| # RUN: %PYTHON %s | FileCheck %s |
| |
| from mlir.dialects import arith, func, linalg, tensor, memref, builtin |
| from mlir.dialects.linalg.opdsl.lang import * |
| from mlir.extras import types as T |
| 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: 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) |
| |
| |
| # CHECK-LABEL: TEST: testGenericOp |
| @run |
| def testGenericOp(): |
| with Context(), Location.unknown(): |
| module = Module.create() |
| f32 = F32Type.get() |
| memref_t = MemRefType.get([10, 10], f32) |
| with InsertionPoint(module.body): |
| id_map_1 = AffineMap.get_identity(2) |
| # CHECK: %[[VAL_0:.*]] = tensor.empty() : tensor<16x16xf32> |
| # CHECK: %[[VAL_1:.*]] = tensor.empty() : tensor<16x16xf32> |
| x = tensor.empty((16, 16), f32) |
| y = tensor.empty((16, 16), f32) |
| |
| # CHECK: %[[VAL_2:.*]] = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel", "parallel"]} ins(%[[VAL_0]] : tensor<16x16xf32>) outs(%[[VAL_1]] : tensor<16x16xf32>) { |
| # CHECK: ^bb0(%in: f32, %out: f32): |
| # CHECK: linalg.yield %in : f32 |
| # CHECK: } -> tensor<16x16xf32> |
| @linalg.generic( |
| [x], |
| [y], |
| [id_map_1, id_map_1], |
| [linalg.IteratorType.parallel, linalg.IteratorType.parallel], |
| ) |
| def f(a, b): |
| assert isinstance(a, Value) |
| assert isinstance(a.type, F32Type) |
| assert isinstance(b, Value) |
| assert isinstance(b.type, F32Type) |
| return a |
| |
| assert isinstance(f, Value) |
| assert isinstance(f.type, RankedTensorType) |
| |
| # CHECK: %[[VAL_3:.*]] = tensor.empty() : tensor<16x16x16xf32> |
| z = tensor.empty((16, 16, 16), f32) |
| |
| minor_id = AffineMap.get_minor_identity(3, 2) |
| id_map_2 = AffineMap.get_identity(3) |
| |
| # CHECK: %[[VAL_4:.+]]:2 = linalg.generic {indexing_maps = [#map1, #map2, #map2], iterator_types = ["parallel", "parallel", "parallel"]} ins(%[[VAL_0]] : tensor<16x16xf32>) outs(%[[VAL_3]], %[[VAL_3]] : tensor<16x16x16xf32>, tensor<16x16x16xf32>) { |
| # CHECK: ^bb0(%in: f32, %out: f32, %out_1: f32): |
| # CHECK: linalg.yield %in, %out : f32, f32 |
| # CHECK: } -> (tensor<16x16x16xf32>, tensor<16x16x16xf32>) |
| @linalg.generic( |
| [x], |
| [z, z], |
| [minor_id, id_map_2, id_map_2], |
| [ |
| linalg.IteratorType.parallel, |
| linalg.IteratorType.parallel, |
| linalg.IteratorType.parallel, |
| ], |
| ) |
| def g(a, b, c): |
| assert isinstance(a, Value) |
| assert isinstance(a.type, F32Type) |
| assert isinstance(b, Value) |
| assert isinstance(b.type, F32Type) |
| assert isinstance(c, Value) |
| assert isinstance(c.type, F32Type) |
| return a, b |
| |
| assert isinstance(g, OpResultList) |
| assert len(g) == 2 |
| assert isinstance(g[0].type, RankedTensorType) |
| assert isinstance(g[1].type, RankedTensorType) |
| |
| # CHECK: %[[VAL_5:.*]] = memref.alloc() : memref<10x10xf32> |
| # CHECK: %[[VAL_6:.*]] = memref.alloc() : memref<10x10xf32> |
| xx = memref.alloc(memref_t, [], []) |
| yy = memref.alloc(memref_t, [], []) |
| |
| # CHECK: linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel", "parallel"]} ins(%[[VAL_5]] : memref<10x10xf32>) outs(%[[VAL_6]] : memref<10x10xf32>) { |
| # CHECK: ^bb0(%in: f32, %out: f32): |
| # CHECK: linalg.yield %in : f32 |
| # CHECK: } |
| @linalg.generic( |
| [xx], |
| [yy], |
| [id_map_1, id_map_1], |
| [linalg.IteratorType.parallel, linalg.IteratorType.parallel], |
| ) |
| def f(a, b): |
| assert isinstance(a, Value) |
| assert isinstance(a.type, F32Type) |
| assert isinstance(b, Value) |
| assert isinstance(b.type, F32Type) |
| return a |
| |
| module.operation.verify() |
| print(module) |
| |
| |
| # CHECK-LABEL: TEST: testMatmulOp |
| @run |
| def testMatmulOp(): |
| with Context(), Location.unknown(): |
| module = Module.create() |
| f32 = F32Type.get() |
| with InsertionPoint(module.body): |
| a_shape = (4, 8) |
