mirror of https://github.com/llvm/torch-mlir
661 lines
28 KiB
C++
661 lines
28 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/TorchToTMTensor/TorchToTMTensor.h"
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#include "../PassDetail.h"
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#include "mlir/Dialect/Arith/IR/Arith.h"
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#include "mlir/Dialect/Func/IR/FuncOps.h"
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#include "mlir/Dialect/Linalg/IR/Linalg.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/IR/Builders.h"
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#include "mlir/IR/BuiltinTypes.h"
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#include "mlir/IR/MLIRContext.h"
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#include "torch-mlir-dialects/Dialect/TMTensor/IR/TMTensorDialect.h"
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#include "torch-mlir-dialects/Dialect/TMTensor/IR/TMTensorOps.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/IR/TorchTypes.h"
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#include "torch-mlir/Dialect/Torch/Utils/TorchUpstream.h"
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#include "torch-mlir/Dialect/Torch/Utils/Utils.h"
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#include "torch-mlir/Dialect/TorchConversion/Transforms/BackendTypeConversion.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::TorchConversion;
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using namespace mlir::torch::TMTensor;
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// -----------------------------------------------------------------------------
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// Patterns (as this grows, it should be organized into multiple files)
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// -----------------------------------------------------------------------------
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// This is going to eventually be O(#aten ops), which is in the 100s.
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//
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// Most of these patterns consist of:
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// 1. Checking that the operand/result types and other static properties are
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// good-enough to create a valid linalg op (such as operands being of
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// ranks/dtypes acceptable to the linalg op).
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// 2. Creating dynamic error guards, usually checking a predicate on the
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// compatibility of operand shapes.
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// 3. Creating init tensors for the computation op. Usually this involves
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// reifying IR for a shape transfer function based on the operand shapes.
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// 4. Creating a named linalg op to replace the original op.
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//
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// TODO: Use linalg OpDSL to autogenerate at least 1)/2)/3) such
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// that these patterns become mostly mechanical associations of
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// "aten.foo -> linalg.foo".
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static Value createTMTensorScatterOp(
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OpBuilder &b, Location loc, Value updates, Value indices, Value original,
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bool uniqueIndices,
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function_ref<void(OpBuilder &, Location, Value, Value)> bodyBuild) {
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auto originalTensorType = original.getType().cast<RankedTensorType>();
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Type originalElementType = originalTensorType.getElementType();
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auto scatterOp = b.create<TMTensor::ScatterOp>(
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loc, originalTensorType, ValueRange{updates, indices},
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ValueRange{original}, uniqueIndices);
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Region &scatterOpRegion = scatterOp.getRegion();
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auto &scatterOpBlock = scatterOpRegion.emplaceBlock();
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scatterOpBlock.addArguments({originalElementType, originalElementType},
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{loc, loc});
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OpBuilder regionBuilder(scatterOpRegion);
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auto blockArgs = scatterOpBlock.getArguments();
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Value updatesElement = blockArgs[0];
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Value originalElement = blockArgs[1];
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bodyBuild(regionBuilder, loc, updatesElement, originalElement);
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return scatterOp->getResult(0);
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}
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static Value createTMTensorScanOp(
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OpBuilder &b, Location loc, Value input, Value output, Value accumulator,
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int64_t dim, bool inclusive,
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function_ref<void(OpBuilder &, Location, Value, Value)> bodyBuild) {
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auto inputType = input.getType().cast<RankedTensorType>();
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auto accType = accumulator.getType().cast<RankedTensorType>();
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Type elementType = inputType.getElementType();
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auto scanOp = b.create<TMTensor::ScanOp>(
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loc, TypeRange{inputType, accType}, input,
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ValueRange{output, accumulator}, b.getI64IntegerAttr(dim),
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b.getBoolAttr(inclusive));
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Region &scanOpRegion = scanOp.getRegion();
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auto &scanOpBlock = scanOpRegion.emplaceBlock();
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scanOpBlock.addArguments({elementType, elementType}, {loc, loc});
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OpBuilder regionBuilder(scanOpRegion);
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auto blockArgs = scanOpBlock.getArguments();
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Value inputElement = blockArgs[0];
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Value accElement = blockArgs[1];
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bodyBuild(regionBuilder, loc, inputElement, accElement);
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return scanOp->getResult(0);
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}
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namespace {
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// aten::bincount op counts the frequency of each value in a 1-d input tensor of
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// non-negative ints.
