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141 changes: 141 additions & 0 deletions
141
lib/Dialect/TorchConversion/Transforms/UnpackTensor.cpp
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| //===----------------------------------------------------------------------===// | ||
| // | ||
| // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. | ||
| // See https://llvm.org/LICENSE.txt for license information. | ||
| // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception | ||
| // Also available under a BSD-style license. See LICENSE. | ||
| // | ||
| //===----------------------------------------------------------------------===// | ||
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| #include "PassDetail.h" | ||
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| #include "mlir/Transforms/DialectConversion.h" | ||
| #include "mlir/Transforms/GreedyPatternRewriteDriver.h" | ||
| #include "torch-mlir/Dialect/Torch/IR/TorchDialect.h" | ||
| #include "torch-mlir/Dialect/Torch/IR/TorchOps.h" | ||
| #include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.h" | ||
| #include "torch-mlir/Dialect/TorchConversion/Transforms/Passes.h" | ||
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| using namespace mlir; | ||
| using namespace mlir::torch; | ||
| using namespace mlir::torch::Torch; | ||
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| namespace { | ||
| class UnpackQuantizedMatmulWeights | ||
| : public OpRewritePattern<ValueTensorLiteralOp> { | ||
| public: | ||
| using OpRewritePattern::OpRewritePattern; | ||
| LogicalResult matchAndRewrite(ValueTensorLiteralOp constOp, | ||
| PatternRewriter &rewriter) const override { | ||
| if (!constOp->hasOneUse()) | ||
| return failure(); | ||
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| OpOperand *use = constOp.getResult().use_begin().getOperand(); | ||
| auto op = dyn_cast<OperatorOp>(use->getOwner()); | ||
| if (!op) | ||
| return failure(); | ||
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| if (use->getOperandNumber() != 1) | ||
| return failure(); | ||
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| if (op.getName().str() != "brevitas.matmul_rhs_group_quant") { | ||
| return failure(); | ||
| } | ||
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| Value rhs = op.getOperand(1); | ||
| Value bitWidth = op.getOperand(4); | ||
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| auto getConstantIntegerFromDefiningOp = [](Value operand, | ||
| int &extractedInt) { | ||
| auto constOp = dyn_cast<Torch::ConstantIntOp>(operand.getDefiningOp()); | ||
| if (!constOp) { | ||
| return failure(); | ||
| } | ||
| extractedInt = constOp.getValue(); | ||
| return success(); | ||
| }; | ||
| int unpackedBitWidth; | ||
| if (failed(getConstantIntegerFromDefiningOp(bitWidth, unpackedBitWidth))) | ||
| return failure(); | ||
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| auto rhsType = rhs.getType().dyn_cast<ValueTensorType>(); | ||
| if (!rhsType) | ||
| return failure(); | ||
|
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| if (!rhsType.hasDtype()) | ||
| return failure(); | ||
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| Type dType = rhsType.getDtype(); | ||
| int dTypeWidth = dType.getIntOrFloatBitWidth(); | ||
| if (dTypeWidth == unpackedBitWidth) | ||
| return failure(); | ||
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| if (!rhsType.hasSizes()) | ||
| return failure(); | ||
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| SmallVector<int64_t> tensorShape(rhsType.getSizes()); | ||
| if (tensorShape.back() == kUnknownSize) | ||
| return failure(); | ||
| int packRatio = dTypeWidth / unpackedBitWidth; | ||
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| tensorShape[tensorShape.size() - 1] *= packRatio; | ||
| Type unpackedElementType; | ||
| if (dType.isSignedInteger()) | ||
| unpackedElementType = rewriter.getIntegerType(unpackedBitWidth, true); | ||
| else | ||
| unpackedElementType = rewriter.getIntegerType(unpackedBitWidth, false); | ||
| ValueTensorType newRhsType = ValueTensorType::get( | ||
| rewriter.getContext(), tensorShape, unpackedElementType); | ||
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| auto elements = constOp.getValueAttr().dyn_cast<DenseIntElementsAttr>(); | ||
| if (!elements) | ||
| return failure(); | ||
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| auto attrType = RankedTensorType::get(tensorShape, unpackedElementType); | ||
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| // This is terrible but idk what else to do. | ||
| auto data = elements.getRawData(); | ||
| std::vector<APInt> newData(data.size() * packRatio, | ||
| APInt(unpackedBitWidth, 0)); | ||
| for (int i = 0, e = data.size(); i < e; ++i) { | ||
| auto el = data[i]; | ||
| char mask = (1 << unpackedBitWidth) - 1; | ||
| for (int b = 0; b < packRatio; b++) { | ||
| newData[i * packRatio + b] = | ||
| APInt(unpackedBitWidth, (el & mask) >> (unpackedBitWidth * b)); | ||
| mask = mask << unpackedBitWidth; | ||
| } | ||
| } | ||
| rewriter.replaceOpWithNewOp<ValueTensorLiteralOp>( | ||
| constOp, newRhsType, | ||
| DenseElementsAttr::get(attrType, ArrayRef<APInt>(newData))); | ||
| return success(); | ||
| } | ||
| }; | ||
| } // namespace | ||
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| namespace { | ||
| class UnpackTorchTensorPass | ||
| : public TorchConversion::UnpackTorchTensorBase<UnpackTorchTensorPass> { | ||
| using UnpackTorchTensorBase<UnpackTorchTensorPass>::UnpackTorchTensorBase; | ||
| void getDependentDialects(DialectRegistry ®istry) const override { | ||
| registry.insert<func::FuncDialect>(); | ||
| registry.insert<Torch::TorchDialect>(); | ||
| } | ||
|
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| void runOnOperation() override { | ||
| MLIRContext *context = &getContext(); | ||
| RewritePatternSet patterns(context); | ||
| patterns.add<UnpackQuantizedMatmulWeights>(context); | ||
|
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| if (failed( | ||
| applyPatternsAndFoldGreedily(getOperation(), std::move(patterns)))) | ||
| signalPassFailure(); | ||
| } | ||
| }; | ||
| } // namespace | ||
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| std::unique_ptr<OperationPass<func::FuncOp>> | ||
| mlir::torch::TorchConversion::createUnpackTorchTensorPass() { | ||
| return std::make_unique<UnpackTorchTensorPass>(); | ||
| } |