | 3 年前 | |
[mlir][emitc] Add support for C-API/python binding to EmitC dialect (#119476) Added EmitC dialect bindings. | 1 年前 | |
[mlir,python] Fix case when FuncOp.arg_attrs is not set (#117188) FuncOps can have arg_attrs, an array of dictionary attributes associated with their arguments. E.g., mlir func.func @main(%arg0: tensor<8xf32> {test.attr_name = "value"}, %arg1: tensor<8x16xf32>) These are exposed via the MLIR Python bindings with my_funcop.arg_attrs. In this case, it would return [{test.attr_name = "value"}, {}], i.e., %arg1 has an empty DictAttr. However, if I try and access this property from a FuncOp with an empty arg_attrs, e.g., mlir func.func @main(%arg0: tensor<8xf32>, %arg1: tensor<8x16xf32>) This raises the error: python return ArrayAttr(self.attributes[ARGUMENT_ATTRIBUTE_NAME]) ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^ KeyError: 'attempt to access a non-existent attribute' This PR fixes this by returning the expected [{}, {}]. | 1 年前 | |
[mlir][python] Enable python bindings for Index dialect (#85827) This small patch enables python bindings for the index dialect. --------- Co-authored-by: Steven Varoumas <steven.varoumas1@huawei.com> | 2 年前 | |
[MLIR][Python] Add python bindings for IRDL dialect (#158488) In this PR we add basic python bindings for IRDL dialect, so that python users can create and load IRDL dialects in python. This allows users, to some extent, to define dialects in Python without having to modify MLIR’s CMake/TableGen/C++ code and rebuild, making prototyping more convenient. A basic example is shown below (and also in the added test case): python # create a module with IRDL dialects module = Module.create() with InsertionPoint(module.body): dialect = irdl.DialectOp("irdl_test") with InsertionPoint(dialect.body): op = irdl.OperationOp("test_op") with InsertionPoint(op.body): f32 = irdl.is_(TypeAttr.get(F32Type.get())) irdl.operands_([f32], ["input"], [irdl.Variadicity.single]) # load the module irdl.load_dialects(module) # use the op defined in IRDL m = Module.parse(""" module { %a = arith.constant 1.0 : f32 "irdl_test.test_op"(%a) : (f32) -> () } """) | 10 个月前 | |
[MLIR][Python] add GetTypeID for llvm.struct_type and llvm.ptr and enable downcasting (#169383) | 8 个月前 | |
[NFC][Py Reformat] Reformat python files in mlir subdir This is an ongoing series of commits that are reformatting our Python code. Reformatting is done with black. If you end up having problems merging this commit because you have made changes to a python file, the best way to handle that is to run git checkout --ours <yourfile> and then reformat it with black. If you run into any problems, post to discourse about it and we will try to help. RFC Thread below: https://discourse.llvm.org/t/rfc-document-and-standardize-python-code-style Differential Revision: https://reviews.llvm.org/D150782 | 3 年前 | |
[mlir][py] Reduce size of allocation memrefs in test. | 2 年前 | |
Add SymbolRefAttr to python bindings Differential Revision: https://reviews.llvm.org/D154541 | 3 年前 | |
[mlir][py] Add NVGPU's TensorMapDescriptorType in py bindings (#88855) This PR adds NVGPU dialects' TensorMapDescriptorType in the py bindings. This is a follow-up issue from [this PR](https://github.com/llvm/llvm-project/pull/87153#discussion_r1546193095) | 2 年前 | |
| 8 个月前 | ||
[mlir][python] Fix how the mlir variadic Python accessor _ods_equally_sized_accessor is used (#101132) (#106003) As reported in https://github.com/llvm/llvm-project/issues/101132, this fixes two bugs: 1. When accessing variadic operands inside an operation, it must be accessed as self.operation.operands instead of operation.operands 2. The implementation of the equally_sized_accessor function is doing wrong arithmetics when calculating the resulting index and group sizes. I have added a test for the equally_sized_accessor function, which did not have a test previously. | 1 年前 | |
| 9 个月前 | ||
