clang-p2996/mlir/test/python/dialects/linalg/ops.py

# RUN: %PYTHON %s | FileCheck %s

from mlir.ir import *
from mlir.dialects import arith
from mlir.dialects import builtin
from mlir.dialects import func
from mlir.dialects import linalg

from mlir.dialects.linalg.opdsl.lang import *


def run(f):
  print("\nTEST:", f.__name__)
  f()
  return f


# CHECK-LABEL: TEST: testInitTensor
@run
def testInitTensor():
  with Context() as ctx, Location.unknown():
    module = Module.create()
    f32 = F32Type.get()
    with InsertionPoint(module.body):
      # CHECK-LABEL: func @static_sizes
      # CHECK: %0 = linalg.init_tensor [3, 4] : tensor<3x4xf32>
      @func.FuncOp.from_py_func()
      def static_sizes():
        return linalg.InitTensorOp([3, 4], f32)

      # CHECK-LABEL: func @dynamic_sizes
      # CHECK: %0 = linalg.init_tensor [%arg0, %arg1] : tensor<?x?xf32>
      @func.FuncOp.from_py_func(IndexType.get(), IndexType.get())
      def dynamic_sizes(d0, d1):
        return linalg.InitTensorOp([d0, d1], f32)

      # CHECK-LABEL: func @zero_d
      # CHECK: %0 = linalg.init_tensor [] : tensor<f32>
      @func.FuncOp.from_py_func()
      def zero_d():
        return linalg.InitTensorOp([], f32)

  print(module)


# CHECK-LABEL: TEST: testInitTensorStaticSizesAttribute
@run
def testInitTensorStaticSizesAttribute():
  with Context() as ctx, Location.unknown():
    module = Module.create()
    f32 = F32Type.get()
    with InsertionPoint(module.body):
      op = linalg.InitTensorOp([3, 4], f32)
      # CHECK: [3, 4]
      print(op.attributes["static_sizes"])


# CHECK-LABEL: TEST: testFill
@run
def testFill():
  with Context() as ctx, Location.unknown():
    module = Module.create()
    f32 = F32Type.get()
    with InsertionPoint(module.body):
      # CHECK-LABEL: func @fill_tensor
      #  CHECK-SAME:   %[[OUT:[0-9a-z]+]]: tensor<12x?xf32>
      #  CHECK-NEXT: %[[CST:.*]] = arith.constant 0.0{{.*}} : f32
      #  CHECK-NEXT: %[[RES:.*]] = linalg.fill ins(%[[CST]] : f32) outs(%[[OUT]] : tensor<12x?xf32>) -> tensor<12x?xf32>
      #  CHECK-NEXT: return %[[RES]] : tensor<12x?xf32>
      @func.FuncOp.from_py_func(RankedTensorType.get((12, -1), f32))
      def fill_tensor(out):
        zero = arith.ConstantOp(value=FloatAttr.get(f32, 0.), result=f32).result
        return linalg.fill(zero, outs=[out])

      # CHECK-LABEL: func @fill_buffer
      #  CHECK-SAME:   %[[OUT:[0-9a-z]+]]: memref<12x?xf32>
      #  CHECK-NEXT: %[[CST:.*]] = arith.constant 0.0{{.*}} : f32
      #  CHECK-NEXT: linalg.fill ins(%[[CST]] : f32) outs(%[[OUT]] : memref<12x?xf32>)
      #  CHECK-NEXT: return
      @func.FuncOp.from_py_func(MemRefType.get((12, -1), f32))
      def fill_buffer(out):
        zero = arith.ConstantOp(value=FloatAttr.get(f32, 0.), result=f32).result
        linalg.fill(zero, outs=[out])

  print(module)


# CHECK-LABEL: TEST: testNamedStructuredOpCustomForm
@run
def testNamedStructuredOpCustomForm():
  with Context() as ctx, Location.unknown():
    module = Module.create()
    f32 = F32Type.get()
    with InsertionPoint(module.body):

