mirror of https://github.com/llvm/torch-mlir
272 lines
10 KiB
C++
272 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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// Also available under a BSD-style license. See LICENSE.
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//
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//===----------------------------------------------------------------------===//
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#include "torch-mlir/Conversion/TorchToMhlo/TorchToMhlo.h"
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#include "../PassDetail.h"
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#include "./MhloLegalizeUtils.h"
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#include "./PopulatePatterns.h"
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#include "mhlo/IR/hlo_ops.h"
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#include "mlir/Dialect/Arith/IR/Arith.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "torch-mlir/Conversion/Utils/Utils.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
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#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
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#include "torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.h"
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using namespace mlir;
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using namespace mlir::torch;
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using namespace mlir::torch::Torch;
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using namespace mlir::torch::torch_to_mhlo;
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namespace {
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Value gatherTensorAlongSingleAxis(PatternRewriter &rewriter, Operation *op,
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Value input, Value indices, int64_t axis,
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size_t dimSizeIndexBits) {
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auto loc = op->getLoc();
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Type intType = rewriter.getIntegerType(dimSizeIndexBits);
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Value one = rewriter.create<arith::ConstantOp>(
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loc, rewriter.getIntegerAttr(intType, 1));
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// sliceSizes
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auto inputRankTy = input.getType().dyn_cast<RankedTensorType>();
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auto inputRank = inputRankTy.getRank();
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SmallVector<Value, 4> sliceSizes;
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sliceSizes.reserve(inputRank);
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for (int64_t r = 0; r < inputRank; ++r) {
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if (r == axis) {
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sliceSizes.push_back(one);
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} else {
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sliceSizes.push_back(rewriter.create<arith::IndexCastOp>(
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loc, intType, rewriter.create<tensor::DimOp>(loc, input, r)));
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}
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}
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auto sliceSizesTensor =
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rewriter.create<tensor::FromElementsOp>(loc, sliceSizes);
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// offsetDims
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SmallVector<int64_t, 4> offsetDims;
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offsetDims.reserve(inputRank);
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for (int64_t r = 0; r < axis; ++r) {
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offsetDims.push_back(r);
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}
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auto indicesRankTy = indices.getType().dyn_cast<RankedTensorType>();
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auto indicesRank = indicesRankTy.getRank();
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for (int64_t r = axis + 1; r < inputRank; ++r) {
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offsetDims.push_back(r + indicesRank - 1);
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}
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// collapsedSliceDims
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SmallVector<int64_t, 4> collapsedSliceDims(1, axis);
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// startIndexMap
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SmallVector<int64_t, 4> startIndexMap(1, axis);
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// indexVecDim
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int64_t indexVecDim = indicesRank;
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auto dimsAttr = mhlo::GatherDimensionNumbersAttr::get(
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rewriter.getContext(),
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/*offsetDims=*/offsetDims,
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/*collapsedSliceDims=*/collapsedSliceDims,
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/*startIndexMap=*/startIndexMap,
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/*indexVecDim=*/indexVecDim);
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// outputShape = input.shape[:axis] + indices.shape +
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// input.shape[axis + 1:]
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auto inputShape = inputRankTy.getShape();
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auto indicesShape = indicesRankTy.getShape();
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SmallVector<int64_t, 4> outputShape(inputShape.begin(),
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inputShape.begin() + axis);
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outputShape.insert(outputShape.end(), indicesShape.begin(),
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indicesShape.end());
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outputShape.insert(outputShape.end(), inputShape.begin() + axis + 1,
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inputShape.end());
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// create output tensor type
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auto outputTy =
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RankedTensorType::get(outputShape, inputRankTy.getElementType());
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return rewriter
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.create<mhlo::DynamicGatherOp>(loc, outputTy, input, indices,
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sliceSizesTensor, dimsAttr)
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.getResult();
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}
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} // namespace
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// Ref: https://pytorch.org/docs/stable/generated/torch.nn.functional.embedding.html
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// padding_idx (int, optional)
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// – If specified, the entries at padding_idx do not contribute to the gradient;
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// therefore, the embedding vector at padding_idx is not updated during training,
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// i.e. it remains as a fixed “pad”.
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// scale_grad_by_freq (boolean, optional)
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// – If given, this will scale gradients by the inverse of frequency of the
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// words in the mini-batch. Default False.
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// sparse (bool, optional)
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// – If True, gradient w.r.t. weight matrix will be a sparse tensor.