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class ConvertAtenBincountOp : public OpConversionPattern<AtenBincountOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(AtenBincountOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
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return failure();
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Location loc = op.getLoc();
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MLIRContext *context = op->getContext();
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TypeConverter *typeConverter = getTypeConverter();
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Value input = adaptor.getSelf();
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Value torchTypeInput = op.getSelf();
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Value minlength = adaptor.getMinlength();
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Value weights = adaptor.getWeights();
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// TODO: Add a check to verify that the input tensor elements are all
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// non-negative.
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// Check whether the input is a 1-d tensor of integer type or not.
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RankedTensorType inputType = input.getType().cast<RankedTensorType>();
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if (inputType.getRank() != 1 ||
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!inputType.getElementType().isa<mlir::IntegerType>())
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return rewriter.notifyMatchFailure(
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op,
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"Input tensor has to be a one-dimensional tensor of integer type.");
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// Check whether the input tensor element type is i64 or not.
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IntegerType inputIntegerType =
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inputType.getElementType().cast<IntegerType>();
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if (inputIntegerType.getWidth() != 64)
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return rewriter.notifyMatchFailure(
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op,
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"Unimplemented: Integer width not equal to 64 are not supported.");
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// TODO: Incorporate the weight argument.
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if (!weights.getType().isa<mlir::torch::Torch::NoneType>())
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return rewriter.notifyMatchFailure(
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op, "Unimplemented: the weights operand is not incorporated.");
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// Finding the maximum value in the input tensor.
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SmallVector<int64_t> maxTensorSizes;
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ValueTensorType maxTensorType = ValueTensorType::get(
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context, llvm::makeArrayRef(maxTensorSizes),
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torchTypeInput.getType().cast<ValueTensorType>().getDtype());
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Value maxTensor =
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rewriter.create<AtenMaxOp>(loc, maxTensorType, torchTypeInput);
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maxTensor = typeConverter->materializeTargetConversion(
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rewriter, loc, typeConverter->convertType(maxTensor.getType()),
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maxTensor);
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// `maxTensor` is a 0-d tensor, extracting its only element and
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// storing it in `maxInput`.
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Value maxInput = rewriter.create<tensor::ExtractOp>(loc, maxTensor);
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// Creating a tm_tensor.scatter op with the following mapping:
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// 1.) `input` tensor maps to the indices in scatter op. `input` is
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// expanded from 1-d to 2-d, and its element type is set to i32 as required
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// for the scatter op.
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// 2.) `updates` is a 1-d dummy tensor with the size equivalent to the
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// `input`.
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// 3.) `bincount` a 1-d tensor maps to the original in scatter op
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// with size equal to the max(max(input) + 1, minlength).
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SmallVector<int64_t> expandedInputSizes{
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makeShapeTorchCompatible(inputType.getShape())[0], 1};
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ValueTensorType expandInputType = ValueTensorType::get(
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context, llvm::makeArrayRef(expandedInputSizes),
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torchTypeInput.getType().cast<ValueTensorType>().getDtype());
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Value torchCstOne = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(1));
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Value expandedInputTensor = rewriter.create<AtenUnsqueezeOp>(
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loc, expandInputType, torchTypeInput, torchCstOne);
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// Converting the input element type to i32.
