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# RUN: %PYTHON %s | FileCheck %s
from mlir.dialects import arith, builtin, func, linalg, tensor
from mlir.dialects.linalg.opdsl.lang import *
from mlir.ir import *
def run(f):
print("\nTEST:", f.__name__)
f()
return f
# 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, ShapedType.get_dynamic_size()), f32)
)
def fill_tensor(out):
zero = arith.ConstantOp(
value=FloatAttr.get(f32, 0.0), 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, ShapedType.get_dynamic_size()), f32)
)
def fill_buffer(out):
zero = arith.ConstantOp(
value=FloatAttr.get(f32, 0.0), 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 = tensor.EmptyOp([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 = tensor.EmptyOp([4, 8], f32)
# CHECK: "linalg.matmul"(%{{.*}})
# CHECK-SAME: cast = #linalg.type_fn<cast_signed>
# CHECK-SAME: operandSegmentSizes = array<i32: 2, 1>
# 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: (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 = tensor.EmptyOp([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:.*]] = tensor.empty
init = tensor.EmptyOp([4, 8], f32)
# CHECK: linalg.matmul
# CHECK: ins(%[[LHS]], %[[RHS]]
# CHECK: outs(%[[INIT]]
return linalg.matmul(lhs, rhs, outs=init)
print(module)
# CHECK-LABEL: TEST: testIdentityRegionOps
@run
def testIdentityRegionOps():
with Context(), Location.unknown():
module = Module.create()
f32 = F32Type.get()
with InsertionPoint(module.body):
# CHECK: %[[VAL_0:.*]] = tensor.empty() : tensor<1x13xf32>
# CHECK: %[[VAL_1:.*]] = tensor.empty() : tensor<13x1xf32>
op1 = tensor.EmptyOp([1, 13], f32)
op2 = tensor.EmptyOp([13, 1], f32)
# CHECK: %[[VAL_2:.*]] = linalg.transpose ins(%[[VAL_0]] : tensor<1x13xf32>) outs(%[[VAL_1]] : tensor<13x1xf32>) permutation = [1, 0]
op3 = linalg.TransposeOp(
result=[RankedTensorType.get((13, 1), f32)],
input=op1,
init=op2,
permutation=[1, 0],
)
linalg.fill_builtin_region(op3.operation)
# CHECK: %[[VAL_3:.*]] = linalg.transpose ins(%[[VAL_1]] : tensor<13x1xf32>) outs(%[[VAL_0]] : tensor<1x13xf32>) permutation = [1, 0]
op4 = linalg.transpose(op2, outs=[op1], permutation=[1, 0])
# CHECK: func.func @transpose_op(%[[VAL_4:.*]]: memref<1x13xf32>, %[[VAL_5:.*]]: memref<13x1xf32>)
@func.FuncOp.from_py_func(
MemRefType.get((1, 13), f32),
MemRefType.get((13, 1), f32),
)
def transpose_op(op1, op2):
# CHECK: linalg.transpose ins(%[[VAL_4]] : memref<1x13xf32>) outs(%[[VAL_5]] : memref<13x1xf32>) permutation = [1, 0]
op3 = linalg.TransposeOp(
result=[],
input=op1,
init=op2,
permutation=[1, 0],
)
linalg.fill_builtin_region(op3.operation)
# CHECK: linalg.transpose ins(%[[VAL_5]] : memref<13x1xf32>) outs(%[[VAL_4]] : memref<1x13xf32>) permutation = [1, 0]
op4 = linalg.transpose(op2, outs=[op1], permutation=[1, 0])
# CHECK: %[[VAL_6:.*]] = tensor.empty() : tensor<16xf32>
# CHECK: %[[VAL_7:.*]] = tensor.empty() : tensor<16x64xf32>
op1 = tensor.EmptyOp([16], f32)
op2 = tensor.EmptyOp([16, 64], f32)
# CHECK: %[[VAL_8:.*]] = linalg.broadcast ins(%[[VAL_6]] : tensor<16xf32>) outs(%[[VAL_7]] : tensor<16x64xf32>) dimensions = [1]
op3 = linalg.BroadcastOp(
result=[RankedTensorType.get((16, 64), f32)],
input=op1,
init=op2,
dimensions=[1],
)
linalg.fill_builtin_region(op3.operation)
# CHECK: %[[VAL_9:.*]] = tensor.empty() : tensor<64xf32>
op4 = tensor.EmptyOp([64], f32)
# CHECK: %[[VAL_10:.*]] = linalg.broadcast ins(%[[VAL_9]] : tensor<64xf32>) outs(%[[VAL_7]] : tensor<16x64xf32>) dimensions = [0]
op5 = linalg.broadcast(op4, outs=[op2], dimensions=[0])
# CHECK: func.func @broadcast_op(%[[VAL_11:.*]]: memref<16xf32>, %[[VAL_12:.*]]: memref<16x64xf32>, %[[VAL_13:.*]]: memref<64xf32>)
@func.FuncOp.from_py_func(
MemRefType.get((16,), f32),
MemRefType.get((16, 64), f32),
MemRefType.get((64,), f32),
)
def broadcast_op(op1, op2, op3):
# CHECK: linalg.broadcast ins(%[[VAL_11]] : memref<16xf32>) outs(%[[VAL_12]] : memref<16x64xf32>) dimensions = [1]
op4 = linalg.BroadcastOp(
result=[],
input=op1,
init=op2,
dimensions=[1],
)
linalg.fill_builtin_region(op4.operation)
# CHECK: linalg.broadcast ins(%[[VAL_13]] : memref<64xf32>) outs(%[[VAL_12]] : memref<16x64xf32>) dimensions = [0]
op5 = linalg.broadcast(op3, outs=[op2], dimensions=[0])
print(module)
|
