# 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 import tensor from mlir.dialects.linalg.opdsl.lang import * T1 = TV.T1 T2 = TV.T2 @linalg_structured_op def matmul_mono( A=TensorDef(T, S.M, S.K), B=TensorDef(T, S.K, S.N), C=TensorDef(T, S.M, S.N, output=True), ): domain(D.m, D.n, D.k) C[D.m, D.n] += A[D.m, D.k] * B[D.k, D.n] @linalg_structured_op def matmul_poly( A=TensorDef(T1, S.M, S.K), B=TensorDef(T2, S.K, S.N), C=TensorDef(U, S.M, S.N, output=True), cast=TypeFnAttrDef(default=TypeFn.cast_signed), ): domain(D.m, D.n, D.k) C[D.m, D.n] += cast(U, A[D.m, D.k]) * cast(U, B[D.k, D.n]) with Context() as ctx, Location.unknown(): module = Module.create() f16 = F16Type.get() f32 = F32Type.get() f64 = F64Type.get() i8 = IntegerType.get_signless(8) i16 = IntegerType.get_signless(16) i32 = IntegerType.get_signless(32) with InsertionPoint(module.body): # Multiplication indexing maps. We verify only the indexing maps of the # first multiplication and then do additional tests on casting and body # generation behavior. # CHECK: #[[$MUL_MAP_A:.+]] = affine_map<(d0, d1, d2) -> (d0, d2)> # CHECK: #[[$MUL_MAP_B:.+]] = affine_map<(d0, d1, d2) -> (d2, d1)> # CHECK: #[[$MUL_MAP_C:.+]] = affine_map<(d0, d1, d2) -> (d0, d1)> # CHECK-LABEL: func @test_matmul_mono # CHECK-SAME: %[[A:.+]]: tensor<4x16xf32> # CHECK-SAME: %[[B:.+]]: tensor<16x8xf32> # CHECK: %[[INITC:.+]] = tensor.empty() : tensor<4x8xf32> # CHECK: linalg.generic # CHECK-SAME: indexing_maps = [#[[$MUL_MAP_A]], #[[$MUL_MAP_B]], #[[$MUL_MAP_C]]] # CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"] # CHECK-SAME: ins(%[[A]], %[[B]] # CHECK-SAME: outs(%[[INITC]] @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), f32), RankedTensorType.get((16, 8), f32) ) def test_matmul_mono(lhs, rhs): init_result = tensor.empty([4, 8], f32) return matmul_mono(lhs, rhs, outs=[init_result]) # CHECK-LABEL: @test_i8i8i32_matmul # CHECK: ^{{.*}}(%[[A_ARG:.+]]: i8, %[[B_ARG:.+]]: i8, %[[C_ARG:.+]]: i32) # CHECK-NEXT: %[[A_CAST:.+]] = arith.extsi %[[A_ARG]] : i8 to i32 # CHECK-NEXT: %[[B_CAST:.+]] = arith.extsi %[[B_ARG]] : i8 to i32 # CHECK-NEXT: %[[MUL:.+]] = arith.muli %[[A_CAST]], %[[B_CAST]] : i32 # CHECK-NEXT: %[[ADD:.+]] = arith.addi %[[C_ARG]], %[[MUL]] : i32 # CHECK-NEXT: linalg.yield %[[ADD]] : i32 # CHECK-NEXT: -> tensor<4x8xi32> @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i8), RankedTensorType.get((4, 8), i32), ) def test_i8i8i32_matmul(lhs, rhs, init_result): return matmul_poly(lhs, rhs, outs=[init_result]) # CHECK-LABEL: @test_i8i8i32_matmul_unsigned # CHECK: = arith.extui # CHECK: = arith.extui @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i8), RankedTensorType.get((4, 8), i32), ) def test_i8i8i32_matmul_unsigned(lhs, rhs, init_result): return matmul_poly(lhs, rhs, outs=[init_result], cast=TypeFn.cast_unsigned) # CHECK-LABEL: @test_i8i16i32_matmul # CHECK: ^{{.*}}(%[[A_ARG:.+]]: i8, %[[B_ARG:.+]]: i16, %[[C_ARG:.+]]: i32) # CHECK-NEXT: %[[A_CAST:.+]] = arith.extsi %[[A_ARG]] : i8 to i32 # CHECK-NEXT: %[[B_CAST:.+]] = arith.extsi %[[B_ARG]] : i16 to i32 # CHECK-NEXT: %[[MUL:.+]] = arith.muli %[[A_CAST]], %[[B_CAST]] : i32 # CHECK-NEXT: %[[ADD:.