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fix: break circular dependency to enable 'BUILD_SHARED_LIBS' (#4620)
This PR tries to close #3961. `Torch-MLIR` fails to build when using `-DBUILD_SHARED_LIBS=ON`. The root cause is a circular dependency between `TorchMLIRTorchUtils` and `TorchMLIRTorchDialect`, between `TorchMLIRConversionPasses` and `TorchMLIRTorchConversionPasses`, combined with many missing explicit link dependencies across conversion libraries. This commit does the following works: - Inline small utility functions used by `TorchMLIRTorchDialect` and move `squeezeTensor/unsqueezeTensor/sparsity` functions to `TorchOps.cpp` to make `TorchMLIRTorchDialect` not depend on `TorchMLIRTorchUtils`. - Relocate backend pipeline registration to conversion lib to break the circular dependency between `TorchMLIRConversionPasses` and `TorchMLIRTorchConversionPasses`. - Add many missing CMake `LINK_LIBS`. It's my first time to contribute to LLVM community and this project, any feedback is appreciated!
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Lines changed: 558 additions & 554 deletions

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‎include/torch-mlir/Dialect/Torch/IR/TorchOps.h‎

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Original file line numberDiff line numberDiff line change
@@ -370,6 +370,31 @@ inline int64_t getIntAttrAsSigned(IntegerAttr intAttr) {
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return intAttr.getValue().getSExtValue();
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}
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/// Create a new SparseTensorEncodingAttr based on the provided `attr`, but with
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/// a new dense level inserted at `dim`.
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FailureOr<Attribute> getSparsityWithDenseLTAtDim(Attribute attr, Value dim);
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/// Helper function to squeeze the input tensor at given dim.
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/// Return the squeezed tensor or failure.
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FailureOr<Value> squeezeTensor(PatternRewriter &rewriter, Operation *op,
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Location loc, int64_t dim, Value input);
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/// Helper function to unsqueeze the input tensor at given dim.
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/// Return the unsqueezed tensor or failure.
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FailureOr<Value> unsqueezeTensor(PatternRewriter &rewriter, Operation *op,
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Value input, Value dim);
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/// Helper function to get the list construct elements.
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/// The `elems` array is expected to be empty.
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/// Return true if the value `v` is defined by ListConstruct.
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bool getListConstructElements(Value v, SmallVectorImpl<Value> &elems);
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/// Returns the index indicated by `v` for a list of given `length`.
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/// If the index is negative, it is adjusted to `length` + `v`.
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/// `None` is returned the index is not an integer in the range [0,`length).
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std::optional<int64_t> matchLegalConstantIndexIntoListOfSize(Value v,
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int64_t length);
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} // namespace Torch
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} // namespace torch
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} // namespace mlir

‎include/torch-mlir/Dialect/Torch/Utils/SparsityUtils.h‎

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This file was deleted.