| b_shape = (8, 12) |
| b_transposed_shape = (12, 8) |
| c_shape = (4, 12) |
| |
| dimM = ir.AffineDimExpr.get(0) |
| dimN = ir.AffineDimExpr.get(1) |
| dimK = ir.AffineDimExpr.get(2) |
| |
| # CHECK: #[[$A_MAP:.*]] = affine_map<(d0, d1, d2) -> (d0, d2)> |
| # CHECK: #[[$BTrans_MAP:.*]] = affine_map<(d0, d1, d2) -> (d1, d2)> |
| # CHECK: #[[$C_MAP:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)> |
| a_map = ir.AffineMap.get(3, 0, [dimM, dimK]) |
| b_map = ir.AffineMap.get(3, 0, [dimK, dimN]) |
| c_map = ir.AffineMap.get(3, 0, [dimM, dimN]) |
| b_transposed_map = ir.AffineMap.get(3, 0, [dimN, dimK]) |
| |
| # CHECK: func.func @matmul_op( |
| @func.FuncOp.from_py_func( |
| # CHECK-SAME: %[[A:.*]]: tensor<4x8xf32>, |
| RankedTensorType.get(a_shape, f32), |
| # CHECK-SAME: %[[Amem:.*]]: memref<4x8xf32>, |
| MemRefType.get(a_shape, f32), |
| # CHECK-SAME: %[[B:.*]]: tensor<8x12xf32>, |
| RankedTensorType.get(b_shape, f32), |
| # CHECK-SAME: %[[Bmem:.*]]: memref<8x12xf32>, |
| MemRefType.get(b_shape, f32), |
| # CHECK-SAME: %[[BTrans:.*]]: tensor<12x8xf32>, |
| RankedTensorType.get(b_transposed_shape, f32), |
| # CHECK-SAME: %[[BTransmem:.*]]: memref<12x8xf32>, |
| MemRefType.get(b_transposed_shape, f32), |
| # CHECK-SAME: %[[C:.*]]: tensor<4x12xf32>, |
| RankedTensorType.get(c_shape, f32), |
| # CHECK-SAME: %[[Cmem:.*]]: memref<4x12xf32>) |
| MemRefType.get(c_shape, f32), |
| ) |
| def matmul_op(A, Amem, B, Bmem, Btransposed, Btransposedmem, C, Cmem): |
| # CHECK: linalg.matmul ins(%[[A]], %[[B]] : tensor<4x8xf32>, tensor<8x12xf32>) outs(%[[C]] : tensor<4x12xf32>) |
| res = linalg.MatmulOp( |
| result_tensors=(C.type,), |
| inputs=(A, B), |
| outputs=(C,), |
| ) |
| linalg.fill_builtin_region(res.operation) |
| # CHECK: linalg.matmul ins(%[[A]], %[[B]] : tensor<4x8xf32>, tensor<8x12xf32>) outs(%[[C]] : tensor<4x12xf32>) |
| res = linalg.matmul(A, B, outs=(C,)) |
| |
| # CHECK: linalg.matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[BTrans]] : tensor<4x8xf32>, tensor<12x8xf32>) outs(%[[C]] : tensor<4x12xf32>) |
| res = linalg.MatmulOp( |
| result_tensors=(C.type,), |
| inputs=(A, Btransposed), |
| outputs=(C,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| linalg.fill_builtin_region(res.operation) |
| # CHECK: linalg.matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[BTrans]] : tensor<4x8xf32>, tensor<12x8xf32>) outs(%[[C]] : tensor<4x12xf32>) |
| res = linalg.matmul( |
| A, |
| Btransposed, |
| outs=(C,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| |
| # And now with memrefs... |
| |
| # CHECK: linalg.matmul ins(%[[Amem]], %[[Bmem]] : memref<4x8xf32>, memref<8x12xf32>) outs(%[[Cmem]] : memref<4x12xf32>) |
| res = linalg.MatmulOp( |
| result_tensors=[], |
| inputs=(Amem, Bmem), |
| outputs=(Cmem,), |
| ) |
| linalg.fill_builtin_region(res.operation) |
| # CHECK: linalg.matmul ins(%[[Amem]], %[[Bmem]] : memref<4x8xf32>, memref<8x12xf32>) outs(%[[Cmem]] : memref<4x12xf32>) |
| linalg.matmul(Amem, Bmem, outs=(Cmem,)) |
| |
| # CHECK: linalg.matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[BTransmem]] : memref<4x8xf32>, memref<12x8xf32>) outs(%[[Cmem]] : memref<4x12xf32>) |
| res = linalg.MatmulOp( |
| result_tensors=[], |
| inputs=(Amem, Btransposedmem), |
| outputs=(Cmem,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| linalg.fill_builtin_region(res.operation) |
| # CHECK: linalg.matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[BTransmem]] : memref<4x8xf32>, memref<12x8xf32>) outs(%[[Cmem]] : memref<4x12xf32>) |
| linalg.matmul( |
| Amem, |
| Btransposedmem, |
| outs=(Cmem,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| |
| print(module) |
| |
| |
| # CHECK-LABEL: TEST: testContractOp |
| @run |
| def testContractOp(): |
| with Context(), Location.unknown(): |
| module = Module.create() |