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Value indices = convertTensorToDtype(
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rewriter, loc, expandedInputTensor,
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mlir::IntegerType::get(context, 32, mlir::IntegerType::Signed));
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indices = typeConverter->materializeTargetConversion(
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rewriter, loc, typeConverter->convertType(indices.getType()), indices);
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auto resultType = typeConverter->convertType(op->getResult(0).getType())
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.cast<RankedTensorType>();
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Type resultElemType = resultType.getElementType();
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SmallVector<Value, 1> inputSizeDynamic =
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getTensorSizesUntilDim(rewriter, loc, input, 0);
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Value updatesTensor = rewriter.create<tensor::EmptyOp>(
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loc, getAsOpFoldResult(inputSizeDynamic), resultElemType);
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Value constantZero = rewriter.create<arith::ConstantOp>(
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loc, rewriter.getZeroAttr(resultElemType));
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Value constantOne = rewriter.create<arith::ConstantIntOp>(
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loc, 1, resultElemType.getIntOrFloatBitWidth());
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// Bincount size = max(max(input) + 1, minlength)
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Value maxInputPlusOne =
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rewriter.create<arith::AddIOp>(loc, maxInput, constantOne);
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Value bincountSize =
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rewriter.create<arith::MaxSIOp>(loc, maxInputPlusOne, minlength);
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bincountSize = castIntToIndex(rewriter, loc, bincountSize);
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Value bincountTensor = createInitTensor(rewriter, loc, {bincountSize},
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resultElemType, constantZero);
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Value scatterOp = createTMTensorScatterOp(
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rewriter, loc, updatesTensor, indices, bincountTensor,
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/*uniqueIndices=*/false,
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[&](OpBuilder &b, Location loc, Value _, Value bincountElem) {
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Value add = b.create<arith::AddIOp>(loc, bincountElem, constantOne);
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b.create<TMTensor::YieldOp>(loc, add);
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});
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rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, scatterOp);
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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 ConvertAten_IndexPutImplOp
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: public OpConversionPattern<Aten_IndexPutImplOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(Aten_IndexPutImplOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
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return failure();
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Location loc = op.getLoc();
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MLIRContext *context = op->getContext();
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Value input = adaptor.getSelf();
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Value values = adaptor.getValues();
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RankedTensorType inputType = input.getType().cast<RankedTensorType>();
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RankedTensorType valuesType = values.getType().cast<RankedTensorType>();
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auto resultType = typeConverter->convertType(op->getResult(0).getType())
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.cast<RankedTensorType>();
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// The unsafe should be either `False` or `none`.
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if (!op.getUnsafe().getType().isa<Torch::NoneType>()) {
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bool unsafe;
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if (!matchPattern(op.getUnsafe(), m_TorchConstantBool(&unsafe)))
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return rewriter.notifyMatchFailure(
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op, "unimplemented: unsafe must be a constant");
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else if (unsafe)
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return rewriter.notifyMatchFailure(
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op, "unimplemented: unsafe is expected to be false");
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}
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// The accumulate should be a torch constant of boolean type.
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bool accumulate;
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if (!matchPattern(op.getAccumulate(), m_TorchConstantBool(&accumulate)))
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return rewriter.notifyMatchFailure(
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op, "Expected accumulate to be constant bool.");
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// The element type of the `input` and `values` should be same.
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if (inputType.getElementType() != valuesType.getElementType())
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return rewriter.notifyMatchFailure(
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op, "Input element type should be same as the values element type.");
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SmallVector<Value> indicesList;
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getListConstructElements(adaptor.getIndices(), indicesList);
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// The size of the list of the index tensors should not be greater than the
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// input rank.
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if ((int64_t)indicesList.size() > inputType.getRank())
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return rewriter.notifyMatchFailure(
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op, "Indices list size should not be greater than the input rank.");
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// TODO: Add support for cases with indices list size not equal to 1.
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if (indicesList.size() != 1)
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return rewriter.notifyMatchFailure(
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op, "Unimplemented: Indices list size != 1");
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Value indexTensor = indicesList[0];
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if (indexTensor.getType().isa<Torch::NoneType>())
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return rewriter.notifyMatchFailure(op, "Index tensor must not be None.");
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// Creating a tm_tensor.scatter op with the following mapping:
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// 1.) Index tensor from the `indicesList` maps to the indices in scatter
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// op. Index tensor is expanded from 1-d to 2-d, and its element type is set
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// to i32 as required for the scatter op.
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// 2.) `values` is mapped to `updates` in scatter op.
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// 3.) `input` is mapped to `original` in scatter op.
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std::optional<unsigned> indexTensorRank = getTensorRank(indexTensor);
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if (!indexTensorRank || *indexTensorRank != 1)
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return rewriter.notifyMatchFailure(
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op, "unimplemented: index tensor with rank != 1 is not supported");
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auto indexTensorType = indexTensor.getType().cast<BaseTensorType>();
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int64_t indexTensorSize = indexTensorType.getSizes()[0];
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SmallVector<int64_t> expandedIndexTensorSizes{indexTensorSize, 1};
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ValueTensorType expandedIndexTensorType = ValueTensorType::get(
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context, llvm::makeArrayRef(expandedIndexTensorSizes),
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indexTensorType.getDtype());
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Value torchCstOne = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(1));
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Value expandedIndexTensor = rewriter.create<AtenUnsqueezeOp>(
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loc, expandedIndexTensorType, indexTensor, torchCstOne);
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// `TMTensor::ScatterOp` expects indices of element type i32.
