mirror of https://github.com/llvm/torch-mlir
216 lines
10 KiB
C++
216 lines
10 KiB
C++
//===----------------------------------------------------------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "npcomp/Conversion/TCFToLinalg/TCFToLinalg.h"
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#include "../PassDetail.h"
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#include "mlir/Dialect/Linalg/IR/LinalgOps.h"
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#include "mlir/Dialect/Shape/IR/Shape.h"
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#include "mlir/Dialect/StandardOps/IR/Ops.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/Dialect/Traits.h"
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// TODO: Remove when memref.dim is split into tensor.dim for the tensor case.
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#include "mlir/Dialect/MemRef/IR/MemRef.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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#include "npcomp/Dialect/TCF/IR/TCFOps.h"
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#include "npcomp/Dialect/TCP/IR/TCPDialect.h"
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#include "npcomp/Dialect/TCP/IR/TCPOps.h"
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using namespace mlir;
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using namespace mlir::NPCOMP;
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static SmallVector<Value, 6> bypassResultShapes(Operation *op,
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OpBuilder &builder) {
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if (auto matmul = dyn_cast<tcf::MatmulOp>(op)) {
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auto lhsRows = builder.create<memref::DimOp>(op->getLoc(), matmul.lhs(), 0);
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auto rhsCols = builder.create<memref::DimOp>(op->getLoc(), matmul.rhs(), 1);
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auto shape = builder.create<tensor::FromElementsOp>(
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op->getLoc(), ValueRange({lhsRows, rhsCols}));
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return {shape};
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}
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// TODO: This only supports the NCHW data format. Consider other formats and lower ranks.
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if (auto conv2dNCHW = dyn_cast<tcf::ConvNCHWOp>(op)) {
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// TODO: Replace hard-coded stride/dilation/padding constant-ops.
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// 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.
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auto cI0 = builder.create<ConstantOp>(op->getLoc(), builder.getIntegerAttr(builder.getIndexType(), 0));
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auto cI1 = builder.create<ConstantOp>(op->getLoc(), builder.getIntegerAttr(builder.getIndexType(), 1));
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auto cI2 = builder.create<ConstantOp>(op->getLoc(), builder.getIntegerAttr(builder.getIndexType(), 2));
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auto stride = cI1;
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auto dilation = cI1;
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auto padding = cI0;
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auto strideHeight = stride;
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auto strideWidth = stride;
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auto dilationHeight = dilation;
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auto dilationWidth = dilation;
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auto paddingHeight = padding;
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auto paddingWidth = padding;
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auto batch =
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builder.create<memref::DimOp>(op->getLoc(), conv2dNCHW.in(), 0);
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auto height =
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builder.create<memref::DimOp>(op->getLoc(), conv2dNCHW.in(), 2);
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auto width =
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builder.create<memref::DimOp>(op->getLoc(), conv2dNCHW.in(), 3);
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auto filterOutChannels =
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builder.create<memref::DimOp>(op->getLoc(), conv2dNCHW.filter(), 0);
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auto filterHeight =
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builder.create<memref::DimOp>(op->getLoc(), conv2dNCHW.filter(), 2);
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auto filterWidth =
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builder.create<memref::DimOp>(op->getLoc(), conv2dNCHW.filter(), 3);
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// Output height
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auto twicePaddingHeight = builder.create<MulIOp>(op->getLoc(), paddingHeight, cI2);
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auto heightPlusTwicePadding = builder.create<SubIOp>(op->getLoc(), height, twicePaddingHeight);
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auto filterHeightMinusOne = builder.create<SubIOp>(op->getLoc(), filterHeight, cI1);
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auto dilationFilterHeight = builder.create<MulIOp>(op->getLoc(), dilationHeight, filterHeightMinusOne);
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auto outHeightUnstridedPlusOne = builder.create<SubIOp>(op->getLoc(), heightPlusTwicePadding, dilationFilterHeight);
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auto outHeightUnstrided = builder.create<SubIOp>(op->getLoc(), outHeightUnstridedPlusOne, cI1);