+]] = arith.addi %[[C_ARG]], %[[MUL]] : i32 # CHECK-NEXT: linalg.yield %[[ADD]] : i32 # CHECK-NEXT: -> tensor<4x8xi32> @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i16), RankedTensorType.get((4, 8), i32), ) def test_i8i16i32_matmul(lhs, rhs, init_result): return matmul_poly(lhs, rhs, outs=[init_result]) # CHECK-LABEL: @test_i32i32i16_matmul # CHECK: ^{{.*}}(%[[A_ARG:.+]]: i32, %[[B_ARG:.+]]: i32, %[[C_ARG:.+]]: i16) # CHECK-NEXT: %[[A_CAST:.+]] = arith.trunci %[[A_ARG]] : i32 to i16 # CHECK-NEXT: %[[B_CAST:.+]] = arith.trunci %[[B_ARG]] : i32 to i16 # CHECK-NEXT: %[[MUL:.+]] = arith.muli %[[A_CAST]], %[[B_CAST]] : i16 # CHECK-NEXT: %[[ADD:.+]] = arith.addi %[[C_ARG]], %[[MUL]] : i16 # CHECK-NEXT: linalg.yield %[[ADD]] : i16 # CHECK-NEXT: -> tensor<4x8xi16> @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), i32), RankedTensorType.get((16, 8), i32), RankedTensorType.get((4, 8), i16), ) def test_i32i32i16_matmul(lhs, rhs, init_result): return matmul_poly(lhs, rhs, outs=[init_result]) # CHECK-LABEL: @test_i8i8f32_matmul # CHECK: ^{{.*}}(%[[A_ARG:.+]]: i8, %[[B_ARG:.+]]: i8, %[[C_ARG:.+]]: f32) # CHECK-NEXT: %[[A_CAST:.+]] = arith.sitofp %[[A_ARG]] : i8 to f32 # CHECK-NEXT: %[[B_CAST:.+]] = arith.sitofp %[[B_ARG]] : i8 to f32 # CHECK-NEXT: %[[MUL:.+]] = arith.mulf %[[A_CAST]], %[[B_CAST]] : f32 # CHECK-NEXT: %[[ADD:.+]] = arith.addf %[[C_ARG]], %[[MUL]] : f32 # CHECK-NEXT: linalg.yield %[[ADD]] : f32 # CHECK-NEXT: -> tensor<4x8xf32> @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i8), RankedTensorType.get((4, 8), f32), ) def test_i8i8f32_matmul(lhs, rhs, init_result): return matmul_poly(lhs, rhs, outs=[init_result]) # CHECK-LABEL: @test_i8i8f32_matmul_unsigned # CHECK: = arith.uitofp # CHECK: = arith.uitofp @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), i8), RankedTensorType.get((16, 8), i8), RankedTensorType.get((4, 8), f32), ) def test_i8i8f32_matmul_unsigned(lhs, rhs, init_result): return matmul_poly(lhs, rhs, outs=[init_result], cast=TypeFn.cast_unsigned) # CHECK-LABEL: @test_f16f16f32_matmul # CHECK: ^{{.*}}(%[[A_ARG:.+]]: f16, %[[B_ARG:.+]]: f16, %[[C_ARG:.+]]: f32) # CHECK-NEXT: %[[A_CAST:.+]] = arith.extf %[[A_ARG]] : f16 to f32 # CHECK-NEXT: %[[B_CAST:.+]] = arith.extf %[[B_ARG]] : f16 to f32 # CHECK-NEXT: %[[MUL:.+]] = arith.mulf %[[A_CAST]], %[[B_CAST]] : f32 # CHECK-NEXT: %[[ADD:.+]] = arith.addf %[[C_ARG]], %[[MUL]] : f32 # CHECK-NEXT: linalg.yield %[[ADD]] : f32 # CHECK-NEXT: -> tensor<4x8xf32> @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), f16), RankedTensorType.get((16, 8), f16), RankedTensorType.get((4, 8), f32), ) def test_f16f16f32_matmul(lhs, rhs, init_result): return matmul_poly(lhs, rhs, outs=[init_result]) # CHECK-LABEL: @test_f64f64f32_matmul # CHECK: ^{{.*}}(%[[A_ARG:.+]]: f64, %[[B_ARG:.+]]: f64, %[[C_ARG:.+]]: f32) # CHECK-NEXT: %[[A_CAST:.+]] = arith.truncf %[[A_ARG]] : f64 to f32 # CHECK-NEXT: %[[B_CAST:.+]] = arith.truncf %[[B_ARG]] : f64 to f32 # CHECK-NEXT: %[[MUL:.+]] = arith.mulf %[[A_CAST]], %[[B_CAST]] : f32 # CHECK-NEXT: %[[ADD:.+]] = arith.addf %[[C_ARG]], %[[MUL]] : f32 # CHECK-NEXT: linalg.yield %[[ADD]] : f32 # CHECK-NEXT: -> tensor<4x8xf32> @func.FuncOp.from_py_func( RankedTensorType.get((4, 16), f64), RankedTensorType.get((16, 8), f64), RankedTensorType.get((4, 8), f32), ) def test_f64f64f32_matmul(lhs, rhs, init_result): return matmul_poly(lhs, rhs, outs=[init_result]) print(module)