torch-mlir/lib/Conversion/Utils/Utils.cpp

//===----------------------------------------------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// Also available under a BSD-style license. See LICENSE.
//
//===----------------------------------------------------------------------===//

#include "torch-mlir/Conversion/Utils/Utils.h"

#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Transforms/DialectConversion.h"
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"

namespace mlir {
namespace torch {
namespace Torch {

LogicalResult verifyLinalgCompatibleTypes(Operation *op,
                                          PatternRewriter &rewriter) {
  // Check the value tensor is ranked as expected by Linalg.
  // TODO: Remove this check but use a separate verification pass to verify the
  // invariants expected by later passes.
  auto isValidLinalgType = [](Type type) {
    auto tensor = type.dyn_cast<ValueTensorType>();
    return !tensor ||
           tensor.toBuiltinTensor().dyn_cast_or_null<RankedTensorType>();
  };

  bool valid = llvm::all_of(op->getOperandTypes(), isValidLinalgType) &&
               llvm::all_of(op->getResultTypes(), isValidLinalgType);
  if (!valid)
    return rewriter.notifyMatchFailure(op, "type cannot be lowered to linalg");
  return success();
}

LogicalResult checkNotNone(PatternRewriter &rewriter, Operation *op, Value v) {
  Type type = v.getType();
  if (type.isa<OptionalType>() || type.isa<Torch::NoneType>() ||
      type.isa<mlir::NoneType>())
    return rewriter.notifyMatchFailure(op, "unimplemented None type arg");
  return success();
}

// Generate IR: dim = dim >= 0 ? dim : dim + inputRank
Value toPositiveDimDynamic(OpBuilder &b, Location loc, Value dim,
                           Value inputRank) {
  assert(dim.getType().isa<IntegerType>() &&
         "dim arg of toPositiveDim must be integer type");
  Value dimAddInputRank = b.create<arith::AddIOp>(loc, dim, inputRank);
  Value cst0 =
      b.create<arith::ConstantOp>(loc, b.getZeroAttr(inputRank.getType()));
  Value predDimGEZero =
      b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::sge, dim, cst0);
  Value dimInt =
      b.create<arith::SelectOp>(loc, predDimGEZero, dim, dimAddInputRank);
  return dimInt;
}

// Generate IR: assert(dim >= 0 && dim < inputRank)
void assertIsValidDim(OpBuilder &b, Location loc, Value dim, Value inputRank) {
  assert(dim.getType().isa<IntegerType>() &&
         "dim arg of assertIsValidDim must be integer type");
  Value cst0 =
      b.create<arith::ConstantOp>(loc, b.getZeroAttr(inputRank.getType()));
  Value predGEZero =
      b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::sge, dim, cst0);
  b.create<cf::AssertOp>(
      loc, predGEZero, b.getStringAttr("dim must be greater or equal to zero"));
  Value predLTInputRank =
      b.create<arith::CmpIOp>(loc, arith::CmpIPredicate::slt, dim, inputRank);
  b.create<cf::AssertOp>(loc, predLTInputRank,
                         b.getStringAttr("dim must be smaller than inputRank"));
}

// Hack to deal with the Torch list type arguments which is not supported end
// to end. Constant values can be be extracted directly and non constant
// list values are not supported.
// TODO: loose this constraint when properly support list type
bool isConstantIntListMatching(Value value, SmallVectorImpl<int64_t> &expects) {
  SmallVector<int64_t> intValues;
  if (!matchPattern(value, m_TorchConstantIntList(intValues)))
    return false;

  if (intValues.size() != expects.size())
    return false;

  for (auto it : llvm::zip(intValues, expects)) {
    if (std::get<0>(it) != std::get<1>(it))
      return false;
  }
  return true;
}

void checkDimEqualHelper(OpBuilder &b, Location loc, Value lhsDim,
                         Value rhsDim) {
  Type lhsType = lhsDim.getType();
  Type rhsType = rhsDim.getType();
  auto checkIntOrIndex = [](Type type) {
    assert(type.isa<IntegerType>() ||
           type.isa<IndexType>() && "must be either integer or index type");
  };
  checkIntOrIndex(lhsType);
  checkIntOrIndex(rhsType);
  Value lhsDimInt =
      lhsType.isIndex() ? castIndexToInt64(b, loc, lhsDim) : lhsDim;
  Value rhsDimInt =
      rhsType.isIndex() ? castIndexToInt64(b, loc, rhsDim) : rhsDim;
  Value contractingDimEqual = b.create<arith::CmpIOp>(
      loc, arith::CmpIPredicate::eq, lhsDimInt, rhsDimInt);
  b.create<cf::AssertOp>(loc, contractingDimEqual,
                         b.getStringAttr("mismatching contracting dimension"));
}