[mlir][python bindings] turn on openmp Just as in https://reviews.llvm.org/D157820, dialect registration is independent of any vendor specific libs having been linked/built/etc. Reviewed By: rkayaith Differential Revision: https://reviews.llvm.org/D158670 | 2 年前 | |
Reapply "[mlir][PDL] Add support for native constraints with results (#82760)" with a small stack-use-after-scope fix in getConstraintPredicates() This reverts commit c80e6edba4a9593f0587e27fa0ac825ebe174afd. | 2 年前 | |
[mlir] Introduce Python bindings for the PDL dialect This change adds full python bindings for PDL, including types and operations with additional mixins to make operation construction more similar to the PDL syntax. Reviewed By: ftynse Differential Revision: https://reviews.llvm.org/D117458 | 4 年前 | |
[MLIR] [Python] ir.Value is now generic in the type of the value it holds (#166148) This makes it similar to mlir::TypedValue in the MLIR C++ API and allows users to be more specific about the values they produce or accept. Co-authored-by: Maksim Levental <maksim.levental@gmail.com> | 8 个月前 | |
Sub-channel quantized type implementation (#120172) This is an implementation for [RFC: Supporting Sub-Channel Quantization in MLIR](https://discourse.llvm.org/t/rfc-supporting-sub-channel-quantization-in-mlir/82694). In order to make the review process easier, the PR has been divided into the following commit labels: 1. **Add implementation for sub-channel type:** Includes the class design for UniformQuantizedSubChannelType, printer/parser and bytecode read/write support. The existing types (per-tensor and per-axis) are unaltered. 2. **Add implementation for sub-channel type:** Lowering of quant.qcast and quant.dcast operations to Linalg operations. 3. **Adding C/Python Apis:** We first define he C-APIs and build the Python-APIs on top of those. 4. **Add pass to normalize generic ....:** This pass normalizes sub-channel quantized types to per-tensor per-axis types, if possible. A design note: - **Explicitly storing the quantized_dimensions, even when they can be derived for ranked tensor.** While it's possible to infer quantized dimensions from the static shape of the scales (or zero-points) tensor for ranked data tensors ([ref](https://discourse.llvm.org/t/rfc-supporting-sub-channel-quantization-in-mlir/82694/3) for background), there are cases where this can lead to ambiguity and issues with round-tripping. Consider the example: tensor<2x4x!quant.uniform<i8:f32:{0:2, 0:2}, {{s00:z00, s01:z01}}>> The shape of the scales tensor is [1, 2], which might suggest that only axis 1 is quantized. While this inference is technically correct, as the block size for axis 0 is a degenerate case (equal to the dimension size), it can cause problems with round-tripping. Therefore, even for ranked tensors, we are explicitly storing the quantized dimensions. Suggestions welcome! PS: I understand that the upcoming holidays may impact your schedule, so please take your time with the review. There's no rush. | 1 年前 | |
[mlir][ROCDL] Refactor wmma intrinsics to use attributes not operands where possible (#167041) The current implementation of the WMMA intrinsic ops as they are defined in the ROCDL tablegen is incorrect. They represent as operands what should be attributes such as clamp, opsel, signA/signB. This change performs a refactoring to bring it in line with what we expect. --------- Signed-off-by: Muzammiluddin Syed <muzasyed@amd.com> | 8 个月前 | |
[MLIR][Python] Add wrappers for scf.index_switch (#167458) The C++ index switch op has utilities for getCaseBlock(int i) and getDefaultBlock(), so these have been added. Optional body builder args have been added: one for the default case and one for the switch cases. | 8 个月前 | |
[NFC][Py Reformat] Reformat python files in mlir subdir This is an ongoing series of commits that are reformatting our Python code. Reformatting is done with black. If you end up having problems merging this commit because you have made changes to a python file, the best way to handle that is to run git checkout --ours <yourfile> and then reformat it with black. If you run into any problems, post to discourse about it and we will try to help. RFC Thread below: https://discourse.llvm.org/t/rfc-document-and-standardize-python-code-style Differential Revision: https://reviews.llvm.org/D150782 | 3 年前 | |