      @func.FuncOp.from_py_func(
          RankedTensorType.get((4, 8), f32), RankedTensorType.get((4, 8), f32))
      def named_form(lhs, rhs):
        init_result = linalg.InitTensorOp([4, 8], f32)
        # Check for the named form with custom format
        #      CHECK: linalg.elemwise_unary
        # CHECK-SAME:    cast = #linalg.type_fn<cast_signed>
        # CHECK-SAME:    fun = #linalg.unary_fn<exp>
        # CHECK-SAME:    ins(%{{.*}} : tensor<4x8xf32>) outs(%{{.*}} : tensor<4x8xf32>)
        unary_result = linalg.elemwise_unary(lhs, outs=[init_result.result])
        #      CHECK: linalg.elemwise_binary
        # CHECK-SAME:    cast = #linalg.type_fn<cast_unsigned>
        # CHECK-SAME:    fun = #linalg.binary_fn<mul>
        # CHECK-SAME:    ins(%{{.*}}, %{{.*}} : tensor<4x8xf32>, tensor<4x8xf32>) outs(%{{.*}} : tensor<4x8xf32>)
        #      CHECK: return
        binary_result = linalg.elemwise_binary(
            lhs,
            rhs,
            outs=[init_result.result],
            fun=BinaryFn.mul,
            cast=TypeFn.cast_unsigned)
        return unary_result, binary_result

  print(module)


# CHECK-LABEL: TEST: testNamedStructuredOpGenericForm
@run
def testNamedStructuredOpGenericForm():
  with Context() as ctx, Location.unknown():
    module = Module.create()
    f32 = F32Type.get()
    with InsertionPoint(module.body):

      @func.FuncOp.from_py_func(
          RankedTensorType.get((4, 16), f32), RankedTensorType.get((16, 8),
                                                                   f32))
      def named_form(lhs, rhs):
        init_result = linalg.InitTensorOp([4, 8], f32)
        #      CHECK: "linalg.matmul"(%{{.*}})
        # CHECK-NEXT:  ^bb0(%{{.*}}: f32, %{{.*}}: f32, %{{.*}}: f32):
        # CHECK-NEXT:    arith.mulf{{.*}} (f32, f32) -> f32
        # CHECK-NEXT:    arith.addf{{.*}} (f32, f32) -> f32
        # CHECK-NEXT:    linalg.yield{{.*}} (f32) -> ()
        # CHECK-NEXT:    cast = #linalg.type_fn<cast_signed>
        # CHECK-SAME:    operand_segment_sizes = dense<[2, 1]> : vector<2xi32>
        # CHECK-SAME: (tensor<4x16xf32>, tensor<16x8xf32>, tensor<4x8xf32>) -> tensor<4x8xf32>
        return linalg.matmul(lhs, rhs, outs=[init_result.result])

  module.operation.print(print_generic_op_form=True)


# CHECK-LABEL: TEST: testNamedStructuredAsGenericOp
@run
def testNamedStructuredAsGenericOp():
  with Context() as ctx, Location.unknown():
    module = Module.create()
    f32 = F32Type.get()
    with InsertionPoint(module.body):

      @func.FuncOp.from_py_func(
          RankedTensorType.get((4, 16), f32), RankedTensorType.get((16, 8),
                                                                   f32))
      def generic_form(lhs, rhs):
        init_result = linalg.InitTensorOp([4, 8], f32)
        # CHECK: linalg.generic
        return linalg.matmul(
            lhs, rhs, outs=[init_result.result], emit_generic=True)

  print(module)


# CHECK-LABEL: TEST: testOpResultFromOtherOp
@run
def testOpResultFromOtherOp():
  with Context(), Location.unknown():
    module = Module.create()
    f32 = F32Type.get()
    with InsertionPoint(module.body):

      @func.FuncOp.from_py_func(
          RankedTensorType.get((4, 16), f32), RankedTensorType.get((16, 8),
                                                                   f32))
      def pass_an_op_directly(arg0, arg1):
        one = arith.ConstantOp(F32Type.get(), 1.0)
        # CHECK: %[[LHS:.*]] = linalg.fill
        lhs = linalg.fill(one, outs=[arg0])
        # CHECK: %[[RHS:.*]] = linalg.fill
        rhs = linalg.fill(one, outs=[arg1])
        # CHECK: %[[INIT:.*]] = linalg.init_tensor
        init = linalg.InitTensorOp([4, 8], f32)
        # CHECK: linalg.matmul
        # CHECK: ins(%[[LHS]], %[[RHS]]
        # CHECK: outs(%[[INIT]]
        return linalg.matmul(lhs, rhs, outs=init)

  print(module)