| # RUN: %PYTHON %s | FileCheck %s |
| |
| from mlir.ir import * |
| from mlir.dialects import builtin |
| from mlir.dialects import linalg |
| from mlir.dialects import std |
| |
| 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:.*]] = constant 0.0{{.*}} : f32 |
| # CHECK-NEXT: %[[RES:.*]] = linalg.fill(%[[OUT]], %[[CST]]) : tensor<12x?xf32>, f32 -> 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 = std.ConstantOp(value=FloatAttr.get(f32, 0.), result=f32).result |
| # TODO: FillOp.result is None. When len(results) == 1 we expect it to |
| # be results[0] as per _linalg_ops_gen.py. This seems like an |
| # orthogonal bug in the generator of _linalg_ops_gen.py. |
| return linalg.FillOp(output=out, value=zero).results[0] |
| |
| # CHECK-LABEL: func @fill_buffer |
| # CHECK-SAME: %[[OUT:[0-9a-z]+]]: memref<12x?xf32> |
| # CHECK-NEXT: %[[CST:.*]] = constant 0.0{{.*}} : f32 |
| # CHECK-NEXT: linalg.fill(%[[OUT]], %[[CST]]) : memref<12x?xf32>, f32 |
| # CHECK-NEXT: return |
| @builtin.FuncOp.from_py_func( |
| MemRefType.get((12, -1), f32)) |
| def fill_buffer(out): |
| zero = std.ConstantOp(value=FloatAttr.get(f32, 0.), result=f32).result |
| linalg.FillOp(output=out, value=zero) |
| |
| print(module) |
| |
| |
| # CHECK-LABEL: TEST: testStructuredOpOnTensors |
| @run |
| def testStructuredOpOnTensors(): |
| with Context() as ctx, Location.unknown(): |
| module = Module.create() |
| f32 = F32Type.get() |
| tensor_type = RankedTensorType.get((2, 3, 4), f32) |
| with InsertionPoint(module.body): |
| func = builtin.FuncOp(name="matmul_test", |
| type=FunctionType.get( |
| inputs=[tensor_type, tensor_type], |
| results=[tensor_type])) |
| with InsertionPoint(func.add_entry_block()): |
| lhs, rhs = func.entry_block.arguments |
| result = linalg.MatmulOp([lhs, rhs], results=[tensor_type]).result |
| std.ReturnOp([result]) |
| |
| # CHECK: %[[R:.*]] = linalg.matmul ins(%arg0, %arg1 : tensor<2x3x4xf32>, tensor<2x3x4xf32>) -> tensor<2x3x4xf32> |
| print(module) |
| |
| |
| # CHECK-LABEL: TEST: testStructuredOpOnBuffers |
| @run |
| def testStructuredOpOnBuffers(): |
| with Context() as ctx, Location.unknown(): |
| module = Module.create() |
| f32 = F32Type.get() |
| memref_type = MemRefType.get((2, 3, 4), f32) |
| with InsertionPoint(module.body): |
| func = builtin.FuncOp(name="matmul_test", |
| type=FunctionType.get( |
| inputs=[memref_type, memref_type, memref_type], |
| results=[])) |
| with InsertionPoint(func.add_entry_block()): |
| lhs, rhs, result = func.entry_block.arguments |
| # TODO: prperly hook up the region. |
| linalg.MatmulOp([lhs, rhs], outputs=[result]) |
| std.ReturnOp([]) |
| |
| # CHECK: linalg.matmul ins(%arg0, %arg1 : memref<2x3x4xf32>, memref<2x3x4xf32>) outs(%arg2 : memref<2x3x4xf32>) |
| 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: std.mulf{{.*}} (f32, f32) -> f32 |
| # CHECK-NEXT: std.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) |