‎include/torch-mlir/Dialect/Torch/Utils/Utils.h‎

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Original file line numberDiff line numberDiff line change
@@ -18,19 +18,147 @@ namespace mlir {
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namespace torch {
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namespace Torch {
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21-
int64_t toPositiveDim(int64_t dim, int64_t inputRank);
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bool isValidDim(int64_t dim, int64_t inputRank);
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inline int64_t toPositiveDim(int64_t dim, int64_t inputRank) {
22+
return dim >= 0 ? dim : dim + inputRank;
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}
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inline bool isValidDim(int64_t dim, int64_t inputRank) {
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return dim >= 0 && dim < inputRank;
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}
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2329
Value toIntListConstruct(PatternRewriter &rewriter, Location loc,
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ArrayRef<int64_t> cstInput);
25-
bool getListConstructElements(Value v, SmallVectorImpl<Value> &elems);
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/// Returns the index indicated by `v` for a list of given `length`.
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/// If the index is negative, it is adjusted to `length` + `v`.
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/// `None` is returned the index is not an integer in the range [0,`length).
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std::optional<int64_t> matchLegalConstantIndexIntoListOfSize(Value v,
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int64_t length);
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torch_upstream::ScalarType getScalarTypeForType(Type type);
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FailureOr<Type> getTypeForScalarType(MLIRContext *context,
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torch_upstream::ScalarType dtypeInt);
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inline torch_upstream::ScalarType getScalarTypeForType(Type type) {
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if (isa<Float32Type>(type))
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return torch_upstream::ScalarType::Float;
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if (isa<Float64Type>(type))
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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.isSignedInteger(16))
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return torch_upstream::ScalarType::Short;
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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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if (type.isF16())
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return torch_upstream::ScalarType::Half;
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if (type.isUnsignedInteger(8))
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return torch_upstream::ScalarType::Byte;
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if (type.isSignedInteger(8))
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return torch_upstream::ScalarType::Char;
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if (isa<QUInt8Type>(type))
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return torch_upstream::ScalarType::QUInt8;
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if (isa<QInt8Type>(type))
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return torch_upstream::ScalarType::QInt8;
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if (isa<QInt16Type>(type))
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return torch_upstream::ScalarType::QInt16;
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if (isa<QInt32Type>(type))
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return torch_upstream::ScalarType::QInt32;
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if (isa<ComplexType>(type)) {
62+
mlir::Type complexElemType = cast<ComplexType>(type).getElementType();
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if (complexElemType.isF16())
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return torch_upstream::ScalarType::ComplexHalf;
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if (complexElemType.isF32())
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return torch_upstream::ScalarType::ComplexFloat;
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if (complexElemType.isF64())
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return torch_upstream::ScalarType::ComplexDouble;
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}
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if (isa<Float8E5M2Type>(type))
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return torch_upstream::ScalarType::Float8_e5m2;
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if (isa<Float8E4M3FNType>(type))
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return torch_upstream::ScalarType::Float8_e4m3fn;
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if (isa<Float8E5M2FNUZType>(type))
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return torch_upstream::ScalarType::Float8_e5m2fnuz;
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if (isa<Float8E4M3FNUZType>(type))
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return torch_upstream::ScalarType::Float8_e4m3fnuz;
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if (isa<Float8E8M0FNUType>(type))
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return torch_upstream::ScalarType::Float8_e8m0fnu;
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if (isa<Float4E2M1FNType>(type))
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return torch_upstream::ScalarType::Float4_e2m1fn_x2;
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std::string errorMsg = "Unhandled type in getScalarTypeForType: ";
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llvm::raw_string_ostream os(errorMsg);
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type.print(os);
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// os << "\nType ID: " << type.getTypeID();
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os << "\nType properties:";
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os << "\n Is integer: " << (type.isInteger() ? "yes" : "no");
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os << "\n Is float: "
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<< (type.isIntOrFloat() && !type.isInteger() ? "yes" : "no");
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os << "\n Is index: " << (type.isIndex() ? "yes" : "no");
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os << "\n Bit width: "
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<< (type.isIntOrFloat() ? std::to_string(type.getIntOrFloatBitWidth())
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: "N/A");
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os << "\n Is signless: " << (type.isSignlessInteger() ? "yes" : "no");
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os << "\n Is signed: " << (type.isSignedInteger() ? "yes" : "no");
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// special error message for unsigned integer
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if (type.isUnsignedInteger()) {
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os << "\n Is unsigned: yes";
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os << "\nUnsigned integer support is currently spotty. Please seeheck "
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"https://github.com/llvm/torch-mlir/issues/3720 "
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"for more details.";
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}
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llvm::report_fatal_error(llvm::StringRef(errorMsg));
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}
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inline FailureOr<Type>
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getTypeForScalarType(MLIRContext *context,
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torch_upstream::ScalarType dtypeInt) {
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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, mlir::IntegerType::Signed);
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case torch_upstream::ScalarType::Int:
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return IntegerType::get(context, 32, mlir::IntegerType::Signed);
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case torch_upstream::ScalarType::Short:
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return IntegerType::get(context, 16, mlir::IntegerType::Signed);
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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::BFloat16Type::get(context);
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case torch_upstream::ScalarType::Half:
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return mlir::Float16Type::get(context);
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case torch_upstream::ScalarType::Byte:
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return mlir::IntegerType::get(context, 8, mlir::IntegerType::Unsigned);
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case torch_upstream::ScalarType::Char:
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return mlir::IntegerType::get(context, 8, mlir::IntegerType::Signed);
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case torch_upstream::ScalarType::QUInt8:
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return QUInt8Type::get(context);
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case torch_upstream::ScalarType::QInt8:
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return QInt8Type::get(context);
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case torch_upstream::ScalarType::QInt16:
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return QInt16Type::get(context);
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case torch_upstream::ScalarType::QInt32:
137+
return QInt32Type::get(context);
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case torch_upstream::ScalarType::ComplexHalf:
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return mlir::ComplexType::get(Float16Type::get(context));
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case torch_upstream::ScalarType::ComplexFloat:
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return mlir::ComplexType::get(Float32Type::get(context));
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case torch_upstream::ScalarType::ComplexDouble:
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return mlir::ComplexType::get(Float64Type::get(context));
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case torch_upstream::ScalarType::Float8_e5m2:
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return Float8E5M2Type::get(context);
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case torch_upstream::ScalarType::Float8_e4m3fn:
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return Float8E4M3FNType::get(context);
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case torch_upstream::ScalarType::Float8_e5m2fnuz:
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return Float8E5M2FNUZType::get(context);
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case torch_upstream::ScalarType::Float8_e4m3fnuz:
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return Float8E4M3FNUZType::get(context);
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case torch_upstream::ScalarType::Float8_e8m0fnu:
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return Float8E8M0FNUType::get(context);
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case torch_upstream::ScalarType::Float4_e2m1fn_x2:
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return Float4E2M1FNType::get(context);
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case torch_upstream::ScalarType::Undefined:
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return failure();
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default:
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llvm::report_fatal_error("unhandled type for getTypeForScalarType");
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}
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}
34162

