mirror of https://github.com/llvm/torch-mlir
461 lines
20 KiB
C++
461 lines
20 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/TorchToLinalg/TorchToLinalg.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/Math/IR/Math.h"
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#include "mlir/Dialect/MemRef/IR/MemRef.h" // TODO: For `memref.dim`.
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#include "mlir/Dialect/Traits.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include "npcomp/Dialect/Torch/IR/TorchOps.h"
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#include "npcomp/Dialect/Torch/Transforms/BackendTypeConversion.h"
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using namespace mlir;
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using namespace mlir::NPCOMP;
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using namespace mlir::NPCOMP::Torch;
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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 LogicalResult verifyLinalgCompatibleTypes(Operation *op,
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PatternRewriter &rewriter) {
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// Check the value tensor is ranked as expected by Linalg.
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// TODO: Remove this check but use a separate verification pass to verify the
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// invariants expected by later passes.
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auto isValidLinalgType = [](Type type) {
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auto tensor = type.dyn_cast<ValueTensorType>();
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return !tensor ||
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tensor.toBuiltinTensor().dyn_cast_or_null<RankedTensorType>();
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};
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bool valid = llvm::all_of(op->getOperandTypes(), isValidLinalgType) &&
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llvm::all_of(op->getResultTypes(), isValidLinalgType);
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if (!valid)
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return rewriter.notifyMatchFailure(op, "type cannot be lowered to linalg");
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return success();
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}
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namespace {
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class ConvertAtenBatchNormOp : public OpConversionPattern<AtenBatchNormOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(AtenBatchNormOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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AtenBatchNormOp::Adaptor adaptor(operands);
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MLIRContext *context = op->getContext();
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Location loc = op->getLoc();
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Value input = adaptor.input();
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Value weight = adaptor.weight();
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Value bias = adaptor.bias();
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Value runningMean = adaptor.running_mean();
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Value runningVar = adaptor.running_var();
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Value training = adaptor.training();
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Value eps = adaptor.eps();
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// TODO: Handle the None cases for the optional parameters:
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// weight, bias, running_mean, running_var.
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if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
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return failure();
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auto inputType = input.getType().cast<RankedTensorType>();
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auto weightType = weight.getType().cast<RankedTensorType>();
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auto biasType = bias.getType().cast<RankedTensorType>();
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auto runningMeanType = runningMean.getType().cast<RankedTensorType>();
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auto runningVarType = runningVar.getType().cast<RankedTensorType>();
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auto inputRank = inputType.getRank();
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if (inputRank <= 2)
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return rewriter.notifyMatchFailure(
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op, "input should have rank larger than 2");
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if (weightType.getRank() != 1 || biasType.getRank() != 1 ||
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runningMeanType.getRank() != 1 || runningVarType.getRank() != 1) {
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return rewriter.notifyMatchFailure(
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op, "expect weight, bias, running_mean and running_var to be rank 1");
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}
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// TODO: Add support for training.
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auto constFalse = rewriter.create<ConstantOp>(
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loc, IntegerAttr::get(IntegerType::get(context, 1), 0));
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auto trainingFalse =
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rewriter.create<CmpIOp>(loc, CmpIPredicate::eq, training, constFalse);
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rewriter.create<AssertOp>(
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loc, trainingFalse,
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rewriter.getStringAttr("training is not supported for now"));
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// num_features – C from an expected input of size (N,C,D,H,W ...)
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Value numFeatures = rewriter.create<memref::DimOp>(loc, input, 1);
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auto contractingDim0EqualsNumFeatures = [&](Value v) {
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auto dim0 = rewriter.create<memref::DimOp>(loc, v, 0);
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auto dim0Equal =
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rewriter.create<CmpIOp>(loc, CmpIPredicate::eq, numFeatures, dim0);
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rewriter.create<AssertOp>(
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loc, dim0Equal,
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rewriter.getStringAttr(
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"expect the size of dim 0 equal to the number of features"));
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};
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contractingDim0EqualsNumFeatures(weight);
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contractingDim0EqualsNumFeatures(bias);
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contractingDim0EqualsNumFeatures(runningMean);
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contractingDim0EqualsNumFeatures(runningVar);
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auto indexingMap = AffineMap::get(
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/*dimCount=*/inputRank,
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/*symbolCount=*/0, rewriter.getAffineDimExpr(1), context);
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SmallVector<AffineMap> indexingMaps = {
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rewriter.getMultiDimIdentityMap(inputRank), // input
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indexingMap, // weight
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indexingMap, // bias
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indexingMap, // runningMean
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indexingMap, // runningVar
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rewriter.getMultiDimIdentityMap(inputRank), // output
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};
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SmallVector<StringRef> iteratorTypes(inputRank, "parallel");
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Value batchNorm =
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rewriter
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.create<linalg::GenericOp>(
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loc, input.getType(),
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ValueRange{input, weight, bias, runningMean, runningVar}, input,
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/*indexingMaps=*/indexingMaps,
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/*iteratorTypes=*/iteratorTypes,
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[&](OpBuilder &b, Location loc, ValueRange args) {
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Value input = args[0], weight = args[1], bias = args[2],
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mean = args[3], var = args[4];
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// ((input - mean) / sqrt(var + eps)) * weight + bias
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Value inputSubMean = b.create<SubFOp>(loc, input, mean);
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// The eps is always f64.