| f32 = F32Type.get() |
| with InsertionPoint(module.body): |
| a_shape = (4, 8) |
| b_shape = (8, 12) |
| b_transposed_shape = (12, 8) |
| c_shape = (4, 12) |
| |
| dimM = ir.AffineDimExpr.get(0) |
| dimN = ir.AffineDimExpr.get(1) |
| dimK = ir.AffineDimExpr.get(2) |
| |
| # CHECK: #[[$A_MAP:.*]] = affine_map<(d0, d1, d2) -> (d0, d2)> |
| # CHECK: #[[$B_MAP:.*]] = affine_map<(d0, d1, d2) -> (d2, d1)> |
| # CHECK: #[[$C_MAP:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)> |
| # CHECK: #[[$BTrans_MAP:.*]] = affine_map<(d0, d1, d2) -> (d1, d2)> |
| a_map = ir.AffineMap.get(3, 0, [dimM, dimK]) |
| b_map = ir.AffineMap.get(3, 0, [dimK, dimN]) |
| c_map = ir.AffineMap.get(3, 0, [dimM, dimN]) |
| b_transposed_map = ir.AffineMap.get(3, 0, [dimN, dimK]) |
| |
| # CHECK: func.func @matmul_as_contract_op( |
| @func.FuncOp.from_py_func( |
| # CHECK-SAME: %[[A:.*]]: tensor<4x8xf32>, |
| RankedTensorType.get(a_shape, f32), |
| # CHECK-SAME: %[[Amem:.*]]: memref<4x8xf32>, |
| MemRefType.get(a_shape, f32), |
| # CHECK-SAME: %[[B:.*]]: tensor<8x12xf32>, |
| RankedTensorType.get(b_shape, f32), |
| # CHECK-SAME: %[[Bmem:.*]]: memref<8x12xf32>, |
| MemRefType.get(b_shape, f32), |
| # CHECK-SAME: %[[BTrans:.*]]: tensor<12x8xf32>, |
| RankedTensorType.get(b_transposed_shape, f32), |
| # CHECK-SAME: %[[BTransmem:.*]]: memref<12x8xf32>, |
| MemRefType.get(b_transposed_shape, f32), |
| # CHECK-SAME: %[[C:.*]]: tensor<4x12xf32>, |
| RankedTensorType.get(c_shape, f32), |
| # CHECK-SAME: %[[Cmem:.*]]: memref<4x12xf32>) |
| MemRefType.get(c_shape, f32), |
| ) |
| def matmul_as_contract_op( |
| A, Amem, B, Bmem, Btransposed, Btransposedmem, C, Cmem |
| ): |
| # CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$B_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[B]] : tensor<4x8xf32>, tensor<8x12xf32>) outs(%[[C]] : tensor<4x12xf32>) |
| op4 = linalg.ContractOp( |
| result_tensors=(C.type,), |
| inputs=(A, B), |
| outputs=(C,), |
| indexing_maps=[a_map, b_map, c_map], |
| ) |
| linalg.fill_builtin_region(op4.operation) |
| # CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$B_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[B]] : tensor<4x8xf32>, tensor<8x12xf32>) outs(%[[C]] : tensor<4x12xf32>) |
| op5 = linalg.contract( |
| A, B, outs=(C,), indexing_maps=[a_map, b_map, c_map] |
| ) |
| |
| # CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[BTrans]] : tensor<4x8xf32>, tensor<12x8xf32>) outs(%[[C]] : tensor<4x12xf32>) |
| op4 = linalg.ContractOp( |
| result_tensors=(C.type,), |
| inputs=(A, Btransposed), |
| outputs=(C,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| linalg.fill_builtin_region(op4.operation) |
| # CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[BTrans]] : tensor<4x8xf32>, tensor<12x8xf32>) outs(%[[C]] : tensor<4x12xf32>) |
| op5 = linalg.contract( |
| A, |
| Btransposed, |
| outs=(C,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| # And now with memrefs... |
| |
| # CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$B_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[Bmem]] : memref<4x8xf32>, memref<8x12xf32>) outs(%[[Cmem]] : memref<4x12xf32>) |
| op4 = linalg.ContractOp( |
| result_tensors=[], |
| inputs=(Amem, Bmem), |
| outputs=(Cmem,), |
| indexing_maps=[a_map, b_map, c_map], |
| ) |
| linalg.fill_builtin_region(op4.operation) |
| # CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$B_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[Bmem]] : memref<4x8xf32>, memref<8x12xf32>) outs(%[[Cmem]] : memref<4x12xf32>) |
| linalg.contract( |
| Amem, Bmem, outs=(Cmem,), indexing_maps=[a_map, b_map, c_map] |
| ) |
| |
| # CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[BTransmem]] : memref<4x8xf32>, memref<12x8xf32>) outs(%[[Cmem]] : memref<4x12xf32>) |
| op4 = linalg.ContractOp( |
| result_tensors=[], |