[MLIR][Python] Add shard Dialect Python Bindings (#162578) Add Python bindings for shard dialect. Provide means for creating constructs in this dialect in Python. | 9 个月前 | |
[mlir][SMT] add python bindings (#135674) This PR adds "rich" python bindings to SMT dialect. | 1 年前 | |
[mlir][spirv] Add support for C-API/python binding to SPIR-V dialect (#76055) Enable bindings. --------- Co-authored-by: jungpark-mlir <jungwook@jungwook-22.04> | 2 年前 | |
[mlir][python] meta region_op (#75673) | 2 年前 | |
Enable MLIR Python bindings for TOSA. Differential Revision: https://reviews.llvm.org/D103035 | 5 年前 | |
[MLIR][Transform][Python] Sync derived classes and their wrappers (#166871) Updates the derived Op-classes for the main transform ops to have all the arguments, etc, from the auto-generated classes. Additionally updates and adds missing snake_case wrappers for the derived classes which shadow the snake_case wrappers of the auto-generated classes, which were hitherto exposed alongside the derived classes. | 8 个月前 | |
[mlir][bufferization] Remove allow-return-allocs and create-deallocs pass options, remove bufferization.escape attribute (#66619) This commit removes the deallocation capabilities of one-shot-bufferization. One-shot-bufferization should never deallocate any memrefs as this should be entirely handled by the ownership-based-buffer-deallocation pass going forward. This means the allow-return-allocs pass option will default to true now, create-deallocs defaults to false and they, as well as the escape attribute indicating whether a memref escapes the current region, will be removed. A new allow-return-allocs-from-loops option is added as a temporary workaround for some bufferization limitations. | 2 年前 | |
[MLIR][Transform] expose transform.debug extension in Python (#145550) Removes the Debug... prefix on the ops in tablegen, in line with pretty much all other Transform-dialect extension ops. This means that the ops in Python look like debug.EmitParamAsRemarkOp/debug.emit_param_as_remark instead of debug.DebugEmitParamAsRemarkOp/debug.debug_emit_param_as_remark. | 1 年前 | |
[MLIR][transform][python] Introduce abstractions for handles to values and parameters (#77305) In addition to the existing OpHandle which provides an abstraction to emit transform ops targeting operations this introduces a similar concept for _values_ and _parameters_ in form of ValueHandle and ParamHandle. New core transform abstractions: - constant_param - OpHandle.get_result - OpHandle.print - ValueHandle.get_defining_op | 2 年前 | |
[mlir][gpu][transform] Provide better error messages and avoid crashing in MapForallToBlocks. This revision addresses issues surfaced in https://reviews.llvm.org/D159093 | 2 年前 | |
[MLIR][Transform][Python] Expose applying named_sequences as a method (#168223) Makes it so that a NamedSequenceOp can be directly applied to a Module, via a method apply(...). | 8 个月前 | |
[MLIR][Python] Add missing peel_front argument to LoopPeelOp's extension class (#81424) | 2 年前 | |
[mlir][memref][transform] Add new alloca_to_global op. (#66511) This PR adds a new transform op that replaces memref.allocas with memref.get_globals to newly inserted memref.globals. This is useful, for example, for allocations that should reside in the shared memory of a GPU, which have to be declared as globals. | 2 年前 | |
[mlir] enable python bindings for nvgpu transforms (#68088) Expose the autogenerated bindings. Co-authored-by: Martin Lücke <mluecke@google.com> | 2 年前 | |