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auto outHeightMinusOne = builder.create<UnsignedDivIOp>(op->getLoc(), outHeightUnstrided, strideHeight);
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auto outHeight = builder.create<AddIOp>(op->getLoc(), outHeightMinusOne, cI1);
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// Output width
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auto twicePaddingWidth = builder.create<MulIOp>(op->getLoc(), paddingWidth, cI2);
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auto widthPlusTwicePadding = builder.create<SubIOp>(op->getLoc(), width, twicePaddingWidth);
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auto filterWidthMinusOne = builder.create<SubIOp>(op->getLoc(), filterWidth, cI1);
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auto dilationFilterWidth = builder.create<MulIOp>(op->getLoc(), dilationWidth, filterWidthMinusOne);
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auto outWidthUnstridedPlusOne = builder.create<SubIOp>(op->getLoc(), widthPlusTwicePadding, dilationFilterWidth);
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auto outWidthUnstrided = builder.create<SubIOp>(op->getLoc(), outWidthUnstridedPlusOne, cI1);
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auto outWidthMinusOne = builder.create<UnsignedDivIOp>(op->getLoc(), outWidthUnstrided, strideWidth);
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auto outWidth = builder.create<AddIOp>(op->getLoc(), outWidthMinusOne, cI1);
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// Output shape
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auto shape = builder.create<tensor::FromElementsOp>(
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op->getLoc(),
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ValueRange({batch, filterOutChannels, outHeight, outWidth}));
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return {shape};
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}
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// No shape transfer function.
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return {};
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}
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namespace {
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class ConvertMatmul : public OpRewritePattern<tcf::MatmulOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(tcf::MatmulOp op,
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PatternRewriter &rewriter) const override {
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// Create the constraints, and the assuming region.
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Value lhsK = rewriter.create<memref::DimOp>(op.getLoc(), op.lhs(), 1);
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Value rhsK = rewriter.create<memref::DimOp>(op.getLoc(), op.rhs(), 0);
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Value matchingK =
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rewriter.create<CmpIOp>(op.getLoc(), CmpIPredicate::eq, lhsK, rhsK);
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Value witness = rewriter.create<shape::CstrRequireOp>(
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op.getLoc(), matchingK, "mismatching contracting dimension for matmul");
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auto assuming = rewriter.create<shape::AssumingOp>(
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op.getLoc(), ArrayRef<Type>{op.getType()}, witness);
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// Build the region body.
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rewriter.createBlock(&assuming.doRegion());
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// Create the init tensor for the matmul.
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// TODO: Expand supported data types.
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Value c0 =
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rewriter.create<ConstantOp>(op.getLoc(), rewriter.getF32FloatAttr(0.0));
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Value shape = bypassResultShapes(op, rewriter)[0];
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Value initTensor =
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rewriter.create<tcp::SplattedOp>(op.getLoc(), op.getType(), c0, shape);
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// Create the matmul.
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auto matmul = rewriter.create<linalg::MatmulOp>(
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op.getLoc(), TypeRange(op.getType()), op.getOperands(),
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ValueRange(initTensor));
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rewriter.create<shape::AssumingYieldOp>(op.getLoc(), matmul.getResult(0));
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// Finally, replace with the results of the shape.assuming
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rewriter.replaceOp(op, assuming.getResults());
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return success();
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}
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};
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} // namespace
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namespace {
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class ConvertConvNCHW : public OpRewritePattern<tcf::ConvNCHWOp> {
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public:
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using OpRewritePattern::OpRewritePattern;
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LogicalResult matchAndRewrite(tcf::ConvNCHWOp op,
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PatternRewriter &rewriter) const override {
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// Create the constraints, and the assuming region.
