torch-mlir/lib/Conversion/TCFToLinalg/TCFToLinalg.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
//
//===----------------------------------------------------------------------===//
#include "npcomp/Conversion/TCFToLinalg/TCFToLinalg.h"
#include "../PassDetail.h"
#include "mlir/Dialect/Linalg/IR/LinalgOps.h"
#include "mlir/Dialect/Shape/IR/Shape.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Traits.h"
#include "mlir/Transforms/DialectConversion.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "npcomp/Dialect/TCF/IR/TCFOps.h"
#include "npcomp/Dialect/TCP/IR/TCPDialect.h"
#include "npcomp/Dialect/TCP/IR/TCPOps.h"
using namespace mlir;
using namespace mlir::NPCOMP;
static SmallVector<Value, 6> bypassResultShapes(Operation *op,
OpBuilder &builder) {
if (auto matmul = dyn_cast<tcf::MatmulOp>(op)) {
auto lhsRows = builder.create<DimOp>(op->getLoc(), matmul.lhs(), 0);
auto rhsCols = builder.create<DimOp>(op->getLoc(), matmul.rhs(), 1);
auto shape = builder.create<tensor::FromElementsOp>(
op->getLoc(), ValueRange({lhsRows, rhsCols}));
return {shape};
}
// TODO: This only supports the NCHW data format. Consider other formats and lower ranks.
if (auto conv2dNCHW = dyn_cast<tcf::ConvNCHWOp>(op)) {
// TODO: Replace hard-coded stride/dilation/padding constant-ops.
// TODO: Consider migrating this SSA shape-computing graph to a complex op or use the `mlir-linalg-ods-gen` approach and define a `*.tc` spec file.
auto cI0 = builder.create<ConstantOp>(op->getLoc(), builder.getIntegerAttr(builder.getIndexType(), 0));
auto cI1 = builder.create<ConstantOp>(op->getLoc(), builder.getIntegerAttr(builder.getIndexType(), 1));
auto cI2 = builder.create<ConstantOp>(op->getLoc(), builder.getIntegerAttr(builder.getIndexType(), 2));
auto stride = cI1;
auto dilation = cI1;
auto padding = cI0;
auto strideHeight = stride;
auto strideWidth = stride;
auto dilationHeight = dilation;
auto dilationWidth = dilation;
auto paddingHeight = padding;
auto paddingWidth = padding;
auto batch = builder.create<DimOp>(op->getLoc(), conv2dNCHW.in(), 0);
auto height = builder.create<DimOp>(op->getLoc(), conv2dNCHW.in(), 2);
auto width = builder.create<DimOp>(op->getLoc(), conv2dNCHW.in(), 3);
auto filterOutChannels = builder.create<DimOp>(op->getLoc(), conv2dNCHW.filter(), 0);
auto filterHeight = builder.create<DimOp>(op->getLoc(), conv2dNCHW.filter(), 2);
auto filterWidth = builder.create<DimOp>(op->getLoc(), conv2dNCHW.filter(), 3);
// Output height
auto twicePaddingHeight = builder.create<MulIOp>(op->getLoc(), paddingHeight, cI2);
auto heightPlusTwicePadding = builder.create<SubIOp>(op->getLoc(), height, twicePaddingHeight);
auto filterHeightMinusOne = builder.create<SubIOp>(op->getLoc(), filterHeight, cI1);
auto dilationFilterHeight = builder.create<MulIOp>(op->getLoc(), dilationHeight, filterHeightMinusOne);
auto outHeightUnstridedPlusOne = builder.create<SubIOp>(op->getLoc(), heightPlusTwicePadding, dilationFilterHeight);
auto outHeightUnstrided = builder.create<SubIOp>(op->getLoc(), outHeightUnstridedPlusOne, cI1);
auto outHeightMinusOne = builder.create<UnsignedDivIOp>(op->getLoc(), outHeightUnstrided, strideHeight);
auto outHeight = builder.create<AddIOp>(op->getLoc(), outHeightMinusOne, cI1);
// Output width
auto twicePaddingWidth = builder.create<MulIOp>(op->getLoc(), paddingWidth, cI2);
auto widthPlusTwicePadding = builder.create<SubIOp>(op->getLoc(), width, twicePaddingWidth);
auto filterWidthMinusOne = builder.create<SubIOp>(op->getLoc(), filterWidth, cI1);
auto dilationFilterWidth = builder.create<MulIOp>(op->getLoc(), dilationWidth, filterWidthMinusOne);
auto outWidthUnstridedPlusOne = builder.create<SubIOp>(op->getLoc(), widthPlusTwicePadding, dilationFilterWidth);
auto outWidthUnstrided = builder.create<SubIOp>(op->getLoc(), outWidthUnstridedPlusOne, cI1);
auto outWidthMinusOne = builder.create<UnsignedDivIOp>(op->getLoc(), outWidthUnstrided, strideWidth);
auto outWidth = builder.create<AddIOp>(op->getLoc(), outWidthMinusOne, cI1);
// Output shape
auto shape = builder.create<tensor::FromElementsOp>(
op->getLoc(),
ValueRange({batch, filterOutChannels, outHeight, outWidth}));
return {shape};
}
// No shape transfer function.
return {};
}
namespace {
class ConvertMatmul : public OpRewritePattern<tcf::MatmulOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(tcf::MatmulOp op,
PatternRewriter &rewriter) const override {
// Create the constraints, and the assuming region.