[MLIR][Transform][SMT] Allow for declarative computations in schedules (#160895) By allowing transform.smt.constrain_params's region to yield SMT-vars, op instances can declare relationships, through constraints, on incoming params-as-SMT-vars and outgoing SMT-vars-as-params. This makes it possible to declare that computations on params should be performed. The semantics are that the yielded SMT-vars should be from any valid satisfying assignment/model of the constraints in the region. | 9 个月前 | |
[mlir][sparse] Generates python bindings for SparseTensorTransformOps. (#66937) | 2 年前 | |
[MLIR][Linalg][Transform] Expose more args in VectorizeChildren[...] op's Python bindings (#166134) Expose missing boolean arguments in VectorizeChildrenAndApplyPatternsOp Python bindings. | 8 个月前 | |
[mlir][python bindings] generate all the enums This PR implements python enum bindings for *all* the enums - this includes I*Attrs (including positional/bit) and Dialect/EnumAttr. There are a few parts to this: 1. CMake: a small addition to declare_mlir_dialect_python_bindings and declare_mlir_dialect_extension_python_bindings to generate the enum, a boolean arg GEN_ENUM_BINDINGS to make it opt-in (even though it works for basically all of the dialects), and an optional GEN_ENUM_BINDINGS_TD_FILE for handling corner cases. 2. EnumPythonBindingGen.cpp: there are two weedy aspects here that took investigation: 1. If an enum attribute is not a Dialect/EnumAttr then the EnumAttrInfo record is canonical, as far as both the cases of the enum **and the AttrDefName**. On the otherhand, if an enum is a Dialect/EnumAttr then the EnumAttr record has the correct AttrDefName ("load bearing", i.e., populates ods.ir.AttributeBuilder('<NAME>')) but its enum field contains the cases, which is an instance of EnumAttrInfo. The solution is to generate an one enum class for both Dialect/EnumAttr and "independent" EnumAttrInfo but to make that class interopable with two builder registrations that both do the right thing (see next sub-bullet). 2. Because we don't have a good connection to cpp EnumAttr, i.e., only the enum class getters are exposed (like DimensionAttr::get(Dimension value)), we have to resort to parsing e.g., Attribute.parse(f'#gpu<dim {x}>'). This means that the set of supported assemblyFormats (for the enum) is fixed at compile of MLIR (currently 2, the only 2 I saw). There might be some things that could be done here but they would require quite a bit more C API work to support generically (e.g., casting ints to enum cases and binding all the getters or going generically through the symbolize* methods, like symbolizeDimension(uint32_t) or symbolizeDimension(StringRef)). A few small changes: 1. In addition, since this patch registers default builders for attributes where people might've had their own builders already written, I added a replace param to AttributeBuilder.insert (False by default). 2. makePythonEnumCaseName can't handle all the different ways in which people write their enum cases, e.g., llvm.CConv.Intel_OCL_BI, which gets turned into INTEL_O_C_L_B_I (because llvm::convertToSnakeFromCamelCase doesn't look for runs of caps). So I dropped it. On the otherhand regularization does need to done because some enums have None as a case (and others might have other python keywords). 3. I turned on llvm dialect generation here in order to test nvvm.WGMMAScaleIn, which is an enum with [[ https://github.com/llvm/llvm-project/blob/d7e26b56207cbd8995296c5bb7c11ce676b649da/mlir/include/mlir/IR/EnumAttr.td#L22-L25 | no explicit discriminator ]] for the neg case. Note, dialects that didn't get a GEN_ENUM_BINDINGS don't have any enums to generate. Let me know if I should add more tests (the three trivial ones I added exercise both the supported assemblyFormats and replace=True). Reviewed By: stellaraccident Differential Revision: https://reviews.llvm.org/D157934 | 2 年前 | |
