mirror of https://github.com/llvm/torch-mlir
131 lines
4.5 KiB
C++
131 lines
4.5 KiB
C++
//===----------------------------------------------------------------------===//
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//
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// This file is licensed 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/Dialect/Torch/Utils/Utils.h"
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#include "mlir/IR/BuiltinDialect.h"
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#include "torch-mlir/Dialect/Torch/IR/TorchOps.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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int64_t Torch::toPositiveDim(int64_t dim, int64_t inputRank) {
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return dim >= 0 ? dim : dim + inputRank;
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}
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bool Torch::isValidDim(int64_t dim, int64_t inputRank) {
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return dim >= 0 && dim < inputRank;
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}
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llvm::Optional<int64_t>
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Torch::matchLegalConstantIndexIntoListOfSize(Value v, int64_t length) {
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int64_t dim;
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if (!matchPattern(v, m_TorchConstantInt(&dim)))
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return llvm::None;
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dim = toPositiveDim(dim, length);
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if (!isValidDim(dim, length))
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return llvm::None;
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return dim;
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}
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bool Torch::getListConstructElements(Value v, SmallVectorImpl<Value> &elems) {
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auto listConstruct = v.getDefiningOp<PrimListConstructOp>();
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if (!listConstruct)
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return false;
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elems = llvm::to_vector<4>(listConstruct.elements());
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return true;
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}
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torch_upstream::ScalarType Torch::getScalarTypeForType(Type type) {
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if (type.isa<Float32Type>())
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return torch_upstream::ScalarType::Float;
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if (type.isa<Float64Type>())
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return torch_upstream::ScalarType::Double;
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if (type.isSignedInteger(64))
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return torch_upstream::ScalarType::Long;
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if (type.isSignedInteger(32))
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return torch_upstream::ScalarType::Int;
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if (type.isSignlessInteger(1))
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return torch_upstream::ScalarType::Bool;
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if (type.isBF16())
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return torch_upstream::ScalarType::BFloat16;
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llvm::report_fatal_error("unhandled type for getScalarTypeForType");
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}
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Type Torch::getTypeForScalarType(
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MLIRContext *context, torch_upstream::ScalarType dtypeInt,
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mlir::IntegerType::SignednessSemantics signedness) {
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switch (dtypeInt) {
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case torch_upstream::ScalarType::Float:
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return Float32Type::get(context);
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case torch_upstream::ScalarType::Double:
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return Float64Type::get(context);
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case torch_upstream::ScalarType::Long:
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return IntegerType::get(context, 64, signedness);
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case torch_upstream::ScalarType::Int:
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return IntegerType::get(context, 32, signedness);
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case torch_upstream::ScalarType::Bool:
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return IntegerType::get(context, 1);
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case torch_upstream::ScalarType::BFloat16:
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return mlir::FloatType::getBF16(context);
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default:
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return Type();
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}
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}
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Type Torch::getTorchTypeForScalarType(MLIRContext *context,
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torch_upstream::ScalarType dtypeInt) {
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switch (dtypeInt) {
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case torch_upstream::ScalarType::Double:
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return Torch::FloatType::get(context);
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case torch_upstream::ScalarType::Long:
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return Torch::IntType::get(context);
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default:
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llvm::report_fatal_error(
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"Unsupported scalar type to Torch type conversion");
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}
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}
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Value Torch::getDtypeIntValueForType(PatternRewriter &rewriter, Location loc,
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Type dtype) {
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int intType = (int)getScalarTypeForType(dtype);
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return rewriter.create<ConstantIntOp>(loc,
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rewriter.getI64IntegerAttr(intType));
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}
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// Helper to convert a tensor to a specific scalar type.
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Value Torch::convertTensorToDtype(PatternRewriter &rewriter, Location loc,
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Value input, Type dtype) {
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BaseTensorType origType = input.getType().cast<BaseTensorType>();
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Type newType = origType.getWithSizesAndDtype(origType.getSizes(), dtype);
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// `convertIntVal` contains the corresponding integer for the dtype which is
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// used by the aten.to.dtype op.
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Value convertIntVal = getDtypeIntValueForType(rewriter, loc, dtype);
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Value falseVal = rewriter.create<ConstantBoolOp>(loc, false);
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Value noneVal = rewriter.create<ConstantNoneOp>(loc);
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Value converted = rewriter.create<AtenToDtypeOp>(
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loc, newType, input, convertIntVal, falseVal, falseVal, noneVal);
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return converted;
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}
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bool Torch::isBuiltInType(Type type) {
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return isa<BuiltinDialect>(type.getDialect());
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}
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int Torch::getTensorRank(Value tensor) {
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int tensorRank = -1;
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BaseTensorType tensorType = tensor.getType().cast<BaseTensorType>();
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if (tensorType.hasSizes()) {
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ArrayRef<int64_t> tensorShape = tensorType.getSizes();
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tensorRank = tensorShape.size();
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}
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return tensorRank;
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}
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