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Value truncatedEps =
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b.create<FPTruncOp>(loc, var.getType(), eps);
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Value varPlusEps = b.create<AddFOp>(loc, var, truncatedEps);
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Value rSTD = b.create<math::RsqrtOp>(loc, varPlusEps);
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Value temp = b.create<MulFOp>(loc, inputSubMean, rSTD);
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Value timesWeight = b.create<MulFOp>(loc, temp, weight);
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Value plusBias = b.create<AddFOp>(loc, timesWeight, bias);
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b.create<linalg::YieldOp>(loc, plusBias);
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})
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.getResult(0);
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Type newResultType = getTypeConverter()->convertType(op.getType());
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rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType, batchNorm);
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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 ConvertAtenMmOp : public OpConversionPattern<AtenMmOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(AtenMmOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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Location loc = op->getLoc();
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Value lhs = operands[0];
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Value rhs = operands[1];
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// A user can write an errorneous program where `aten.mm` is in fact called
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// with operands of invalid rank or dtype. We cannot convert to linalg in
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// this case or we will get a verifier error, which corresponds to breaking
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// of *internal* compiler invariants, and for a user manifests as a compiler
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// crash in the worst case (such as we try to canonicalize/fold/print the
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// invalid op before the verifier gets to see it -- also release builds of a
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// mature copmiler usually have the verifier turned off for compile time
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// reasons).
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//
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// The compiler cannot crash even if the user wrote an erroneous program!
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if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
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return failure();
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if (lhs.getType().cast<RankedTensorType>().getRank() != 2 ||
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rhs.getType().cast<RankedTensorType>().getRank() != 2) {
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return rewriter.notifyMatchFailure(
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op, "expected both operands to aten.mm to be rank 2");
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}
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Value lhsDim0 = rewriter.create<memref::DimOp>(loc, lhs, 0);
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Value lhsDim1 = rewriter.create<memref::DimOp>(loc, lhs, 1);
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Value rhsDim0 = rewriter.create<memref::DimOp>(loc, rhs, 0);
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Value rhsDim1 = rewriter.create<memref::DimOp>(loc, rhs, 1);
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Value contractingDimEqual =
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rewriter.create<CmpIOp>(loc, CmpIPredicate::eq, lhsDim1, rhsDim0);
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rewriter.create<AssertOp>(
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loc, contractingDimEqual,
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rewriter.getStringAttr(
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"mismatching contracting dimension for torch.aten.mm"));
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Type newResultType = getTypeConverter()->convertType(op.getType());
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Type elementType = newResultType.cast<TensorType>().getElementType();
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Value initTensor = rewriter.create<linalg::InitTensorOp>(
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loc, ValueRange{lhsDim0, rhsDim1}, elementType);
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Value c0 =
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rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 0.0));
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Value zeroFill =
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rewriter.create<linalg::FillOp>(loc, initTensor, c0).getResult(0);
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Value matmul = rewriter
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.create<linalg::MatmulOp>(loc, zeroFill.getType(),
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ValueRange{lhs, rhs}, zeroFill)
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.getResult(0);
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// When constructed with just dynamic sizes, InitTensorOp will have a result
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// type which has all `?`'s for dimensions, which might not be the result
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// type of `op`. The constraints on later linalg ops means that the result
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// of the MatmulOp will have this type too. So cast it to the desired type
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// so that in the end we have the original result type.
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rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType, matmul);
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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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// See comments at in convertMmOp and the heading for this section for general
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// considerations. This function needs to be auto-generated.
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class ConvertAtenLinearOp : public OpConversionPattern<AtenLinearOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(AtenLinearOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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AtenLinearOp::Adaptor adaptor(operands);
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MLIRContext *context = op->getContext();
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Location loc = op->getLoc();
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Value input = adaptor.input();
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Value weight = adaptor.weight();
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Value bias = adaptor.bias();
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// TODO: Handle the case of bias being None (bias is optional).
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if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
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return failure();
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auto inputType = input.getType().cast<RankedTensorType>();
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auto weightType = weight.getType().cast<RankedTensorType>();
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auto biasType = bias.getType().cast<RankedTensorType>();
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// Only handle the case of rank 2 `input` for now.
