Includes a version of a quantized conv2D operations with a lowering from TOSA
to linalg with corresponding test. We keep the quantized and quantized variants
as separate named ops to avoid the additional operations for non-quantized
convolutions.
Differential Revision: https://reviews.llvm.org/D106407
libMLIRPublicAPI.so came into existence early when the Python and C-API were being co-developed because the Python extensions need a single DSO which exports the C-API to link against. It really should never have been exported as a mondo library in the first place, which has caused no end of problems in different linking modes, etc (i.e. the CAPI tests depended on it).
This patch does a mechanical move that:
* Makes the C-API tests link directly to their respective libraries.
* Creates a libMLIRPythonCAPI as part of the Python bindings which assemble to exact DSO that they need.
This has the effect that the C-API is no longer monolithic and can be subset and used piecemeal in a modular fashion, which is necessary for downstreams to only pay for what they use. There are additional, more fundamental changes planned for how the Python API is assembled which should make it more out of tree friendly, but this minimal first step is necessary to break the fragile dependency between the C-API and Python API.
Downstream actions required:
* If using the C-API and linking against MLIRPublicAPI, you must instead link against its constituent components. As a reference, the Python API dependencies are in lib/Bindings/Python/CMakeLists.txt and approximate the full set of dependencies available.
* If you have a Python API project that was previously linking against MLIRPublicAPI (i.e. to add its own C-API DSO), you will want to `s/MLIRPublicAPI/MLIRPythonCAPI/` and all should be as it was. There are larger changes coming in this area but this part is incremental.
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D106369
The unstrided transposed conv can be represented as a regular convolution.
Lower to this variant to handle the basic case. This includes transitioning from
the TC defined convolution operation and a yaml defined one.
Reviewed By: NatashaKnk
Differential Revision: https://reviews.llvm.org/D106389
Added the named op variants for quantized matmul and quantized batch matmul
with the necessary lowerings/tests from tosa's matmul/fully connected ops.
Current version does not use the contraction op interface as its verifiers
are not compatible with scalar operations.
Differential Revision: https://reviews.llvm.org/D105063
This deletes all the pooling ops in LinalgNamedStructuredOpsSpec.tc. All the
uses are replaced with the yaml pooling ops.
Reviewed By: gysit, rsuderman
Differential Revision: https://reviews.llvm.org/D106181
Annotate LinalgNamedStructuredOps.yaml with a comment stating the file is auto-generated and should not be edited manually.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D105809
Introduce the exp and log function in OpDSL. Add the soft plus operator to test the emitted IR in Python and C++.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D105420
Add the min operation to OpDSL and introduce a min pooling operation to test the implementation. The patch is a sibling of the max operation patch https://reviews.llvm.org/D105203 and the min operation is again lowered to a compare and select pair.
Differential Revision: https://reviews.llvm.org/D105345
Add the max operation to the OpDSL and introduce a max pooling operation to test the implementation. As MLIR has no builtin max operation, the max function is lowered to a compare and select pair.
Differential Revision: https://reviews.llvm.org/D105203
Similarly to batch_mat vec outer most dim is a batching dim
and this op does |b| matrix-vector-products :
C[b, i] = sum_k(A[b, i, k] * B[b, k])
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D104739
Extend the OpDSL syntax with an optional `domain` function to specify an explicit dimension order. The extension is needed to provide more control over the dimension order instead of deducing it implicitly depending on the formulation of the tensor comprehension. Additionally, the patch also ensures the symbols are ordered according to the operand definitions of the operation.
Differential Revision: https://reviews.llvm.org/D105117
Add an index_dim annotation to specify the shape to loop mapping of shape-only tensors. A shape-only tensor serves is not accessed withing the body of the operation but is required to span the iteration space of certain operations such as pooling.
Differential Revision: https://reviews.llvm.org/D104767
Extend the OpDSL with index attributes. After tensors and scalars, index attributes are the third operand type. An index attribute represents a compile-time constant that is limited to index expressions. A use cases are the strides and dilations defined by convolution and pooling operations.
The patch only updates the OpDSL. The C++ yaml codegen is updated by a followup patch.
Differential Revision: https://reviews.llvm.org/D104711
Adapt the FillOp definition to use a scalar operand instead of a capture. This patch is a follow up to https://reviews.llvm.org/D104109. As the input operands are in front of the output operands the patch changes the internal operand order of the FillOp. The pretty printed version of the operation remains unchanged though. The patch also adapts the linalg to standard lowering to ensure the c signature of the FillOp remains unchanged as well.
Differential Revision: https://reviews.llvm.org/D104121
The patch replaces the existing capture functionality by scalar operands that have been introduced by https://reviews.llvm.org/D104109. Scalar operands behave as tensor operands except for the fact that they are not indexed. As a result ScalarDefs can be accessed directly as no indexing expression is needed.
The patch only updates the OpDSL. The C++ side is updated by a follow up patch.
Differential Revision: https://reviews.llvm.org/D104220
Currently, passes are registered on a per-dialect basis, which
provides the smallest footprint obviously. But for prototyping
and experimentation, a convenience "all passes" module is provided,
which registers all known MLIR passes in one run.
Usage in Python:
import mlir.all_passes_registration
Reviewed By: mehdi_amini
Differential Revision: https://reviews.llvm.org/D103130
The patch extends the yaml code generation to support the following new OpDSL constructs:
- captures
- constants
- iteration index accesses
- predefined types
These changes have been introduced by revision
https://reviews.llvm.org/D101364.
Differential Revision: https://reviews.llvm.org/D102075
First set of "boilerplate" to get sparse tensor
passes available through CAPI and Python.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D102362
All glue and clutter in the linalg ops has been replaced by proper
sparse tensor type encoding. This code is no longer needed. Thanks
to ntv@ for giving us a temporary home in linalg.
So long, and thanks for all the fish.
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D102098
* NFC but has some fixes for CMake glitches discovered along the way (things not cleaning properly, co-mingled depends).
* Includes previously unsubmitted fix in D98681 and a TODO to fix it more appropriately in a smaller followup.
Differential Revision: https://reviews.llvm.org/D101493
Rob Suderman