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Value indices = convertTensorToDtype(
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rewriter, loc, expandedIndexTensor,
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mlir::IntegerType::get(context, 32, mlir::IntegerType::Signed));
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indices = typeConverter->materializeTargetConversion(
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rewriter, loc, typeConverter->convertType(indices.getType()), indices);
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bool invalidInputTypeFound = false;
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Value scatterOp = createTMTensorScatterOp(
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rewriter, loc, values, indices, input, /*uniqueIndices=*/false,
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[&](OpBuilder &b, Location loc, Value valuesElement,
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Value inputElement) {
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Value yieldValue = valuesElement;
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if (accumulate) {
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if (inputElement.getType().isa<mlir::IntegerType>()) {
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yieldValue =
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b.create<arith::AddIOp>(loc, inputElement, valuesElement);
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} else if (inputElement.getType().isa<mlir::FloatType>()) {
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yieldValue =
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b.create<arith::AddFOp>(loc, inputElement, valuesElement);
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} else {
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invalidInputTypeFound = true;
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return;
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}
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}
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b.create<TMTensor::YieldOp>(loc, yieldValue);
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});
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if (invalidInputTypeFound) {
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return rewriter.notifyMatchFailure(
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op,
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"unimplemented: input tensor must be of integer type or float type");
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}
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rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, scatterOp);
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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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// The original implementation of the op is as follows:
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//
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// Indices and GradOutput Layout: [N, C, H, W] or [C, H, W]
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// Input Layout: [N, C, Hin, Win] or [C, Hin, Win]
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//
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// for i in range(N):
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// for j in range(C):
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// for k in range(H):
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// for l in range(W):
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// index = indices[i, j, k, l]
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// result[i, j, index/Win, index%Win] += gradOutput[i, j, k, l]
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//
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// OR
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//
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// for i in range(C):
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// for j in range(H):
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// for k in range(W):
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// index = indices[i, j, k]
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// result[i, index/Win, index%Win] += gradOutput[i, j, k]
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//
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class ConvertAtenMaxPool2dWithIndicesBackwardOp
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: public OpConversionPattern<AtenMaxPool2dWithIndicesBackwardOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(AtenMaxPool2dWithIndicesBackwardOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
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return failure();
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Location loc = op.getLoc();
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MLIRContext *context = op->getContext();
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Value gradOutput = adaptor.getGradOutput();
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Value input = adaptor.getSelf();
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RankedTensorType gradOutputType =
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gradOutput.getType().cast<RankedTensorType>();
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Type gradOutputElemType = gradOutputType.getElementType();
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RankedTensorType inputType = input.getType().cast<RankedTensorType>();
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Type inputElemType = inputType.getElementType();
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int64_t tensorOperandRank = inputType.getRank();
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// `TMTensor::ScatterOp` expects indices of element type i32.
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Value indices = convertTensorToDtype(
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rewriter, loc, op.getIndices(),
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mlir::IntegerType::get(context, 32, mlir::IntegerType::Signed));
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indices = typeConverter->materializeTargetConversion(
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rewriter, loc, typeConverter->convertType(indices.getType()), indices);
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RankedTensorType indicesType = indices.getType().cast<RankedTensorType>();
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Type indicesElemType = indicesType.getElementType();
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// The element type of the `input` and `grad_output` should be same.