// Creates a tensor with required `sizes` and `elemTy` and fills it with
// initElem.
Value createInitTensor(OpBuilder &b, Location loc, ValueRange sizes,
                       Type elemTy, Value initElem) {
  Value initTensor = b.create<linalg::InitTensorOp>(loc, sizes, elemTy);
  return b.create<linalg::FillOp>(loc, initElem, initTensor).getResult(0);
}

Value createZeroInitTensor(OpBuilder &b, Location loc, ValueRange sizes,
                           Type elemTy) {
  Value initTensor = b.create<linalg::InitTensorOp>(loc, sizes, elemTy);
  RankedTensorType type = initTensor.getType().cast<RankedTensorType>();
  Value c0 =
      b.create<arith::ConstantOp>(loc, b.getZeroAttr(type.getElementType()));
  return b.create<linalg::FillOp>(loc, c0, initTensor).getResult(0);
}

Value castIntToIndex(OpBuilder &b, Location loc, Value v) {
  assert(v.getType().isa<IntegerType>() && "must be called with integer type");
  return b.create<arith::IndexCastOp>(loc, b.getIndexType(), v);
}

Value castIndexToInt64(OpBuilder &b, Location loc, Value idx) {
  assert(idx.getType().isa<IndexType>() && "must be called with integer type");
  return b.create<arith::IndexCastOp>(loc, b.getI64Type(), idx);
}

Value getDimOp(OpBuilder &b, Location loc, Value v, int dim) {
  return b.createOrFold<tensor::DimOp>(loc, v, dim);
}

SmallVector<Value> getTensorSizesUntilDim(OpBuilder &b, Location loc,
                                          Value tensor, int dim) {
  RankedTensorType type = tensor.getType().cast<RankedTensorType>();
  assert(dim < type.getRank() &&
         "The given dim must be smaller than tensor rank");
  (void)type;
  SmallVector<Value> sizes;
  for (int i = 0; i <= dim; i++)
    sizes.push_back(getDimOp(b, loc, tensor, i));
  return sizes;
}

SmallVector<Value> getTensorSizes(OpBuilder &b, Location loc, Value tensor) {
  RankedTensorType type = tensor.getType().cast<RankedTensorType>();
  return getTensorSizesUntilDim(b, loc, tensor, type.getRank() - 1);
}

Value getTensorSize(OpBuilder &b, Location loc, Value tensor) {
  SmallVector<Value> sizes(getTensorSizes(b, loc, tensor));
  Value productResult = b.create<arith::ConstantOp>(loc, b.getIndexAttr(1));
  for (Value size : sizes)
    productResult = b.create<arith::MulIOp>(loc, productResult, size);
  return castIndexToInt64(b, loc, productResult);
}

// Creates a constant of type `elemType` with value `val`.
Value getConstant(OpBuilder &b, Location loc, int64_t val, Type elemType) {
  Attribute attr = {};
  if (elemType.isa<mlir::FloatType>())
    attr = b.getFloatAttr(elemType, val);
  if (elemType.isa<mlir::IndexType>())
    attr = b.getIndexAttr(val);
  if (elemType.isa<mlir::IntegerType>())
    attr = b.getIntegerAttr(
        elemType, APInt(elemType.cast<IntegerType>().getWidth(), val));
  if (!attr)
    return nullptr;
  return b.create<arith::ConstantOp>(loc, elemType, attr);
}

SmallVector<Value> getAsConstantIntValues(OpBuilder &b, Location loc,
                                          SmallVectorImpl<int64_t> &ints) {
  return llvm::to_vector<4>(llvm::map_range(ints, [&](int64_t val) -> Value {
    return b.create<arith::ConstantOp>(loc,
                                       b.getIntegerAttr(b.getI64Type(), val));
  }));
}