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// TODO: Insert the appropriate reshape to collapse any leading dimensions.
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if (inputType.getRank() != 2 || weightType.getRank() != 2 ||
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biasType.getRank() != 1) {
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return rewriter.notifyMatchFailure(
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op,
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"expected both input and weight to be rank 2 and bias to be rank 1");
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}
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// TODO: Handle type promotion. What are ATen's promotion rules?
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if (inputType.getElementType() != weightType.getElementType() ||
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inputType.getElementType() != biasType.getElementType()) {
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return rewriter.notifyMatchFailure(op, "unimplemented: type promotion");
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}
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// TODO: We can handle a static size 1 here at some complexity cost, but the
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// dynamic case is not representable in linalg. We don't handle either for
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// now. Biases are generally statically shaped for most models (since for
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// inference they are constants, and for training they don't change shape
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// typically), so this is not too constraining.
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auto biasSize = bias.getType().cast<RankedTensorType>().getShape()[0];
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if (biasSize == 1 || biasSize == ShapedType::kDynamicSize)
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return rewriter.notifyMatchFailure(
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op, "unimplemented: size-1 broadcasting for aten::LinearOp");
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auto getDimOp = [&](Value v, int dimension) {
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return rewriter.create<memref::DimOp>(loc, v, dimension);
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};
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Value inputDim0 = getDimOp(input, 0);
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Value inputDim1 = getDimOp(input, 1);
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Value weightDim0 = getDimOp(weight, 0);
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Value weightDim1 = getDimOp(weight, 1);
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Value biasDim0 = getDimOp(bias, 0);
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Value contractingDimEqual =
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rewriter.create<CmpIOp>(loc, CmpIPredicate::eq, inputDim1, weightDim1);
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rewriter.create<AssertOp>(
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loc, contractingDimEqual,
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rewriter.getStringAttr(
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"mismatching contracting dimension for aten.linear"));
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// Here we take advantage of ruling out the size-1 case above.
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// In the static-size-1 case, we will not emit this check at all.
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Value biasSizeCorrect =
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rewriter.create<CmpIOp>(loc, CmpIPredicate::eq, weightDim0, biasDim0);
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rewriter.create<AssertOp>(
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loc, biasSizeCorrect,
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rewriter.getStringAttr("mismatching bias size for aten.linear"));
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Value initTensor = rewriter.create<linalg::InitTensorOp>(
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loc, ValueRange{inputDim0, weightDim0}, inputType.getElementType());
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SmallVector<AffineMap> broadcastIndexingMaps = {
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AffineMap::get(
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/*dimCount=*/2, /*symbolCount=*/0, rewriter.getAffineDimExpr(1)),
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rewriter.getMultiDimIdentityMap(2)};
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SmallVector<StringRef> iteratorTypes(2, "parallel");
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Value broadcasted =
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rewriter
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.create<linalg::GenericOp>(
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loc, initTensor.getType(), bias, initTensor,
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/*indexingMaps=*/broadcastIndexingMaps,
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/*iteratorTypes=*/iteratorTypes,
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[](OpBuilder &b, Location loc, ValueRange args) {
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b.create<linalg::YieldOp>(loc, args[0]);
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})
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.getResult(0);
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// We need a matmul with dimension ordering (N, K) * (M, K), so transpose
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// the weights to fit into linalg::MatmulOp which is (N, K) * (K, M).
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// TODO: This whole aten.linear lowering should eventually be generated from
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// a single linalg ODS generator statement. Both the bias and matmul part.
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SmallVector<AffineMap> transposeIndexingMaps = {
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AffineMap::get(
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/*dimCount=*/2, /*symbolCount=*/0,
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{rewriter.getAffineDimExpr(1), rewriter.getAffineDimExpr(0)},
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context),
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rewriter.getMultiDimIdentityMap(2)};
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Value transposedWeightInitTensor = rewriter.create<linalg::InitTensorOp>(
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loc, ValueRange{weightDim1, weightDim0}, weightType.getElementType());
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Value transposedWeights =
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rewriter
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.create<linalg::GenericOp>(
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loc, transposedWeightInitTensor.getType(), weight,
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transposedWeightInitTensor,
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/*indexingMaps=*/transposeIndexingMaps,
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/*iteratorTypes=*/iteratorTypes,
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[](OpBuilder &b, Location loc, ValueRange args) {
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b.create<linalg::YieldOp>(loc, args[0]);
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})
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.getResult(0);
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Value matmul = rewriter
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.create<linalg::MatmulOp>(
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loc, broadcasted.getType(),
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ValueRange{input, transposedWeights}, broadcasted)
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.getResult(0);
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Type newResultType = getTypeConverter()->convertType(op.getType());
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rewriter.replaceOpWithNewOp<tensor::CastOp>(op, newResultType, matmul);
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return success();
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}
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};
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} // namespace
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static Value createScalarRelu(OpBuilder &b, Location loc, ValueRange args) {
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Type elementType = args[0].getType();
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// TODO: Add support for integer types.
