# 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 # CHECK-SAME: fun = #linalg.unary_fn # 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 # CHECK-SAME: fun = #linalg.binary_fn # 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 # CHECK-SAME: operandSegmentSizes = array # 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)