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Value inputCin = rewriter.create<memref::DimOp>(op.getLoc(), op.in(), 1);
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Value inputH = rewriter.create<memref::DimOp>(op.getLoc(), op.in(), 2);
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Value inputW = rewriter.create<memref::DimOp>(op.getLoc(), op.in(), 3);
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Value filterCin =
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rewriter.create<memref::DimOp>(op.getLoc(), op.filter(), 1);
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Value filterKH =
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rewriter.create<memref::DimOp>(op.getLoc(), op.filter(), 2);
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Value filterKW =
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rewriter.create<memref::DimOp>(op.getLoc(), op.filter(), 3);
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Value matchingCin =
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rewriter.create<CmpIOp>(op.getLoc(), CmpIPredicate::eq, inputCin, filterCin);
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Value validFilterH =
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rewriter.create<CmpIOp>(op.getLoc(), CmpIPredicate::uge, inputH, filterKH);
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Value validFilterW =
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rewriter.create<CmpIOp>(op.getLoc(), CmpIPredicate::uge, inputW, filterKW);
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Value witnessCin = rewriter.create<shape::CstrRequireOp>(
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op.getLoc(), matchingCin, "input and filter in-channels must be equal");
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Value witnessFilterH = rewriter.create<shape::CstrRequireOp>(
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op.getLoc(), validFilterH, "input height must be greater than or equal to filter KH-dimension");
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Value witnessFilterW = rewriter.create<shape::CstrRequireOp>(
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op.getLoc(), validFilterW, "input width must be greater than or equal to filter KW-dimension");
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Value assumingAll = rewriter.create<shape::AssumingAllOp>(
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op.getLoc(), witnessCin.getType(), ValueRange({witnessCin, witnessFilterH, witnessFilterW}));
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auto assuming = rewriter.create<shape::AssumingOp>(
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op.getLoc(), ArrayRef<Type>{op.getType()}, assumingAll);
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// Build the region body.
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rewriter.createBlock(&assuming.doRegion());
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// Create the init tensor for the ConvNCHW.
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// TODO: Expand supported data types.
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Value c0 =
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rewriter.create<ConstantOp>(op.getLoc(), rewriter.getF32FloatAttr(0.0));
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Value shape = bypassResultShapes(op, rewriter)[0];
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Value initTensor =
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rewriter.create<tcp::SplattedOp>(op.getLoc(), op.getType(), c0, shape);
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// Create the ConvNCHW.
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auto conv2dNCHW = rewriter.create<linalg::ConvNCHWOp>(
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op.getLoc(), TypeRange(op.getType()),
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ValueRange({op.in(), op.filter()}), ValueRange(initTensor));
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rewriter.create<shape::AssumingYieldOp>(op.getLoc(), conv2dNCHW.getResults());
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// Finally, replace with the results of the shape.assuming
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rewriter.replaceOp(op, assuming.getResults());
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return success();
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}
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};
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} // namespace
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namespace {
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class ConvertTCFToLinalg : public ConvertTCFToLinalgBase<ConvertTCFToLinalg> {
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public:
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void getDependentDialects(DialectRegistry ®istry) const override {
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registry.insert<shape::ShapeDialect, tcp::TCPDialect, tensor::TensorDialect,
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memref::MemRefDialect, linalg::LinalgDialect>();
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}
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void runOnOperation() override {
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(void)applyPatternsAndFoldGreedily(getOperation(), getPatterns());
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}
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FrozenRewritePatternSet getPatterns() {
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MLIRContext *context = &getContext();
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RewritePatternSet patterns(context);
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patterns.add<ConvertMatmul>(context);
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patterns.add<ConvertConvNCHW>(context);
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return std::move(patterns);
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}
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};
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} // namespace
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std::unique_ptr<OperationPass<FuncOp>>
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mlir::NPCOMP::createConvertTCFToLinalgPass() {
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return std::make_unique<ConvertTCFToLinalg>();
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}
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