# 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)
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