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# RUN: %PYTHON %s | FileCheck %s
from mlir.ir import *
from mlir.dialects import builtin
from mlir.dialects import linalg
from mlir.dialects import std
from mlir.dialects import arith
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>
@builtin.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>
@builtin.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>
@builtin.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(%[[CST]], %[[OUT]]) : f32, tensor<12x?xf32> -> tensor<12x?xf32>
# CHECK-NEXT: return %[[RES]] : tensor<12x?xf32>
@builtin.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.FillOp(output=out, value=zero).result
# 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(%[[CST]], %[[OUT]]) : f32, memref<12x?xf32>
# CHECK-NEXT: return
@builtin.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.FillOp(output=out, value=zero)
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):
@builtin.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)
# First check the named form with custom format
# CHECK: linalg.matmul
# CHECK-NOT: linalg.memoized_indexing_maps
# CHECK-SAME: ins(%{{.*}} : tensor<4x16xf32>, tensor<16x8xf32>)
# CHECK-SAME: outs(%{{.*}} : tensor<4x8xf32>)
# CHECK-SAME: -> tensor<4x8xf32>
# CHECK-NEXT: return
return linalg.matmul(lhs, rhs, outs=[init_result.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):
@builtin.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: {linalg.memoized_indexing_maps{{.*}}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):
@builtin.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):
@builtin.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.FillOp(arg0, one)
# CHECK: %[[RHS:.*]] = linalg.fill
rhs = linalg.FillOp(arg1, one)
# 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)
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