[LLVM] Update LLVM version to 21.0.0git #472
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WuXintong123
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Running standalone build for llvm doesn't install MLIRPythonCAPI library into build/tool/mlir, please make this link path configurable: |
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Also worth notice that, buddy-llc breaks after bump: $ buddy-llc forward-lowered.mlir.ll -mtriple=riscv32 -target-abi=ilp32f -mattr=+m,+f,+zve32f --filetype=obj -o forward-lowered.mlir.ll.o
#5 0x0000555556f05896 getFeatures(llvm::MCSubtargetInfo&, llvm::StringRef, llvm::StringRef, llvm::StringRef, llvm::ArrayRef<llvm::StringRef>, llvm::ArrayRef<llvm::SubtargetSubTypeKV>, llvm::ArrayRef<llvm::SubtargetFeatureKV>) (/nix/store/wcvck76nnpi9slmj4csksgp37df4sx09->
#6 0x0000555556f054bc llvm::MCSubtargetInfo::InitMCProcessorInfo(llvm::StringRef, llvm::StringRef, llvm::StringRef) (/nix/store/wcvck76nnpi9slmj4csksgp37df4sx09-buddy-mlir-unstable-2024-07-18/bin/buddy-llc+0x19b14bc)
#7 0x0000555556b38c77 llvm::TargetSubtargetInfo::TargetSubtargetInfo(llvm::Triple const&, llvm::StringRef, llvm::StringRef, llvm::StringRef, llvm::ArrayRef<llvm::StringRef>, llvm::ArrayRef<llvm::SubtargetFeatureKV>, llvm::ArrayRef<llvm::SubtargetSubTypeKV>, llvm::MCWrite>
#8 0x0000555555dd736c llvm::RISCVSubtarget::RISCVSubtarget(llvm::Triple const&, llvm::StringRef, llvm::StringRef, llvm::StringRef, llvm::StringRef, unsigned int, unsigned int, llvm::TargetMachine const&) (/nix/store/wcvck76nnpi9slmj4csksgp37df4sx09-buddy-mlir-unstable-20>
#9 0x0000555555dd9049 llvm::RISCVTargetMachine::getSubtargetImpl(llvm::Function const&) const (/nix/store/wcvck76nnpi9slmj4csksgp37df4sx09-buddy-mlir-unstable-2024-07-18/bin/buddy-llc+0x885049)I don't get such error using LLVM llc, so I guess there are missing linking libraries. |
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Met Python op error after bump: |
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Should be fixed with following patch. The new --- a/frontend/Python/ops/linalg.py 1970-01-01 08:00:01.000000000 +0800
+++ b/frontend/Python/ops/linalg.py 2025-04-09 21:59:09.917019537 +0800
@@ -1854,9 +1854,9 @@
output = tensor.EmptyOp(output_shape, mlir_dtype)
if not isinstance(input2.type, ir.RankedTensorType):
- input2 = tensor.SplatOp(tensor_type, input2).result
+ input2 = tensor.SplatOp(tensor_type, input2, []).result
if not isinstance(input3.type, ir.RankedTensorType):
- input3 = tensor.SplatOp(tensor_type, input3).result
+ input3 = tensor.SplatOp(tensor_type, input3, []).result
generic_map = ir.AffineMap.get_permutation(
[i for i in range(len(output_shape))]
@@ -2038,7 +2038,7 @@
input_shape = ir.RankedTensorType(input_tensor.type).shape
tensor_type = ir.RankedTensorType.get(input_shape, input_dtype)
scalar = arith.ConstantOp(input_dtype, node.args[1])
- rhs = tensor.SplatOp(tensor_type, scalar)
+ rhs = tensor.SplatOp(tensor_type, scalar, [])
if str(input_dtype).find("i") != -1:
cmp_op = arith.CmpIOp(4, input_tensor, rhs)
else:
@@ -2069,7 +2069,7 @@
tensor_type = ir.RankedTensorType.get(input_shape, input_dtype)
scalar = arith.ConstantOp(input_dtype, node.args[1])
- rhs = tensor.SplatOp(tensor_type, scalar)
+ rhs = tensor.SplatOp(tensor_type, scalar, [])
if str(input_dtype).find("i") != -1:
cmp_op = arith.CmpIOp(5, input_tensor, rhs)
@@ -2390,7 +2390,7 @@
scalar = arith.ConstantOp(input_dtype, float(node.args[1]))
else:
scalar = arith.ConstantOp(input_dtype, node.args[1])
- rhs = tensor.SplatOp(tensor_type, scalar)
+ rhs = tensor.SplatOp(tensor_type, scalar, [])
if str(input_dtype).find("i") != -1:
cmp_op = arith.CmpIOp(0, input_tensor, rhs)
else: |
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Now with following error: |
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Should be fixed with following patch --- a/frontend/Python/ops/linalg.py 1970-01-01 08:00:01.000000000 +0800
+++ b/frontend/Python/ops/linalg.py 1970-01-01 08:00:01.000000000 +0800
@@ -1166,7 +1166,7 @@
element = mlir_element_attr_get(dtype, 0.0)
attr = ir.DenseElementsAttr.get_splat(tensor_type, element)
matmul_result_buffer = arith.ConstantOp(tensor_type, attr).result
- op = linalg.matmul(input1, input2, outs=[matmul_result_buffer])
+ op = linalg.matmul(input1, input2, outputs=[matmul_result_buffer])
return op
@@ -1186,7 +1186,7 @@
element = mlir_element_attr_get(dtype, 0.0)
attr = ir.DenseElementsAttr.get_splat(tensor_type, element)
result_buffer = arith.ConstantOp(tensor_type, attr).result
- op = linalg.matmul_transpose_b(input1, input2, outs=[result_buffer])
+ op = linalg.matmul_transpose_b(input1, input2, outputs=[result_buffer])
return op
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Now with error: |
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