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45577fb79e
This commit rebases the compiler onto commit f69328049e9e from llvm-project. Changes: * Use of the one-shot bufferizer for improved memory management * A new pass `OneShotBufferizeDPSWrapper` that converts functions returning tensors to destination-passing-style as required by the one-shot bufferizer * A new pass `LinalgGenericOpWithTensorsToLoopsPass` that converts `linalg.generic` operations with value semantics to loop nests * Rebase onto a fork of llvm-project at f69328049e9e with local modifications to enable bufferization of `linalg.generic` operations with value semantics * Workaround for the absence of type propagation after type conversion via extra patterns in all dialect conversion passes * Printer, parser and verifier definitions moved from inline declarations in ODS to the respective source files as required by upstream changes * New tests for functions with a large number of inputs * Increase the number of allowed task inputs as required by new tests * Use upstream function `mlir_configure_python_dev_packages()` to locate Python development files for compatibility with various CMake versions Co-authored-by: Quentin Bourgerie <quentin.bourgerie@zama.ai> Co-authored-by: Ayoub Benaissa <ayoub.benaissa@zama.ai> Co-authored-by: Antoniu Pop <antoniu.pop@zama.ai>
76 lines
2.7 KiB
C++
76 lines
2.7 KiB
C++
// Part of the Concrete Compiler Project, under the BSD3 License with Zama
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// Exceptions. See
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// https://github.com/zama-ai/concrete-compiler-internal/blob/main/LICENSE.txt
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// for license information.
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#include "concretelang/Transforms/Bufferize.h"
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#include "mlir/Dialect/Bufferization/Transforms/Bufferize.h"
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#include "mlir/Dialect/MemRef/IR/MemRef.h"
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#include "mlir/IR/Operation.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include "mlir/Transforms/Passes.h"
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using namespace mlir;
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namespace {
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// In a finalizing bufferize conversion, we know that all tensors have been
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// converted to memrefs, thus, this op becomes an identity.
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class BufferizeTensorStoreOp
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: public OpConversionPattern<memref::TensorStoreOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(memref::TensorStoreOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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rewriter.replaceOpWithNewOp<memref::CopyOp>(op, op.tensor(), op.memref());
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return success();
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}
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};
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} // namespace
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void populatePatterns(bufferization::BufferizeTypeConverter &typeConverter,
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RewritePatternSet &patterns) {
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bufferization::populateEliminateBufferizeMaterializationsPatterns(
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typeConverter, patterns);
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patterns.add<BufferizeTensorStoreOp>(typeConverter, patterns.getContext());
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}
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namespace {
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struct FinalizingBufferizePass
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: public FinalizingBufferizeBase<FinalizingBufferizePass> {
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using FinalizingBufferizeBase<
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FinalizingBufferizePass>::FinalizingBufferizeBase;
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void runOnOperation() override {
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auto func = getOperation();
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auto *context = &getContext();
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bufferization::BufferizeTypeConverter typeConverter;
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RewritePatternSet patterns(context);
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ConversionTarget target(*context);
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populatePatterns(typeConverter, patterns);
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// If all result types are legal, and all block arguments are legal (ensured
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// by func conversion above), then all types in the program are legal.
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//
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// We also check that the operand types are legal to avoid creating invalid
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// IR. For example, this prevents
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// populateEliminateBufferizeMaterializationsPatterns from updating the
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// types of the operands to a return op without updating the enclosing
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// function.
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target.markUnknownOpDynamicallyLegal(
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[&](Operation *op) { return typeConverter.isLegal(op); });
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target.addLegalOp<memref::CopyOp>();
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if (failed(applyFullConversion(func, target, std::move(patterns))))
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signalPassFailure();
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}
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};
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} // namespace
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std::unique_ptr<OperationPass<func::FuncOp>>
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mlir::concretelang::createFinalizingBufferizePass() {
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return std::make_unique<FinalizingBufferizePass>();
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}
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