SmallVector<Value> getAsConstantIndexValues(OpBuilder &b, Location loc,
                                            SmallVectorImpl<int64_t> &ints) {
  return llvm::to_vector<4>(llvm::map_range(ints, [&](int64_t val) -> Value {
    return b.create<arith::ConstantOp>(loc, b.getIndexAttr(val));
  }));
}

// This is a temporary solution to deal with types that are not fully supported
// like list, dict. For those container tyes, this helper can be used to
// convert their elements to valid target type.
// TODO: remove this when list gets full support.
SmallVector<Value> getTypeConvertedValues(OpBuilder &b, Location loc,
                                          TypeConverter *converter,
                                          SmallVectorImpl<Value> &vs) {
  return llvm::to_vector<4>(llvm::map_range(vs, [&](Value v) {
    return converter->materializeTargetConversion(
        b, loc, converter->convertType(v.getType()), v);
  }));
}

// Convert a scalar value to the target type. The scalar value can be an element
// from a tensor or a scalar in the pytorch dialect. Both the scalar and dtype
// should be converted builtin types.
Value convertScalarToDtype(OpBuilder &b, Location loc, Value scalar,
                           Type dtype) {
  Type scalarType = scalar.getType();
  if (scalarType == dtype)
    return scalar;

  // TODO: For the byte(ui8) or char(i8) case, we need the unconverted dtype to
  // be able to know if we need signed or unsigned conversion.
  auto isByteOrChar = [](Type type) {
    if (auto integerTy = type.dyn_cast<mlir::IntegerType>()) {
      return integerTy.getWidth() == 8;
    }
    return false;
  };

  if (isByteOrChar(scalarType) || isByteOrChar(dtype) ||
      dtype.isSignlessInteger(1)) {
    // TODO: Handle to-boolean conversion(from-boolean conversion is handled).
    mlir::emitError(loc)
        << "unsupported byte, char or bool type for convertScalarToDtype "
        << scalarType << "(scalar type) -> " << dtype << "(dtype)";
    return nullptr;
  }

  if (auto dtypeFloat = dtype.dyn_cast<mlir::FloatType>()) {
    if (auto scalarFloat = scalarType.dyn_cast<mlir::FloatType>()) {
      if (scalarFloat.getWidth() > dtypeFloat.getWidth())
        return b.create<arith::TruncFOp>(loc, dtype, scalar);
      // Only scalarFloat width < dtypeFloat width can reach here.
      return b.create<arith::ExtFOp>(loc, dtype, scalar);
    }
    assert(scalarType.isa<mlir::IntegerType>());
    if (scalarType.isSignlessInteger(1))
      return b.create<arith::UIToFPOp>(loc, dtype, scalar);
    // It's safe to use SIToFPOp because ui8/si8 are the only ones where
    // unsigned handling is needed, and we checked for that case above.
    return b.create<arith::SIToFPOp>(loc, dtype, scalar);
  }

  if (auto dtypeInteger = dtype.dyn_cast<mlir::IntegerType>()) {
    if (auto scalarFloat = scalarType.dyn_cast<mlir::FloatType>())
      return b.create<arith::FPToSIOp>(loc, dtype, scalar);
    assert(scalarType.isa<mlir::IntegerType>());
    auto scalarInteger = scalarType.cast<mlir::IntegerType>();
    if (scalarInteger.getWidth() > dtypeInteger.getWidth())
      return b.create<arith::TruncIOp>(loc, dtype, scalar);
    if (scalarType.isSignlessInteger(1))
      return b.create<arith::ExtUIOp>(loc, dtype, scalar);
    // Only scalarInteger width < dtypeInteger width can reach here.
    // It's safe to use ExtSIOp here because ui8/si8 are the only ones where
    // unsigned handling is needed, and we checked for that case above.
    return b.create<arith::ExtSIOp>(loc, dtype, scalar);
  }

  llvm_unreachable("convertScalarToDtype should handle all the types");
}

// Return the number of elements of a tensor if the shape is static; otherwise,
// return -1.
int64_t getNumberOfElements(RankedTensorType inputType) {
  if (!inputType.hasStaticShape())
    return -1;
  ArrayRef<int64_t> inputShape = inputType.getShape();
  int64_t numel = 1;
  for (int64_t i = 0; i < inputType.getRank(); i++)
    numel *= inputShape[i];
  return numel;
}

} // namespace Torch
} // namespace torch
} // namespace mlir