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if (inputElemType != gradOutputElemType)
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return rewriter.notifyMatchFailure(
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op,
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"Input element type should be same as the grad_output element type.");
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// Since the scatter op requires indices to be a 2-d tensor, we create a new
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// 5-d/4-d tensor (depending on the original indices layout) comprising the
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// index values. We will collapse this tensor into a 2-d tensor. The
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// algorithm for the creation of updated indices tensor is as follows:
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//
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// for i in range(N):
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// for j in range(C):
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// for k in range(H):
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// for l in range(W):
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// for m in range(4):
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// if m == 0:
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// updatedIndices[N][C][H][W][0] = i
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// if m == 1:
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// updatedIndices[N][C][H][W][1] = j
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// if m == 2:
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// updatedIndices[N][C][H][W][2] =
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// originalIndices[i, j, k, l] / Win
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// if m == 3:
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// updatedIndices[N][C][H][W][3] =
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// originalIndices[i, j, k, l] % Win
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//
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// OR
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//
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// for j in range(C):
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// for k in range(H):
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// for l in range(W):
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// for m in range(3):
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// if m == 0:
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// updatedIndices[C][H][W][0] = i
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// if m == 1:
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// updatedIndices[C][H][W][1] = originalIndices[i, j, k, l] / Win
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// if m == 2:
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// updatedIndices[C][H][W][2] = originalIndices[i, j, k, l] % Win
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SmallVector<Value> inputShape = getTensorSizes(rewriter, loc, input);
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SmallVector<AffineExpr> originalIndicesDimExprs, updatedIndicesDimExprs;
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|
for (int64_t i = 0; i < tensorOperandRank; i++) {
|
|
originalIndicesDimExprs.push_back(rewriter.getAffineDimExpr(i));
|
|
updatedIndicesDimExprs.push_back(rewriter.getAffineDimExpr(i));
|
|
}
|
|
updatedIndicesDimExprs.push_back(
|
|
rewriter.getAffineDimExpr(tensorOperandRank));
|
|
|
|
SmallVector<AffineMap> indexingMaps = AffineMap::inferFromExprList(
|
|
{originalIndicesDimExprs, updatedIndicesDimExprs});
|
|
SmallVector<utils::IteratorType> iteratorTypes(
|
|
tensorOperandRank + 1, utils::IteratorType::parallel);
|
|
|
|
SmallVector<OpFoldResult> updatedIndicesShape =
|
|
getAsOpFoldResult(getTensorSizes(rewriter, loc, indices));
|
|
updatedIndicesShape.push_back(rewriter.getIndexAttr(tensorOperandRank));
|
|
|
|
Value initTensor = rewriter.create<tensor::EmptyOp>(
|
|
loc, updatedIndicesShape, indicesElemType);
|
|
|
|
Value wIn = inputShape[tensorOperandRank - 1];
|
|
SmallVector<Value> cstValues;
|
|
for (int64_t i = 0; i < tensorOperandRank; i++)
|
|
cstValues.push_back(rewriter.create<arith::ConstantIndexOp>(loc, i));
|
|
|
|
Value updatedIndices =
|
|
rewriter
|
|
.create<linalg::GenericOp>(
|
|
loc, initTensor.getType(), indices, initTensor, indexingMaps,
|
|
iteratorTypes,
|
|
[tensorOperandRank, wIn, cstValues,
|
|
indicesElemType](OpBuilder &b, Location loc, ValueRange args) {
|
|
Value index = castIntToIndex(b, loc, args[0]);
|
|
Value updatedIndex = cstValues[0];
|
|
Value lastDim =
|
|
b.create<linalg::IndexOp>(loc, tensorOperandRank);
|
|
|
|
for (int64_t i = tensorOperandRank - 1; i >= 0; i--) {
|
|
Value result;
|
|
if (i == tensorOperandRank - 1)
|
|
result = b.create<arith::RemSIOp>(loc, index, wIn);
|
|
if (i == tensorOperandRank - 2)
|
|
result = b.create<arith::FloorDivSIOp>(loc, index, wIn);
|
|
if (i == tensorOperandRank - 3 ||
|
|
i == tensorOperandRank - 4)
|
|
result = b.create<linalg::IndexOp>(loc, i);
|
|
|
|
Value pred = b.create<arith::CmpIOp>(
|
|
loc, arith::CmpIPredicate::eq, lastDim, cstValues[i]);
|
|
Value addAmount = b.create<arith::SelectOp>(
|
|
loc, pred, result, cstValues[0]);
|
|
updatedIndex =
|
|
b.create<arith::AddIOp>(loc, updatedIndex, addAmount);
|
|
}
|
|
|
|
updatedIndex = b.create<arith::IndexCastOp>(
|
|
loc, indicesElemType, updatedIndex);
|
|
b.create<linalg::YieldOp>(loc, updatedIndex);
|
|
})
|
|
.getResult(0);
|
|
|
|
// Creating a new tensor initialized with zeros and size same as the input
|
|
// tensor.