| inputs=(Amem, Btransposedmem), |
| outputs=(Cmem,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| linalg.fill_builtin_region(op4.operation) |
| # CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[BTransmem]] : memref<4x8xf32>, memref<12x8xf32>) outs(%[[Cmem]] : memref<4x12xf32>) |
| linalg.contract( |
| Amem, |
| Btransposedmem, |
| outs=(Cmem,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| |
| print(module) |
| |
| |
| # CHECK-LABEL: TEST: testBatchMatmulOp |
| @run |
| def testBatchMatmulOp(): |
| with Context(), Location.unknown(): |
| module = Module.create() |
| f32 = F32Type.get() |
| with InsertionPoint(module.body): |
| a_shape = (2, 4, 8) |
| b_shape = (2, 8, 12) |
| b_transposed_shape = (2, 12, 8) |
| c_shape = (2, 4, 12) |
| |
| dimBatch = ir.AffineDimExpr.get(0) |
| dimM = ir.AffineDimExpr.get(1) |
| dimN = ir.AffineDimExpr.get(2) |
| dimK = ir.AffineDimExpr.get(3) |
| |
| # CHECK: #[[$A_MAP:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)> |
| # CHECK: #[[$BTrans_MAP:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)> |
| # CHECK: #[[$C_MAP:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)> |
| |
| a_map = ir.AffineMap.get(4, 0, [dimBatch, dimM, dimK]) |
| b_transposed_map = ir.AffineMap.get(4, 0, [dimBatch, dimN, dimK]) |
| c_map = ir.AffineMap.get(4, 0, [dimBatch, dimM, dimN]) |
| |
| # CHECK: func.func @batch_matmul_op( |
| @func.FuncOp.from_py_func( |
| # CHECK-SAME: %[[A:.*]]: tensor<2x4x8xf32>, |
| RankedTensorType.get(a_shape, f32), |
| # CHECK-SAME: %[[Amem:.*]]: memref<2x4x8xf32>, |
| MemRefType.get(a_shape, f32), |
| # CHECK-SAME: %[[B:.*]]: tensor<2x8x12xf32>, |
| RankedTensorType.get(b_shape, f32), |
| # CHECK-SAME: %[[Bmem:.*]]: memref<2x8x12xf32>, |
| MemRefType.get(b_shape, f32), |
| # CHECK-SAME: %[[BTrans:.*]]: tensor<2x12x8xf32>, |
| RankedTensorType.get(b_transposed_shape, f32), |
| # CHECK-SAME: %[[BTransmem:.*]]: memref<2x12x8xf32>, |
| MemRefType.get(b_transposed_shape, f32), |
| # CHECK-SAME: %[[C:.*]]: tensor<2x4x12xf32>, |
| RankedTensorType.get(c_shape, f32), |
| # CHECK-SAME: %[[Cmem:.*]]: memref<2x4x12xf32>) |
| MemRefType.get(c_shape, f32), |
| ) |
| def batch_matmul_op(A, Amem, B, Bmem, Btransposed, Btransposedmem, C, Cmem): |
| # CHECK: linalg.batch_matmul ins(%[[A]], %[[B]] : tensor<2x4x8xf32>, tensor<2x8x12xf32>) outs(%[[C]] : tensor<2x4x12xf32>) |
| res = linalg.BatchMatmulOp( |
| result_tensors=(C.type,), |
| inputs=(A, B), |
| outputs=(C,), |
| ) |
| linalg.fill_builtin_region(res.operation) |
| # CHECK: linalg.batch_matmul ins(%[[A]], %[[B]] : tensor<2x4x8xf32>, tensor<2x8x12xf32>) outs(%[[C]] : tensor<2x4x12xf32>) |
| res = linalg.batch_matmul(A, B, outs=(C,)) |
| |
| # CHECK: linalg.batch_matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[BTrans]] : tensor<2x4x8xf32>, tensor<2x12x8xf32>) outs(%[[C]] : tensor<2x4x12xf32>) |
| res = linalg.BatchMatmulOp( |
| result_tensors=(C.type,), |
| inputs=(A, Btransposed), |
| outputs=(C,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| linalg.fill_builtin_region(res.operation) |
| # CHECK: linalg.batch_matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[BTrans]] : tensor<2x4x8xf32>, tensor<2x12x8xf32>) outs(%[[C]] : tensor<2x4x12xf32>) |
| res = linalg.batch_matmul( |
| A, |
| Btransposed, |
| outs=(C,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| |
| # CHECK: linalg.batch_matmul ins(%[[Amem]], %[[Bmem]] : memref<2x4x8xf32>, memref<2x8x12xf32>) outs(%[[Cmem]] : memref<2x4x12xf32>) |
| res = linalg.BatchMatmulOp( |
| result_tensors=[], |
| inputs=(Amem, Bmem), |
| outputs=(Cmem,), |
| ) |
| linalg.fill_builtin_region(res.operation) |
| # CHECK: linalg.batch_matmul ins(%[[Amem]], %[[Bmem]] : memref<2x4x8xf32>, memref<2x8x12xf32>) outs(%[[Cmem]] : memref<2x4x12xf32>) |