35163
Type getTypeForTorchType(
36164
MLIRContext *context, Type type,
@@ -75,7 +203,12 @@ bool isBuiltInType(Type type);
75203

76204
// Helper function to get rank of `Base tensor type`.
77205
// std::nullopt is returned if the tensorRank can't be determined.
78-
std::optional<unsigned> getTensorRank(Value tensor);
206+
inline std::optional<unsigned> getTensorRank(Value tensor) {
207+
BaseTensorType tensorType = cast<BaseTensorType>(tensor.getType());
208+
if (!tensorType.hasSizes())
209+
return std::nullopt;
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return tensorType.getSizes().size();
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}
79212

80213
// Helper function to get the number of elements in a tensor.
81214
std::optional<int64_t> getTensorNumel(Value tensor);
@@ -89,23 +222,23 @@ Value getConstantWithGivenDtypeAndValue(PatternRewriter &rewriter, Location loc,
89222
// return -1.
90223
int64_t getNumberOfElements(RankedTensorType inputType);
91224

92-
SmallVector<int64_t> makeShapeLLVMCompatible(ArrayRef<int64_t> shape);
225+
inline SmallVector<int64_t> makeShapeLLVMCompatible(ArrayRef<int64_t> shape) {
226+
SmallVector<int64_t> updatedShape(shape);
227+
int64_t kDynamic = ShapedType::kDynamic;
228+
for (unsigned i = 0; i < shape.size(); i++) {
229+
assert(shape[i] >= 0 || shape[i] == kUnknownSize);
230+
if (shape[i] == kUnknownSize)
231+
updatedShape[i] = kDynamic;
232+
}
233+
return updatedShape;
234+
}
235+
93236
SmallVector<int64_t> makeShapeTorchCompatible(ArrayRef<int64_t> shape);
94237

95238
ValueTensorType getTensorTypeFromShapeValues(ArrayRef<Value> shapes,
96239
Type dtype);
97240
Value getTensorDimSize(PatternRewriter &rewriter, Value tensor, int64_t dim);
98241

99-
// Helper function to squeeze the input tensor at given dim.
100-
// Return the squeezed tensor or failure.
101-
FailureOr<Value> squeezeTensor(PatternRewriter &rewriter, Operation *op,
102-
Location loc, int64_t dim, Value input);
103-
104-
// Helper function to unsqueeze the input tensor at given dim.
105-
// Return the unsqueezed tensor or failure.
106-
FailureOr<Value> unsqueezeTensor(PatternRewriter &rewriter, Operation *op,
107-
Value input, Value dim);
108-
109242
// In Dynamo import paths, we can assume that dynamic dimensions are strictly
110243
// quantities and are not ambiguous with '1' symbols that can be interpreted
111244
// to signal an expansion in various broadcasting scenarios. In the

‎lib/CAPI/CMakeLists.txt‎

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@@ -11,6 +11,7 @@ add_mlir_public_c_api_library(TorchMLIRCAPI
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ENABLE_AGGREGATION
1212

1313
LINK_LIBS PUBLIC
14+
MLIRCAPIIR
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MLIRIR
1516
MLIRSupport
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TorchMLIRTorchDialect

‎lib/Conversion/CMakeLists.txt‎

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@@ -20,7 +20,8 @@ set(linked_libs TorchMLIRTorchToArith
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TorchMLIRTorchToTensor
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TorchMLIRTorchToTMTensor
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TorchMLIRTorchConversionToMLProgram
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TorchMLIRConversionUtils)
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TorchMLIRConversionUtils
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TorchMLIRTorchConversionDialect)
2425
if(TORCH_MLIR_ENABLE_STABLEHLO)
2526
list(APPEND linked_libs TorchMLIRTorchToStablehlo)
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endif()

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