Value lhsK = rewriter.create<DimOp>(op.getLoc(), op.lhs(), 1);
Value rhsK = rewriter.create<DimOp>(op.getLoc(), op.rhs(), 0);
Value matchingK =
rewriter.create<CmpIOp>(op.getLoc(), CmpIPredicate::eq, lhsK, rhsK);
Value witness = rewriter.create<shape::CstrRequireOp>(
op.getLoc(), matchingK, "mismatching contracting dimension for matmul");
auto assuming = rewriter.create<shape::AssumingOp>(
op.getLoc(), ArrayRef<Type>{op.getType()}, witness);
// Build the region body.
rewriter.createBlock(&assuming.doRegion());
// Create the init tensor for the matmul.
// TODO: Expand supported data types.
Value c0 =
rewriter.create<ConstantOp>(op.getLoc(), rewriter.getF32FloatAttr(0.0));
Value shape = bypassResultShapes(op, rewriter)[0];
Value initTensor =
rewriter.create<tcp::SplattedOp>(op.getLoc(), op.getType(), c0, shape);
// Create the matmul.
auto matmul = rewriter.create<linalg::MatmulOp>(
op.getLoc(), TypeRange(op.getType()), op.getOperands(),
ValueRange(initTensor));
rewriter.create<shape::AssumingYieldOp>(op.getLoc(), matmul.getResult(0));
// Finally, replace with the results of the shape.assuming
rewriter.replaceOp(op, assuming.getResults());
return success();
}
};
} // namespace
namespace {
class ConvertConvNCHW : public OpRewritePattern<tcf::ConvNCHWOp> {
public:
using OpRewritePattern::OpRewritePattern;
LogicalResult matchAndRewrite(tcf::ConvNCHWOp op,
PatternRewriter &rewriter) const override {
// Create the constraints, and the assuming region.
Value inputCin = rewriter.create<DimOp>(op.getLoc(), op.in(), 1);
Value inputH = rewriter.create<DimOp>(op.getLoc(), op.in(), 2);
Value inputW = rewriter.create<DimOp>(op.getLoc(), op.in(), 3);
Value filterCin = rewriter.create<DimOp>(op.getLoc(), op.filter(), 1);
Value filterKH = rewriter.create<DimOp>(op.getLoc(), op.filter(), 2);
Value filterKW = rewriter.create<DimOp>(op.getLoc(), op.filter(), 3);
Value matchingCin =
rewriter.create<CmpIOp>(op.getLoc(), CmpIPredicate::eq, inputCin, filterCin);
Value validFilterH =
rewriter.create<CmpIOp>(op.getLoc(), CmpIPredicate::uge, inputH, filterKH);
Value validFilterW =
rewriter.create<CmpIOp>(op.getLoc(), CmpIPredicate::uge, inputW, filterKW);
Value witnessCin = rewriter.create<shape::CstrRequireOp>(
op.getLoc(), matchingCin, "input and filter in-channels must be equal");
Value witnessFilterH = rewriter.create<shape::CstrRequireOp>(
op.getLoc(), validFilterH, "input height must be greater than or equal to filter KH-dimension");
Value witnessFilterW = rewriter.create<shape::CstrRequireOp>(
op.getLoc(), validFilterW, "input width must be greater than or equal to filter KW-dimension");
Value assumingAll = rewriter.create<shape::AssumingAllOp>(
op.getLoc(), witnessCin.getType(), ValueRange({witnessCin, witnessFilterH, witnessFilterW}));
auto assuming = rewriter.create<shape::AssumingOp>(
op.getLoc(), ArrayRef<Type>{op.getType()}, assumingAll);
// Build the region body.
rewriter.createBlock(&assuming.doRegion());
// Create the init tensor for the ConvNCHW.
// TODO: Expand supported data types.
Value c0 =
rewriter.create<ConstantOp>(op.getLoc(), rewriter.getF32FloatAttr(0.0));
Value shape = bypassResultShapes(op, rewriter)[0];
Value initTensor =
rewriter.create<tcp::SplattedOp>(op.getLoc(), op.getType(), c0, shape);
// Create the ConvNCHW.
auto conv2dNCHW = rewriter.create<linalg::ConvNCHWOp>(
op.getLoc(), TypeRange(op.getType()),
ValueRange({op.in(), op.filter()}), ValueRange(initTensor));
rewriter.create<shape::AssumingYieldOp>(op.getLoc(), conv2dNCHW.getResults());
// Finally, replace with the results of the shape.assuming
rewriter.replaceOp(op, assuming.getResults());
return success();
}
};
} // namespace
namespace {
class ConvertTCFToLinalg : public ConvertTCFToLinalgBase<ConvertTCFToLinalg> {
public:
void getDependentDialects(DialectRegistry &registry) const override {
registry
.insert<shape::ShapeDialect, tcp::TCPDialect, tensor::TensorDialect>();
}
void runOnOperation() override {
(void)applyPatternsAndFoldGreedily(getOperation(), getPatterns());
}
FrozenRewritePatternList getPatterns() {
MLIRContext *context = &getContext();
OwningRewritePatternList patterns;
patterns.insert<ConvertMatmul>(context);
patterns.insert<ConvertConvNCHW>(context);
return std::move(patterns);
}
};
} // namespace
std::unique_ptr<OperationPass<FuncOp>>
mlir::NPCOMP::createConvertTCFToLinalgPass() {
return std::make_unique<ConvertTCFToLinalg>();
}