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assert(elementType.isa<::mlir::FloatType>() &&
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"Only support float case for relu");
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Value constZero = b.create<ConstantOp>(loc, FloatAttr::get(elementType, 0.0));
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Value pred = b.create<CmpFOp>(loc, CmpFPredicate::UGT, args[0], constZero);
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return b.create<SelectOp>(loc, pred, args[0], constZero);
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}
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namespace {
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// Converts a unary op. There is no implicit broadcasting behavior, so these can
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// be trivially lowered to linalg.
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// TODO: For binary ops, we will need a "linalg.generic-like" op that models
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// N-ary broadcasting and allows us to do multiversioning techniques for
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// lowering to linalg. We can trivially handle this as through that
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// abstraction instead.
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struct ConvertUnaryOp : ConversionPattern {
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ConvertUnaryOp(TypeConverter &typeConverter, MLIRContext *context)
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: ConversionPattern(typeConverter, MatchAnyOpTypeTag(), /*benefit=*/1,
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context) {}
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LogicalResult
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matchAndRewrite(Operation *op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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if (!isa<AtenTanhOp>(op) && !isa<AtenReluOp>(op))
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return rewriter.notifyMatchFailure(op, "not a unary op");
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if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
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return failure();
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Value operand = operands[0];
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auto type = getTypeConverter()
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->convertType(op->getResult(0).getType())
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.cast<RankedTensorType>();
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auto rank = type.getRank();
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SmallVector<StringRef> iteratorTypes(rank, "parallel");
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SmallVector<AffineMap> indexingMaps = {
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rewriter.getMultiDimIdentityMap(rank),
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rewriter.getMultiDimIdentityMap(rank)};
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rewriter.replaceOpWithNewOp<linalg::GenericOp>(
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op, type, operand, operand,
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/*indexingMaps=*/indexingMaps,
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/*iteratorTypes=*/iteratorTypes,
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[&](OpBuilder &b, Location loc, ValueRange args) {
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Value result;
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if (isa<AtenTanhOp>(op))
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result = b.create<math::TanhOp>(loc, args[0]);
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else if (isa<AtenReluOp>(op))
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result = createScalarRelu(b, loc, args);
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b.create<linalg::YieldOp>(loc, result);
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});
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return success();
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}
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};
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} // namespace
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// -----------------------------------------------------------------------------
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// The pass
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// -----------------------------------------------------------------------------
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namespace {
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class ConvertTorchToLinalg
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: public ConvertTorchToLinalgBase<ConvertTorchToLinalg> {
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public:
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void getDependentDialects(DialectRegistry ®istry) const override {
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registry.insert<linalg::LinalgDialect>();
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registry.insert<memref::MemRefDialect>();
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registry.insert<math::MathDialect>();
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registry.insert<StandardOpsDialect>();
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registry.insert<tensor::TensorDialect>();
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}
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void runOnOperation() override {
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MLIRContext *context = &getContext();
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ConversionTarget target(*context);
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target.addLegalDialect<linalg::LinalgDialect, StandardOpsDialect,
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memref::MemRefDialect, math::MathDialect,
|
||
tensor::TensorDialect>();
|
||
|
||
TypeConverter typeConverter;
|
||
typeConverter.addConversion([](Type type) { return type; });
|
||
setupBackendTypeConversion(target, typeConverter);
|
||
|
||
RewritePatternSet patterns(context);
|
||
target.addIllegalOp<AtenMmOp>();
|
||
patterns.add<ConvertAtenMmOp>(typeConverter, context);
|
||
target.addIllegalOp<AtenLinearOp>();
|
||
patterns.add<ConvertAtenLinearOp>(typeConverter, context);
|
||
target.addIllegalOp<AtenBatchNormOp>();
|
||
patterns.add<ConvertAtenBatchNormOp>(typeConverter, context);
|
||
target.addIllegalOp<AtenTanhOp>();
|
||
patterns.add<ConvertUnaryOp>(typeConverter, context);
|
||
if (failed(applyPartialConversion(getOperation(), target,
|
||
std::move(patterns))))
|
||
return signalPassFailure();
|
||
}
|
||
};
|
||
} // namespace
|
||
|
||
std::unique_ptr<OperationPass<FuncOp>>
|
||
mlir::NPCOMP::createConvertTorchToLinalgPass() {
|
||
return std::make_unique<ConvertTorchToLinalg>();
|
||
}
|