torch-mlir/lib/Dialect/Torch/Utils/Utils.cpp

//===----------------------------------------------------------------------===//
//
// This file is licensed 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/Dialect/Torch/Utils/Utils.h"
#include "mlir/IR/BuiltinDialect.h"
#include "torch-mlir/Dialect/Torch/IR/TorchOps.h"

using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;

int64_t Torch::toPositiveDim(int64_t dim, int64_t inputRank) {
  return dim >= 0 ? dim : dim + inputRank;
}

bool Torch::isValidDim(int64_t dim, int64_t inputRank) {
  return dim >= 0 && dim < inputRank;
}

llvm::Optional<int64_t>
Torch::matchLegalConstantIndexIntoListOfSize(Value v, int64_t length) {
  int64_t dim;
  if (!matchPattern(v, m_TorchConstantInt(&dim)))
    return llvm::None;
  dim = toPositiveDim(dim, length);
  if (!isValidDim(dim, length))
    return llvm::None;
  return dim;
}

bool Torch::getListConstructElements(Value v, SmallVectorImpl<Value> &elems) {
  auto listConstruct = v.getDefiningOp<PrimListConstructOp>();
  if (!listConstruct)
    return false;
  elems = llvm::to_vector<4>(listConstruct.elements());
  return true;
}

torch_upstream::ScalarType Torch::getScalarTypeForType(Type type) {
  if (type.isa<Float32Type>())
    return torch_upstream::ScalarType::Float;
  if (type.isa<Float64Type>())
    return torch_upstream::ScalarType::Double;
  if (type.isSignedInteger(64))
    return torch_upstream::ScalarType::Long;
  if (type.isSignedInteger(32))
    return torch_upstream::ScalarType::Int;
  if (type.isSignlessInteger(1))
    return torch_upstream::ScalarType::Bool;
  if (type.isBF16())
    return torch_upstream::ScalarType::BFloat16;
  if (type.isF16())
    return torch_upstream::ScalarType::Half;
  llvm::report_fatal_error("unhandled type for getScalarTypeForType");
}

Type Torch::getTypeForTorchType(
    MLIRContext *context, Type type,
    mlir::IntegerType::SignednessSemantics signedness) {
  if (type.isa<Torch::IntType>())
    return IntegerType::get(context, 64, signedness);
  if (type.isa<Torch::FloatType>())
    return Float64Type::get(context);
  llvm::report_fatal_error("unhandled type for getTypeForTorchType");
}

Type Torch::getTypeForScalarType(
    MLIRContext *context, torch_upstream::ScalarType dtypeInt,
    mlir::IntegerType::SignednessSemantics signedness) {
  switch (dtypeInt) {
  case torch_upstream::ScalarType::Float:
    return Float32Type::get(context);
  case torch_upstream::ScalarType::Double:
    return Float64Type::get(context);
  case torch_upstream::ScalarType::Long:
    return IntegerType::get(context, 64, signedness);
  case torch_upstream::ScalarType::Int:
    return IntegerType::get(context, 32, signedness);
  case torch_upstream::ScalarType::Bool:
    return IntegerType::get(context, 1);
  case torch_upstream::ScalarType::BFloat16:
    return mlir::FloatType::getBF16(context);
  case torch_upstream::ScalarType::Half:
    return mlir::FloatType::getF16(context);
  default:
    return Type();
  }
}

Type Torch::getTorchTypeForScalarType(MLIRContext *context,
                                      torch_upstream::ScalarType dtypeInt) {
  switch (dtypeInt) {
  case torch_upstream::ScalarType::Double:
    return Torch::FloatType::get(context);
  case torch_upstream::ScalarType::Long:
    return Torch::IntType::get(context);
  default:
    llvm::report_fatal_error(
        "Unsupported scalar type to Torch type conversion");
  }
}

Value Torch::getDtypeIntValueForType(PatternRewriter &rewriter, Location loc,
                                     Type dtype) {
  int intType = (int)getScalarTypeForType(dtype);
  return rewriter.create<ConstantIntOp>(loc,
                                        rewriter.getI64IntegerAttr(intType));
}

// Helper to convert a tensor to a specific scalar type.
Value Torch::convertTensorToDtype(PatternRewriter &rewriter, Location loc,
                                  Value input, Type dtype) {
  BaseTensorType origType = input.getType().cast<BaseTensorType>();
  Type newType = origType.getWithSizesAndDtype(origType.getSizes(), dtype);
  // `convertIntVal` contains the corresponding integer for the dtype which is
  // used by the aten.to.dtype op.
  Value convertIntVal = getDtypeIntValueForType(rewriter, loc, dtype);
  Value falseVal = rewriter.create<ConstantBoolOp>(loc, false);
  Value noneVal = rewriter.create<ConstantNoneOp>(loc);
  Value converted = rewriter.create<AtenToDtypeOp>(
      loc, newType, input, convertIntVal, falseVal, falseVal, noneVal);
  return converted;
}

bool Torch::isBuiltInType(Type type) {
  return isa<BuiltinDialect>(type.getDialect());
}

int Torch::getTensorRank(Value tensor) {
  int tensorRank = -1;
  BaseTensorType tensorType = tensor.getType().cast<BaseTensorType>();

  if (tensorType.hasSizes()) {
    ArrayRef<int64_t> tensorShape = tensorType.getSizes();
    tensorRank = tensorShape.size();
  }
  return tensorRank;
}

bool Torch::isViewLikeOp(Operation *op) {
  // AtenContiguousOp might return a view, so this is conservatively
  // correct. We could potentially be more precise and identify the cases
  // that it does not return a view and treat those as having value
  // semantics.
  return isa<AtenBroadcastToOp, AtenContiguousOp, AtenDetachOp, AtenExpandAsOp,
             AtenExpandOp, AtenFlattenUsingIntsOp, AtenPermuteOp, AtenReshapeOp,
             Aten_ReshapeAliasOp, AtenSelectIntOp, AtenSliceTensorOp,
             AtenSqueezeDimOp, AtenSqueezeOp, AtenTOp, AtenToDtypeOp,
             AtenTransposeIntOp, AtenUnsqueezeOp, AtenViewOp,
             TensorStaticInfoCastOp, AtenToDtypeLayoutOp, AtenNumpyTOp,
             AtenNarrowOp, AtenToDeviceOp>(op);
}