[MLIR][Transform][Tune] Introduce transform.tune.alternatives op (#160724) This op enables expressing uncertainty regarding what should be happening at particular places in transform-dialect schedules. In particular, it enables representing a choice among alternative regions. This choice is resolved through providing a selected_region argument. When this argument is provided, the semantics are such that it is valid to rewrite the op through substituting in the selected region -- with the op's interpreted semantics corresponding to exactly this. This op represents another piece of the puzzle w.r.t. a toolkit for expressing autotuning problems with the transform dialect. Note that this goes beyond tuning knobs _on_ transforms, going further by making it tunable which (sequences of) transforms are to be applied. | 9 个月前 | |
[mlir][vector] Refine Vector to LLVM lowering options (#159553) This is a follow-up to https://github.com/llvm/llvm-project/pull/144307, where we removed vector.matrix_multiply and vector.flat_transpose from the Vector dialect. This PR: * Updates comments that were missed in the previous change. * Renames relevant -convert-vector-to-llvm= options: - vector-contract-lowering=matmul → vector-contract-lowering=llvmintr - vector-transpose-lowering=flat_transpose → vector-transpose-lowering=llvmintr These new names better reflect the actual transformation target - LLVM intrinsics - rather than the now-removed abstract operations. | 10 个月前 | |
[MLIR][XeGPU][TransformOps] Add slice_dims argument to set_op_layout_attr and set_desc_layout (#168929) set_op_layout_attr and set_desc_layout transform ops wrap xegpu.layout in an xegpu.slice attribute if slice_dims argument is set. | 8 个月前 | |
[mlir][python] UB dialect python bindings (#157127) | 10 个月前 | |
[mlir][vector] Make the in_bounds attribute mandatory (#97049) At the moment, the in_bounds attribute has two confusing/contradicting properties: 1. It is both optional _and_ has an effective default-value. 2. The default value is "out-of-bounds" for non-broadcast dims, and "in-bounds" for broadcast dims. (see the isDimInBounds vector interface method for an example of this "default" behaviour [1]). This PR aims to clarify the logic surrounding the in_bounds attribute by: * making the attribute mandatory (i.e. it is always present), * always setting the default value to "out of bounds" (that's consistent with the current behaviour for the most common cases). #### Broadcast dimensions in tests As per [2], the broadcast dimensions requires the corresponding in_bounds attribute to be true: vector.transfer_read op requires broadcast dimensions to be in-bounds The changes in this PR mean that we can no longer rely on the default value in cases like the following (dim 0 is a broadcast dim): mlir %read = vector.transfer_read %A[%base1, %base2], %f, %mask {permutation_map = affine_map<(d0, d1) -> (0, d1)>} : memref<?x?xf32>, vector<4x9xf32> Instead, the broadcast dimension has to explicitly be marked as "in bounds: mlir %read = vector.transfer_read %A[%base1, %base2], %f, %mask {in_bounds = [true, false], permutation_map = affine_map<(d0, d1) -> (0, d1)>} : memref<?x?xf32>, vector<4x9xf32> All tests with broadcast dims are updated accordingly. #### Changes in "SuperVectorize.cpp" and "Vectorization.cpp" The following patterns in "Vectorization.cpp" are updated to explicitly set the in_bounds attribute to false: * LinalgCopyVTRForwardingPattern and LinalgCopyVTWForwardingPattern Also, vectorizeAffineLoad (from "SuperVectorize.cpp") and vectorizeAsLinalgGeneric (from "Vectorization.cpp") are updated to make sure that xfer Ops created by these hooks set the dimension corresponding to broadcast dims as "in bounds". Otherwise, the Op verifier would complain Note that there is no mechanism to verify whether the corresponding memory access are indeed in bounds. Still, this is consistent with the current behaviour where the broadcast dim would be implicitly assumed to be "in bounds". [1] https://github.com/llvm/llvm-project/blob/4145ad2bac4bb99d5034d60c74bb2789f6c6e802/mlir/include/mlir/Interfaces/VectorInterfaces.td#L243-L246 [2] https://mlir.llvm.org/docs/Dialects/Vector/#vectortransfer_read-vectortransferreadop | 2 年前 |
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