|
|
Value outputTensor =
|
|
createZeroInitTensor(rewriter, loc, inputShape, inputElemType);
|
|
|
|
// Collapsing `gradOutput` into a 1-d tensor.
|
|
SmallVector<ReassociationIndices> reassociationCollapse(1);
|
|
for (auto i = 0; i < gradOutputType.getRank(); i++)
|
|
reassociationCollapse[0].push_back(i);
|
|
RankedTensorType gradOutputFlattenedType;
|
|
int64_t numelGradOutput = getNumberOfElements(gradOutputType);
|
|
gradOutputFlattenedType = RankedTensorType::get(
|
|
makeShapeLLVMCompatible({numelGradOutput}), gradOutputElemType);
|
|
Value gradOutputFlattened = rewriter.create<tensor::CollapseShapeOp>(
|
|
loc, gradOutputFlattenedType, gradOutput, reassociationCollapse);
|
|
|
|
// Collapsing updated indices into a 2-d tensor.
|
|
SmallVector<ReassociationIndices> reassociationCollapseIndices(2);
|
|
for (auto i = 0; i < tensorOperandRank; i++)
|
|
reassociationCollapseIndices[0].push_back(i);
|
|
reassociationCollapseIndices[1].push_back(tensorOperandRank);
|
|
int64_t numelIndices = getNumberOfElements(indicesType);
|
|
Value indicesCollapsed = rewriter.create<tensor::CollapseShapeOp>(
|
|
loc,
|
|
RankedTensorType::get(
|
|
makeShapeLLVMCompatible({numelIndices, tensorOperandRank}),
|
|
indicesElemType),
|
|
updatedIndices, reassociationCollapseIndices);
|
|
|
|
bool invalidInputTypeFound = false;
|
|
Value scatterOp = createTMTensorScatterOp(
|
|
rewriter, loc, /*updates=*/gradOutputFlattened,
|
|
/*indices=*/indicesCollapsed, /*original=*/outputTensor,
|
|
/*uniqueIndices=*/false,
|
|
[&](OpBuilder &b, Location loc, Value valuesElement,
|
|
Value inputElement) {
|
|
Value yieldValue = valuesElement;
|
|
if (inputElement.getType().isa<mlir::IntegerType>()) {
|
|
yieldValue =
|
|
b.create<arith::AddIOp>(loc, inputElement, valuesElement);
|
|
} else if (inputElement.getType().isa<mlir::FloatType>()) {
|
|
yieldValue =
|
|
b.create<arith::AddFOp>(loc, inputElement, valuesElement);
|
|
} else {
|
|
invalidInputTypeFound = true;
|
|
return;
|
|
}
|
|
b.create<TMTensor::YieldOp>(loc, yieldValue);
|
|
});
|
|
|
|
if (invalidInputTypeFound) {
|
|
return rewriter.notifyMatchFailure(
|
|
op,
|
|
"unimplemented: input tensor must be of integer type or float type");
|
|
}
|
|
|
|
Type newResultType = getTypeConverter()->convertType(op.getType());
|
|
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType, scatterOp);
|
|
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
namespace {
|
|
class ConvertAtenCumsumOp : public OpConversionPattern<AtenCumsumOp> {
|
|
public:
|
|
using OpConversionPattern::OpConversionPattern;
|
|
LogicalResult
|
|
matchAndRewrite(AtenCumsumOp op, OpAdaptor adaptor,
|
|
ConversionPatternRewriter &rewriter) const override {
|
|
|
|
Value input = adaptor.getSelf();
|
|
auto resultType = input.getType().cast<RankedTensorType>();
|
|
Type elementType = resultType.getElementType();
|
|
int64_t inputRank = resultType.getRank();
|
|
Location loc = op->getLoc();
|
|
Value dtype = op.getDtype();
|
|
if (!dtype.getType().isa<Torch::NoneType>())
|
|
return rewriter.notifyMatchFailure(
|
|
op, "unsupported: dtype argument not supported");
|
|
|
|
int64_t dim;
|
|
if (!matchPattern(op.getDim(), m_TorchConstantInt(&dim)))
|
|
return rewriter.notifyMatchFailure(
|
|
op, "unimplemented: only constant dim value is supported");
|
|
dim = toPositiveDim(dim, inputRank);