| linalg.batch_matmul(Amem, Bmem, outs=(Cmem,)) |
| |
| # CHECK: linalg.batch_matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[BTransmem]] : memref<2x4x8xf32>, memref<2x12x8xf32>) outs(%[[Cmem]] : memref<2x4x12xf32>) |
| res = linalg.BatchMatmulOp( |
| result_tensors=[], |
| inputs=(Amem, Btransposedmem), |
| outputs=(Cmem,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| linalg.fill_builtin_region(res.operation) |
| # CHECK: linalg.batch_matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[BTransmem]] : memref<2x4x8xf32>, memref<2x12x8xf32>) outs(%[[Cmem]] : memref<2x4x12xf32>) |
| linalg.batch_matmul( |
| Amem, |
| Btransposedmem, |
| outs=(Cmem,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| |
| print(module) |
| |
| |
| # CHECK-LABEL: TEST: testBatchReduceMatmulOp |
| @run |
| def testBatchReduceMatmulOp(): |
| with Context(), Location.unknown(): |
| module = Module.create() |
| f32 = F32Type.get() |
| with InsertionPoint(module.body): |
| a_shape = (5, 4, 8) |
| b_shape = (5, 8, 12) |
| b_transposed_shape = (5, 12, 8) |
| c_shape = (4, 12) |
| |
| dimBatch = ir.AffineDimExpr.get(0) |
| dimM = ir.AffineDimExpr.get(1) |
| dimN = ir.AffineDimExpr.get(2) |
| dimK = ir.AffineDimExpr.get(3) |
| |
| # CHECK: #[[$A_MAP:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)> |
| # CHECK: #[[$BTrans_MAP:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)> |
| # CHECK: #[[$C_MAP:.*]] = affine_map<(d0, d1, d2, d3) -> (d1, d2)> |
| a_map = ir.AffineMap.get(4, 0, [dimBatch, dimM, dimK]) |
| b_transposed_map = ir.AffineMap.get(4, 0, [dimBatch, dimN, dimK]) |
| c_map = ir.AffineMap.get(4, 0, [dimM, dimN]) |
| |
| # CHECK: func.func @batch_reduce_matmul_op( |
| @func.FuncOp.from_py_func( |
| # CHECK-SAME: %[[A:.*]]: tensor<5x4x8xf32>, |
| RankedTensorType.get(a_shape, f32), |
| # CHECK-SAME: %[[Amem:.*]]: memref<5x4x8xf32>, |
| MemRefType.get(a_shape, f32), |
| # CHECK-SAME: %[[B:.*]]: tensor<5x8x12xf32>, |
| RankedTensorType.get(b_shape, f32), |
| # CHECK-SAME: %[[Bmem:.*]]: memref<5x8x12xf32>, |
| MemRefType.get(b_shape, f32), |
| # CHECK-SAME: %[[BTrans:.*]]: tensor<5x12x8xf32>, |
| RankedTensorType.get(b_transposed_shape, f32), |
| # CHECK-SAME: %[[BTransmem:.*]]: memref<5x12x8xf32>, |
| MemRefType.get(b_transposed_shape, f32), |
| # CHECK-SAME: %[[C:.*]]: tensor<4x12xf32>, |
| RankedTensorType.get(c_shape, f32), |
| # CHECK-SAME: %[[Cmem:.*]]: memref<4x12xf32>) |
| MemRefType.get(c_shape, f32), |
| ) |
| def batch_reduce_matmul_op( |
| A, Amem, B, Bmem, Btransposed, Btransposedmem, C, Cmem |
| ): |
| # CHECK: linalg.batch_reduce_matmul ins(%[[A]], %[[B]] : tensor<5x4x8xf32>, tensor<5x8x12xf32>) outs(%[[C]] : tensor<4x12xf32>) |
| res = linalg.BatchReduceMatmulOp( |
| result_tensors=(C.type,), |
| inputs=(A, B), |
| outputs=(C,), |
| ) |
| linalg.fill_builtin_region(res.operation) |
| # CHECK: linalg.batch_reduce_matmul ins(%[[A]], %[[B]] : tensor<5x4x8xf32>, tensor<5x8x12xf32>) outs(%[[C]] : tensor<4x12xf32>) |
| res = linalg.batch_reduce_matmul(A, B, outs=(C,)) |
| |
| # CHECK: linalg.batch_reduce_matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[BTrans]] : tensor<5x4x8xf32>, tensor<5x12x8xf32>) outs(%[[C]] : tensor<4x12xf32>) |
| res = linalg.BatchReduceMatmulOp( |
| result_tensors=(C.type,), |
| inputs=(A, Btransposed), |
| outputs=(C,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| linalg.fill_builtin_region(res.operation) |
| # CHECK: linalg.batch_reduce_matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[BTrans]] : tensor<5x4x8xf32>, tensor<5x12x8xf32>) outs(%[[C]] : tensor<4x12xf32>) |
| res = linalg.batch_reduce_matmul( |
| A, |
| Btransposed, |
| outs=(C,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| |
| # CHECK: linalg.batch_reduce_matmul ins(%[[Amem]], %[[Bmem]] : memref<5x4x8xf32>, memref<5x8x12xf32>) outs(%[[Cmem]] : memref<4x12xf32>) |
