# RUN: %PYTHON %s | FileCheck %s from mlir.ir import * from mlir.dialects import builtin from mlir.dialects import func from mlir.dialects import linalg from mlir.dialects.linalg.opdsl.lang import * # This tests miscellaneous features of the emitter that are not tested by the # fill, matmul, convolution, or pooling tests. The features include: # - constant defined in the body # - fix/predefined types # - some math/arith functions, including abs, ceil, exp, floor, log, and negf # - custom op names. @linalg_structured_op def test_const(O=TensorDef(F32, S.M, S.N, output=True)): O[D.m, D.n] = TypeFn.cast_unsigned(F32, const(42)) + TypeFn.cast_unsigned( F32, const(2.3283064e-10) ) @linalg_structured_op def test_index(O=TensorDef(I32, S.M, S.N, output=True)): O[D.m, D.n] = TypeFn.cast_signed(I32, index(D.m)) + TypeFn.cast_signed( I32, index(D.n) ) @linalg_structured_op def elemwise_unary_poly( I=TensorDef(T), O=TensorDef(U, output=True), fun=UnaryFnAttrDef(default=UnaryFn.exp), cast=TypeFnAttrDef(default=TypeFn.cast_signed), ): O[None] = fun(cast(U, I[None])) @linalg_structured_op(op_name="custom_op_name") def non_default_op_name(I=TensorDef(T, S.N), O=TensorDef(T, S.N, output=True)): O[D.n] = I[D.n] with Context() as ctx, Location.unknown(): module = Module.create() f32 = F32Type.get() c32 = ComplexType.get(f32) i32 = IntegerType.get_signless(32) with InsertionPoint(module.body): # CHECK-LABEL: @test_f32_const # CHECK-DAG: %[[CST0:.+]] = arith.constant 42 : i64 # CHECK-DAG: %[[CST0_CAST:.+]] = arith.uitofp %[[CST0]] : i64 to f32 # CHECK-DAG: %[[CST1:.+]] = arith.constant 2.3283063999999999E-10 : f64 # CHECK-DAG: %[[CST1_CAST:.+]] = arith.truncf %[[CST1]] : f64 to f32 # CHECK-DAG: %[[SUM:.+]] = arith.addf %[[CST0_CAST]], %[[CST1_CAST]] : f32 # CHECK-NEXT: linalg.yield %[[SUM]] : f32 @func.FuncOp.from_py_func(RankedTensorType.get((4, 16), f32)) def test_f32_const(init_result): return test_const(outs=[init_result]) # CHECK-LABEL: @test_i32_index # CHECK-DAG: %[[IDX0:.+]] = linalg.index 0 : index # CHECK-DAG: %[[IDX1:.+]] = linalg.index 1 : index # CHECK-DAG: %[[IDX0_CAST:.+]] = arith.index_cast %[[IDX0]] : index to i32 # CHECK-DAG: %[[IDX1_CAST:.+]] = arith.index_cast %[[IDX1]] : index to i32 # CHECK-DAG: %[[SUM:.+]] = arith.addi %[[IDX0_CAST]], %[[IDX1_CAST]] : i32 # CHECK-NEXT: linalg.yield %[[SUM]] : i32 @func.FuncOp.from_py_func(RankedTensorType.get((4, 16), i32)) def test_i32_index(init_result): return test_index(outs=[init_result]) # CHECK-LABEL: @test_f32_elemwise_exp # CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[OUT:.+]]: f32) # CHECK-NEXT: %[[EXP:.+]] = math.exp %[[IN]] : f32 # CHECK-NEXT: linalg.yield %[[EXP]] : f32 # CHECK-NEXT: -> tensor<4x16xf32> @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), f32), RankedTensorType.get((4, 16), f32) ) def test_f32_elemwise_exp(input, init_result): return elemwise_unary_poly(input, outs=[init_result], fun=UnaryFn.exp) # CHECK-LABEL: @test_f32_elemwise_log # CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[OUT:.+]]: f32) # CHECK-NEXT: %[[LOG:.+]] = math.log %[[IN]] : f32 # CHECK-NEXT: linalg.yield %[[LOG]] : f32 # CHECK-NEXT: -> tensor<4x16xf32> @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), f32), RankedTensorType.get((4, 16), f32) ) def test_f32_elemwise_log(input, init_result): return elemwise_unary_poly(input, outs=[init_result], fun=UnaryFn.log) # CHECK-LABEL: @test_f32_elemwise_abs # CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[OUT:.+]]: f32) # CHECK-NEXT: %[[EXP:.+]] = math.absf %[[IN]] : f32 # CHECK-NEXT: linalg.yield %[[EXP]] : f32 # CHECK-NEXT: -> tensor<4x16xf32> @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), f32), RankedTensorType.get((4, 16), f32) ) def test_f32_elemwise_abs(input, init_result): return elemwise_unary_poly(input, outs=[init_result], fun=UnaryFn.abs) # CHECK-LABEL: @test_f32_elemwise_ceil # CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[OUT:.+]]: f32) # CHECK-NEXT: %[[EXP:.+]] = math.ceil %[[IN]] : f32 # CHECK-NEXT: linalg.yield %[[EXP]] : f32 # CHECK-NEXT: -> tensor<4x16xf32> @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), f32), RankedTensorType.get((4, 16), f32) ) def test_f32_elemwise_ceil(input, init_result): return elemwise_unary_poly(input, outs=[init_result], fun=UnaryFn.ceil) # CHECK-LABEL: @test_f32_elemwise_floor # CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[OUT:.+]]: f32) # CHECK-NEXT: %[[EXP:.+]] = math.floor %[[IN]] : f32 # CHECK-NEXT: linalg.yield %[[EXP]] : f32 # CHECK-NEXT: -> tensor<4x16xf32> @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), f32), RankedTensorType.get((4, 16), f32) ) def test_f32_elemwise_floor(input, init_result): return elemwise_unary_poly(input, outs=[init_result], fun=UnaryFn.floor) # CHECK-LABEL: @test_f32_elemwise_neg # CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[OUT:.+]]: f32) # CHECK-NEXT: %[[EXP:.+]] = arith.negf %[[IN]] : f32 # CHECK-NEXT: linalg.yield %[[EXP]] : f32 # CHECK-NEXT: -> tensor<4x16xf32> @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), f32), RankedTensorType.get((4, 16), f32) ) def test_f32_elemwise_neg(input, init_result): return elemwise_unary_poly(input, outs=[init_result], fun=UnaryFn.negf) # CHECK-LABEL: @test_c32_elemwise_neg # CHECK: ^{{.*}}(%[[IN:.+]]: complex, %[[OUT:.+]]: complex) # CHECK-NEXT: %[[EXP:.+]] = complex.neg %[[IN]] : complex # CHECK-NEXT: linalg.yield %[[EXP]] : complex # CHECK-NEXT: -> tensor<4x16xcomplex> @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), c32), RankedTensorType.get((4, 16), c32) ) def test_c32_elemwise_neg(input, init_result): return elemwise_unary_poly(input, outs=[init_result], fun=UnaryFn.negf) # Just check that we don't assert out on name mismatch. # CHECK-LABEL: @test_non_default_op_name @func.FuncOp.from_py_func( RankedTensorType.get((42,), f32), RankedTensorType.get((42,), f32) ) def test_non_default_op_name(input, init_result): return non_default_op_name(input, outs=[init_result]) print(module)