# 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 @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 @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 # 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 = 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 # 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)