| res = linalg.BatchReduceMatmulOp( |
| result_tensors=[], |
| inputs=(Amem, Bmem), |
| outputs=(Cmem,), |
| ) |
| linalg.fill_builtin_region(res.operation) |
| # CHECK: linalg.batch_reduce_matmul ins(%[[Amem]], %[[Bmem]] : memref<5x4x8xf32>, memref<5x8x12xf32>) outs(%[[Cmem]] : memref<4x12xf32>) |
| linalg.batch_reduce_matmul(Amem, Bmem, outs=(Cmem,)) |
| |
| # CHECK: linalg.batch_reduce_matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[BTransmem]] : memref<5x4x8xf32>, memref<5x12x8xf32>) outs(%[[Cmem]] : memref<4x12xf32>) |
| res = linalg.BatchReduceMatmulOp( |
| result_tensors=[], |
| inputs=(Amem, Btransposedmem), |
| outputs=(Cmem,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| linalg.fill_builtin_region(res.operation) |
| # CHECK: linalg.batch_reduce_matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[BTransmem]] : memref<5x4x8xf32>, memref<5x12x8xf32>) outs(%[[Cmem]] : memref<4x12xf32>) |
| linalg.batch_reduce_matmul( |
| Amem, |
| Btransposedmem, |
| outs=(Cmem,), |
| indexing_maps=[a_map, b_transposed_map, c_map], |
| ) |
| |
| print(module) |
| |
| |
| # CHECK-LABEL: TEST: testPackUnPackOp |
| @run |
| def testPackUnPackOp(): |
| with Context(), Location.unknown(): |
| module = Module.create() |
| f32 = F32Type.get() |
| with InsertionPoint(module.body): |
| |
| @func.FuncOp.from_py_func( |
| RankedTensorType.get((128, 128), f32), |
| RankedTensorType.get((16, 16, 8, 8), f32), |
| ) |
| def tensor_pack(src, dst): |
| packed = linalg.pack( |
| src, |
| dst, |
| inner_dims_pos=[1, 0], |
| inner_tiles=[8, 8], |
| padding_value=arith.constant(f32, 0.0), |
| ) |
| |
| unpacked = linalg.unpack( |
| packed, |
| src, |
| inner_dims_pos=[0, 1], |
| inner_tiles=[8, 8], |
| ) |
| |
| return unpacked |
| |
| # CHECK-LABEL: func.func @tensor_pack( |
| # CHECK-SAME: %[[VAL_0:.*]]: tensor<128x128xf32>, %[[VAL_1:.*]]: tensor<16x16x8x8xf32>) -> tensor<128x128xf32> { |
| # CHECK: %[[VAL_2:.*]] = arith.constant 0.000000e+00 : f32 |
| # CHECK: %[[VAL_3:.*]] = linalg.pack %[[VAL_0]] padding_value(%[[VAL_2]] : f32) inner_dims_pos = [1, 0] inner_tiles = [8, 8] into %[[VAL_1]] : tensor<128x128xf32> -> tensor<16x16x8x8xf32> |
| # CHECK: %[[VAL_4:.*]] = linalg.unpack %[[VAL_3]] inner_dims_pos = [0, 1] inner_tiles = [8, 8] into %[[VAL_0]] : tensor<16x16x8x8xf32> -> tensor<128x128xf32> |
| # CHECK: return %[[VAL_4]] : tensor<128x128xf32> |
| # CHECK: } |
| print(module) |
| |
| |
| # CHECK-LABEL: TEST: testElementwiseOp |
| @run |
| def testElementwiseOp(): |
| with Context(), Location.unknown(): |
| module = Module.create() |
| f32 = F32Type.get() |
| with InsertionPoint(module.body): |
| rect_shape = (8, 16) |
| vert_line_shape = (8,) |
| hor_line_shape = (16,) |
| transposed_rect_shape = (16, 8) |
| |
| # CHECK-DAG: #[[$IdentMap2D:.*]] = affine_map<(d0, d1) -> (d0, d1)> |
| # CHECK-DAG: #[[$TransMap2D:.*]] = affine_map<(d0, d1) -> (d1, d0)> |
| # CHECK-DAG: #[[$VertLineBCastMap:.*]] = affine_map<(d0, d1) -> (d0)> |
| # CHECK-DAG: #[[$HorLineBCastMap:.*]] = affine_map<(d0, d1) -> (d1)> |
| |
| ident_map_2d = AffineMap.get_identity(2) |
| transposed_map_2d = AffineMap.get_permutation((1, 0)) |
| vert_line_bcast_map = AffineMap.get(2, 0, [AffineDimExpr.get(0)]) |
| hor_line_bcast_map = AffineMap.get(2, 0, [AffineDimExpr.get(1)]) |
| |
| # CHECK: func.func @elementwise_op( |
| @func.FuncOp.from_py_func( |
| # CHECK-SAME: %[[Rect:.*]]: tensor<8x16xf32>, |
| RankedTensorType.get(rect_shape, f32), |
| # CHECK-SAME: %[[RectMem:.*]]: memref<8x16xf32>, |
| MemRefType.get(rect_shape, f32), |
| # CHECK-SAME: %[[VertLine:.*]]: tensor<8xf32>, |
| RankedTensorType.get(vert_line_shape, f32), |
| # CHECK-SAME: %[[VertLineMem:.*]]: memref<8xf32>, |
| MemRefType.get(vert_line_shape, f32), |
| # CHECK-SAME: %[[HorLine:.*]]: tensor<16xf32>, |