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template <>
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LogicalResult ConvertAtenOp<AtenEmbeddingOp>::matchAndRewrite(
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AtenEmbeddingOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const {
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auto weight = adaptor.getWeight();
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auto weightTy = weight.getType().cast<RankedTensorType>();
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if (!weightTy)
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return op.emitError("only ranked tensor types are supported");
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int64_t padding_idx;
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if (!matchPattern(op.getPaddingIdx(), m_TorchConstantInt(&padding_idx)))
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return rewriter.notifyMatchFailure(
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op, "only constant padding_idx is currently supported");
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bool scale_grad_by_freq;
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if (!matchPattern(op.getScaleGradByFreq(),
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m_TorchConstantBool(&scale_grad_by_freq)))
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return rewriter.notifyMatchFailure(
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op, "only constant scale_grad_by_freq is currently supported");
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if (scale_grad_by_freq)
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return rewriter.notifyMatchFailure(
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op, "scale gradients is currently not supported");
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bool sparse;
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if (!matchPattern(op.getSparse(), m_TorchConstantBool(&sparse)))
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return rewriter.notifyMatchFailure(
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op, "only constant sparse is currently supported");
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if (sparse)
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return rewriter.notifyMatchFailure(
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op, "sparse gradients is currently not supported");
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Value output = gatherTensorAlongSingleAxis(
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rewriter, op, weight, adaptor.getIndices(), 0, options.dimSizeIndexBits);
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rewriter.replaceOpWithNewOp<mhlo::ConvertOp>(
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op, getTypeConverter()->convertType(op.getType()), output);
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return success();
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}
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template <>
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LogicalResult ConvertAtenOp<AtenIndexSelectOp>::matchAndRewrite(
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AtenIndexSelectOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const {
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auto self = adaptor.getSelf();
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auto selfTy = self.getType().cast<RankedTensorType>();
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if (!selfTy)
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return op.emitError("only ranked tensor types are supported");
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int64_t dim;
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if (!matchPattern(op.getDim(), m_TorchConstantInt(&dim)))
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return rewriter.notifyMatchFailure(
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op, "only constant dim is currently supported");
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Value output = gatherTensorAlongSingleAxis(
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rewriter, op, self, adaptor.getIndex(), dim, options.dimSizeIndexBits);
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rewriter.replaceOpWithNewOp<mhlo::ConvertOp>(
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op, getTypeConverter()->convertType(op.getType()), output);
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return success();
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}
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// AtenGatherOp
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template <>
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LogicalResult ConvertAtenOp<AtenGatherOp>::matchAndRewrite(
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AtenGatherOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const {
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Location loc = op->getLoc();
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Value input = adaptor.getSelf();
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Value index = adaptor.getIndex();
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auto inputType = input.getType().cast<RankedTensorType>();
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auto indexType = index.getType().cast<RankedTensorType>();
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auto indexElemType = indexType.getElementType();
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if (indexType.getRank() != inputType.getRank()) {
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return op.emitError("`index` and `input` param should have the same rank");
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}
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int64_t dim;
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if (!matchPattern(op.getDim(), m_TorchConstantInt(&dim))) {
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return rewriter.notifyMatchFailure(
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op, "only constant int `dim` param supported");
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}
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dim = toPositiveDim(dim, inputType.getRank());
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if (!isValidDim(dim, inputType.getRank())) {
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return rewriter.notifyMatchFailure(op, "invalid `dim` param detected");
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}
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bool sparseGrad = false;
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if (!matchPattern(op.getSparseGrad(), m_TorchConstantBool(&sparseGrad))) {
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return rewriter.notifyMatchFailure(
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op, "only constant boolean `sparse_grad` param supported");
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}
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auto options = getOptions();
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auto indexShapeInfo =
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mhlo::getDimSizesOfTensor(rewriter, op, index, options.dimSizeIndexBits);
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if (failed(indexShapeInfo)) {
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return rewriter.notifyMatchFailure(
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op, "failed to get dim sizes of `index` param");
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}
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auto intType = rewriter.getIntegerType(options.dimSizeIndexBits);
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auto one = rewriter.create<arith::ConstantOp>(
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loc, rewriter.getIntegerAttr(intType, 1));
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auto toConcatIndexShapeValueVec = *indexShapeInfo;
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toConcatIndexShapeValueVec.push_back(one);
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auto toConcatIndexShape =
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rewriter.create<tensor::FromElementsOp>(loc, toConcatIndexShapeValueVec);
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auto indexShape = indexType.getShape();
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SmallVector<int64_t> toConcatIndexShapeVec(indexShape.begin(),
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indexShape.end());
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toConcatIndexShapeVec.push_back(1);
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RankedTensorType toConcatIndexType =
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RankedTensorType::get(toConcatIndexShapeVec, indexElemType);
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SmallVector<Value> toConcat;
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for (int64_t i = 0; i < inputType.getRank(); ++i) {
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if (i == dim) {
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toConcat.push_back(rewriter.create<mhlo::DynamicReshapeOp>(
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loc, toConcatIndexType, index, toConcatIndexShape));
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} else {
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toConcat.push_back(rewriter.create<mhlo::DynamicIotaOp>(
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loc, toConcatIndexType, toConcatIndexShape,
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rewriter.getI64IntegerAttr(i)));
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}
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}
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auto gatherIndicies = rewriter.create<mhlo::ConcatenateOp>(
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loc, toConcat, static_cast<uint64_t>(inputType.getRank()));
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SmallVector<int64_t> sliceSizes(inputType.getRank(), 1);
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int64_t indexVecDim = inputType.getRank();
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SmallVector<int64_t> collapsedDims;
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SmallVector<int64_t> startIndexMap;
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for (int64_t i = 0; i < inputType.getRank(); ++i) {
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collapsedDims.push_back(i);
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startIndexMap.push_back(i);
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}
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auto dimsAttr = mhlo::GatherDimensionNumbersAttr::get(
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rewriter.getContext(),
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/*offsetDims=*/{},
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/*collapsedSliceDims=*/collapsedDims,
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/*startIndexMap=*/startIndexMap,
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/*indexVecDim=*/indexVecDim);
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rewriter.replaceOpWithNewOp<mhlo::GatherOp>(
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op, input, gatherIndicies, dimsAttr,
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rewriter.getI64TensorAttr(sliceSizes));
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return success();
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}
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void mlir::torch::torch_to_mhlo::populateGatherOpPatternsAndLegality(
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TypeConverter &typeConverter, RewritePatternSet &patterns,
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ConversionTarget &target, const TorchToMhloOptions &options) {
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MLIRContext *context = patterns.getContext();
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#define INSERT_ATENOP_PATTERN(AtenOp) \
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target.addIllegalOp<AtenOp>(); \
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patterns.add<ConvertAtenOp<AtenOp>>(typeConverter, context, options)
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INSERT_ATENOP_PATTERN(AtenEmbeddingOp);
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INSERT_ATENOP_PATTERN(AtenIndexSelectOp);
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INSERT_ATENOP_PATTERN(AtenGatherOp);
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#undef INSERT_ATENOP_PATTERN
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}
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