|
|
if (!isValidDim(dim, inputRank))
|
|
return rewriter.notifyMatchFailure(op, "invalid dim");
|
|
|
|
SmallVector<Value> sizes = getTensorSizes(rewriter, loc, input);
|
|
Value output = createZeroInitTensor(rewriter, loc, sizes, elementType);
|
|
output = rewriter.create<tensor::CastOp>(loc, resultType, output);
|
|
|
|
SmallVector<Value> accSizes(sizes);
|
|
accSizes.erase(accSizes.begin() + dim);
|
|
SmallVector<int64_t> accStatic(
|
|
makeShapeTorchCompatible(resultType.getShape()));
|
|
accStatic.erase(accStatic.begin() + dim);
|
|
Value acc = createZeroInitTensor(rewriter, loc, accSizes, elementType);
|
|
Type accType =
|
|
RankedTensorType::get(makeShapeLLVMCompatible(accStatic), elementType);
|
|
acc = rewriter.create<tensor::CastOp>(loc, accType, acc);
|
|
|
|
Value result = createTMTensorScanOp(
|
|
rewriter, loc, input, output, acc, dim, /*inclusive=*/true,
|
|
[](OpBuilder &b, Location loc, Value input, Value acc) {
|
|
Value sum = (input.getType().isa<mlir::FloatType>()
|
|
? b.create<arith::AddFOp>(loc, input, acc)
|
|
: b.create<arith::AddIOp>(loc, input, acc))
|
|
->getResult(0);
|
|
b.create<TMTensor::YieldOp>(loc, sum);
|
|
});
|
|
|
|
rewriter.replaceOpWithNewOp<tensor::CastOp>(op, resultType, result);
|
|
return success();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
// -----------------------------------------------------------------------------
|
|
// The pass
|
|
// -----------------------------------------------------------------------------
|
|
|
|
namespace {
|
|
class ConvertTorchToTMTensor
|
|
: public ConvertTorchToTMTensorBase<ConvertTorchToTMTensor> {
|
|
public:
|
|
void getDependentDialects(DialectRegistry ®istry) const override {
|
|
registry.insert<linalg::LinalgDialect>();
|
|
registry.insert<func::FuncDialect>();
|
|
registry.insert<tensor::TensorDialect>();
|
|
registry.insert<arith::ArithDialect>();
|
|
registry.insert<TMTensorDialect>();
|
|
TorchConversion::getBackendTypeConversionDependentDialects(registry);
|
|
}
|
|
|
|
void runOnOperation() override {
|
|
MLIRContext *context = &getContext();
|
|
ConversionTarget target(*context);
|
|
target.addLegalDialect<linalg::LinalgDialect, func::FuncDialect,
|
|
tensor::TensorDialect, arith::ArithDialect,
|
|
Torch::TorchDialect, TMTensorDialect>();
|
|
|
|
TypeConverter typeConverter;
|
|
typeConverter.addConversion([](Type type) { return type; });
|
|
TorchConversion::setupBackendTypeConversion(target, typeConverter);
|
|
|
|
RewritePatternSet patterns(context);
|
|
target.addIllegalOp<AtenBincountOp>();
|
|
patterns.add<ConvertAtenBincountOp>(typeConverter, context);
|
|
target.addIllegalOp<Aten_IndexPutImplOp>();
|
|
patterns.add<ConvertAten_IndexPutImplOp>(typeConverter, context);
|
|
target.addIllegalOp<AtenMaxPool2dWithIndicesBackwardOp>();
|
|
patterns.add<ConvertAtenMaxPool2dWithIndicesBackwardOp>(typeConverter,
|
|
context);
|
|
target.addIllegalOp<AtenCumsumOp>();
|
|
patterns.add<ConvertAtenCumsumOp>(typeConverter, context);
|
|
|
|
if (failed(applyPartialConversion(getOperation(), target,
|
|
std::move(patterns))))
|
|
return signalPassFailure();
|
|
}
|
|
};
|
|
} // namespace
|
|
|
|
std::unique_ptr<OperationPass<func::FuncOp>>
|
|
mlir::torch::createConvertTorchToTMTensorPass() {
|
|
return std::make_unique<ConvertTorchToTMTensor>();
|
|
}
|