| RankedTensorType.get(hor_line_shape, f32), |
| # CHECK-SAME: %[[HorLineMem:.*]]: memref<16xf32>, |
| MemRefType.get(hor_line_shape, f32), |
| # CHECK-SAME: %[[TransRect:.*]]: tensor<16x8xf32>, |
| RankedTensorType.get(transposed_rect_shape, f32), |
| # CHECK-SAME: %[[TransRectMem:.*]]: memref<16x8xf32>) |
| MemRefType.get(transposed_rect_shape, f32), |
| ) |
| def elementwise_op( |
| rect, |
| rect_mem, |
| vert_line, |
| vert_line_mem, |
| hor_line, |
| hor_line_mem, |
| trans_rect, |
| trans_rect_mem, |
| ): |
| # CHECK: %[[OutRect:.*]] = tensor.empty() : tensor<8x16xf32> |
| out_rect = tensor.EmptyOp(rect_shape, f32) |
| # CHECK: %[[OutRectMem:.*]] = memref.alloca() : memref<8x16xf32> |
| out_rect_mem = memref.alloca(MemRefType.get(rect_shape, f32), [], []) |
| |
| if _inferred_affine_maps := True: |
| # CHECK: linalg.elementwise |
| # CHECK-SAME: kind=#linalg.elementwise_kind<exp> |
| # CHECK-SAME: ins(%[[Rect]] : tensor<8x16xf32>) |
| # CHECK-SAME: outs(%[[OutRect]] : tensor<8x16xf32>) -> tensor<8x16xf32> |
| op1 = linalg.ElementwiseOp( |
| result_tensors=(out_rect.result.type,), |
| inputs=(rect,), |
| outputs=(out_rect,), |
| kind=linalg.ElementwiseKind.exp, |
| ) |
| linalg.fill_builtin_region(op1.operation) |
| |
| # CHECK: linalg.elementwise |
| # CHECK-SAME: kind=#linalg.elementwise_kind<exp> |
| # CHECK-SAME: ins(%[[Rect]] : tensor<8x16xf32>) |
| # CHECK-SAME: outs(%[[OutRect]] : tensor<8x16xf32>) -> tensor<8x16xf32> |
| linalg.elementwise( |
| rect, |
| outs=(out_rect,), |
| kind=linalg.ElementwiseKind.exp, |
| ) |
| |
| # CHECK: linalg.elementwise |
| # CHECK-SAME: kind=#linalg.elementwise_kind<exp> |
| # CHECK-SAME: ins(%[[RectMem]] : memref<8x16xf32>) |
| # CHECK-SAME: outs(%[[OutRectMem]] : memref<8x16xf32>) |
| linalg.elementwise( |
| rect_mem, |
| outs=(out_rect_mem,), |
| kind=linalg.ElementwiseKind.exp, |
| ) |
| |
| if _explicit_ident_affine_maps := True: |
| # Same as above but with default identity indexing_maps explicitly provided. |
| # CHECK: linalg.elementwise |
| # CHECK-SAME: kind=#linalg.elementwise_kind<exp> |
| # CHECK-SAME: ins(%[[Rect]] : tensor<8x16xf32>) |
| # CHECK-SAME: outs(%[[OutRect]] : tensor<8x16xf32>) -> tensor<8x16xf32> |
| op3 = linalg.ElementwiseOp( |
| result_tensors=(out_rect.result.type,), |
| inputs=(rect,), |
| outputs=(out_rect,), |
| kind=linalg.ElementwiseKind.exp, |
| indexing_maps=[ident_map_2d, ident_map_2d], |
| ) |
| linalg.fill_builtin_region(op3.operation) |
| |
| # CHECK: linalg.elementwise |
| # CHECK-SAME: kind=#linalg.elementwise_kind<exp> |
| # CHECK-SAME: ins(%[[RectMem]] : memref<8x16xf32>) |
| # CHECK-SAME: outs(%[[OutRectMem]] : memref<8x16xf32>) |
| linalg.elementwise( |
| rect_mem, |
| outs=(out_rect_mem,), |
| kind=linalg.ElementwiseKind.exp, |
| indexing_maps=[ident_map_2d, ident_map_2d], |
| ) |
| |
| if _ops_with_non_ident_input_maps := True: |
| # CHECK: linalg.elementwise kind=#linalg.elementwise_kind<exp> |
| # CHECK-SAME: indexing_maps = [#[[$VertLineBCastMap]], #[[$IdentMap2D]]] |
| # CHECK-SAME: ins(%[[VertLine]] : tensor<8xf32>) |
| # CHECK-SAME: outs(%[[OutRect]] : tensor<8x16xf32>) -> tensor<8x16xf32> |
| op4 = linalg.ElementwiseOp( |
| result_tensors=(out_rect.result.type,), |
| inputs=(vert_line,), |
| outputs=(out_rect,), |
| kind=linalg.ElementwiseKind.exp, |
| indexing_maps=[vert_line_bcast_map, ident_map_2d], |
| ) |
| linalg.fill_builtin_region(op4.operation) |
| |
| # CHECK: linalg.elementwise kind=#linalg.elementwise_kind<add> |
| # CHECK-SAME: indexing_maps = [#[[$IdentMap2D]], #[[$VertLineBCastMap]], #[[$IdentMap2D]]] |
| # CHECK-SAME: ins(%[[Rect]], %[[VertLine]] : tensor<8x16xf32>, tensor<8xf32>) |
| # CHECK-SAME: outs(%[[OutRect]] : tensor<8x16xf32>) -> tensor<8x16xf32> |
| op4 = linalg.ElementwiseOp( |
| result_tensors=(out_rect.result.type,), |
| inputs=(rect, vert_line), |
| outputs=(out_rect,), |
| kind=linalg.ElementwiseKind.add, |
| indexing_maps=[ident_map_2d, vert_line_bcast_map, ident_map_2d], |
| ) |
| linalg.fill_builtin_region(op4.operation) |
| |
| # CHECK: linalg.elementwise kind=#linalg.elementwise_kind<div> |
| # CHECK-SAME: indexing_maps = [#[[$VertLineBCastMap]], #[[$HorLineBCastMap]], #[[$IdentMap2D]]] |
| # CHECK-SAME: ins(%[[VertLine]], %[[HorLine]] : tensor<8xf32>, tensor<16xf32>) |
| # CHECK-SAME: outs(%[[OutRect]] : tensor<8x16xf32>) -> tensor<8x16xf32> |
| linalg.elementwise( |
| vert_line, |
| hor_line, |
| outs=(out_rect,), |
| kind=linalg.ElementwiseKind.div, |
| indexing_maps=[ |
| vert_line_bcast_map, |
| hor_line_bcast_map, |
| ident_map_2d, |
| ], |
| ) |
| |
| if _ops_with_non_ident_and_transposed_input_maps := True: |
| # CHECK: %[[VertLineBoolsMem:.*]] = memref.alloca() : memref<8xi1> |
| vert_line_bools_mem = memref.alloca( |
| MemRefType.get(vert_line_shape, IntegerType.get_signless(1)), |
| [], |
| [], |
| ) |
| # CHECK: linalg.elementwise kind=#linalg.elementwise_kind<select> |
| # CHECK-SAME: indexing_maps = [#[[$VertLineBCastMap]], #[[$HorLineBCastMap]], #[[$TransMap2D]], #[[$IdentMap2D]]] |
| # CHECK-SAME: ins(%[[VertLineBoolsMem]], %[[HorLineMem]], %[[TransRectMem]] : memref<8xi1>, memref<16xf32>, memref<16x8xf32>) |
| # CHECK-SAME: outs(%[[OutRectMem]] : memref<8x16xf32>) |
| linalg.elementwise( |
| vert_line_bools_mem, |
| hor_line_mem, |
| trans_rect_mem, |
| outs=(out_rect_mem,), |
| kind=linalg.ElementwiseKind.select, |
| indexing_maps=[ |
| vert_line_bcast_map, |
| hor_line_bcast_map, |
| transposed_map_2d, |
| ident_map_2d, |
| ], |
| ) |
| |
| print(module) |
| |
| |
| @run |
| def testReduceOp(): |
| with Context(), Location.unknown(): |
| f32 = T.f32() |
| tensor_type = T.tensor(10, f32) |
| |
| @builtin.module |
| def module(): |
| @func.func(tensor_type) |
| def reduce_op(input): |
| c1 = arith.constant(f32, 1.0) |
| single_result = ir.RankedTensorType.get((), f32) |
| dims = ir.DenseI64ArrayAttr.get([0]) |
| init = tensor.splat(single_result, c1, []) |
| |
| @linalg.reduce( |
| result=[single_result], |
| inputs=[input], |
| inits=[init], |
| dimensions=dims, |
| ) |
| def reduced(element: f32, acc: f32): |
| return arith.mulf(acc, element) |
| |
| return tensor.extract(reduced, []) |
| |
| print(module) |
| |
| |
| # CHECK-LABEL: func.func @reduce_op( |
| # CHECK-SAME: %[[ARG0:.*]]: tensor<10xf32>) -> f32 { |
| # CHECK: %[[CONSTANT_0:.*]] = arith.constant 1.000000e+00 : f32 |
| # CHECK: %[[SPLAT_0:.*]] = tensor.splat %[[CONSTANT_0]] : tensor<f32> |
| # CHECK: %[[REDUCE_0:.*]] = linalg.reduce { arith.mulf } ins(%[[ARG0]] : tensor<10xf32>) outs(%[[SPLAT_0]] : tensor<f32>) dimensions = [0] |
| # CHECK: %[[EXTRACT_0:.*]] = tensor.extract %[[REDUCE_0]][] : tensor<f32> |
| # CHECK: return %[[EXTRACT_0]] : f32 |
| # CHECK: } |
| |
| |
| @run |
| def testMapOp(): |
| with Context(), Location.unknown(): |
| f32 = T.f32() |
| tensor_type = T.tensor(10, f32) |
| |
| @builtin.module |
| def module(): |
| @func.func(tensor_type) |
| def map_op(input): |
| empty = tensor.empty(tensor_type.shape, f32) |
| |
| @linalg.map( |
| result=[tensor_type], |
| inputs=[input, input], |
| init=empty, |
| ) |
| def add(element: f32, acc: f32, init: f32): |
| return arith.addf(element, acc) |
| |
| return add |
| |
| module.verify() |
| print(module) |
| |
| |
| # CHECK-LABEL: func.func @map_op( |
| # CHECK-SAME: %[[ARG0:.*]]: tensor<10xf32>) -> tensor<10xf32> { |
| # CHECK: %[[EMPTY_0:.*]] = tensor.empty() : tensor<10xf32> |
| # CHECK: %[[MAP_0:.*]] = linalg.map { arith.addf } ins(%[[ARG0]], %[[ARG0]] : tensor<10xf32>, tensor<10xf32>) outs(%[[EMPTY_0]] : tensor<10xf32>) |
| # CHECK: return %[[MAP_0]] : tensor<10xf32> |
| # CHECK: } |
