1# RUN: %PYTHON %s | FileCheck %s
2
3from mlir.ir import *
4from mlir.dialects import builtin
5from mlir.dialects import linalg
6from mlir.dialects import std
7from mlir.dialects import arith
8
9
10def run(f):
11  print("\nTEST:", f.__name__)
12  f()
13  return f
14
15
16# CHECK-LABEL: TEST: testInitTensor
17@run
18def testInitTensor():
19  with Context() as ctx, Location.unknown():
20    module = Module.create()
21    f32 = F32Type.get()
22    with InsertionPoint(module.body):
23      # CHECK-LABEL: func @static_sizes
24      # CHECK: %0 = linalg.init_tensor [3, 4] : tensor<3x4xf32>
25      @builtin.FuncOp.from_py_func()
26      def static_sizes():
27        return linalg.InitTensorOp([3, 4], f32)
28
29      # CHECK-LABEL: func @dynamic_sizes
30      # CHECK: %0 = linalg.init_tensor [%arg0, %arg1] : tensor<?x?xf32>
31      @builtin.FuncOp.from_py_func(IndexType.get(), IndexType.get())
32      def dynamic_sizes(d0, d1):
33        return linalg.InitTensorOp([d0, d1], f32)
34
35      # CHECK-LABEL: func @zero_d
36      # CHECK: %0 = linalg.init_tensor [] : tensor<f32>
37      @builtin.FuncOp.from_py_func()
38      def zero_d():
39        return linalg.InitTensorOp([], f32)
40
41  print(module)
42
43
44# CHECK-LABEL: TEST: testInitTensorStaticSizesAttribute
45@run
46def testInitTensorStaticSizesAttribute():
47  with Context() as ctx, Location.unknown():
48    module = Module.create()
49    f32 = F32Type.get()
50    with InsertionPoint(module.body):
51      op = linalg.InitTensorOp([3, 4], f32)
52      # CHECK: [3, 4]
53      print(op.attributes["static_sizes"])
54
55
56# CHECK-LABEL: TEST: testFill
57@run
58def testFill():
59  with Context() as ctx, Location.unknown():
60    module = Module.create()
61    f32 = F32Type.get()
62    with InsertionPoint(module.body):
63      # CHECK-LABEL: func @fill_tensor
64      #  CHECK-SAME:   %[[OUT:[0-9a-z]+]]: tensor<12x?xf32>
65      #  CHECK-NEXT: %[[CST:.*]] = arith.constant 0.0{{.*}} : f32
66      #  CHECK-NEXT: %[[RES:.*]] = linalg.fill(%[[CST]], %[[OUT]]) : f32, tensor<12x?xf32> -> tensor<12x?xf32>
67      #  CHECK-NEXT: return %[[RES]] : tensor<12x?xf32>
68      @builtin.FuncOp.from_py_func(RankedTensorType.get((12, -1), f32))
69      def fill_tensor(out):
70        zero = arith.ConstantOp(value=FloatAttr.get(f32, 0.), result=f32).result
71        return linalg.FillOp(output=out, value=zero).result
72
73      # CHECK-LABEL: func @fill_buffer
74      #  CHECK-SAME:   %[[OUT:[0-9a-z]+]]: memref<12x?xf32>
75      #  CHECK-NEXT: %[[CST:.*]] = arith.constant 0.0{{.*}} : f32
76      #  CHECK-NEXT: linalg.fill(%[[CST]], %[[OUT]]) : f32, memref<12x?xf32>
77      #  CHECK-NEXT: return
78      @builtin.FuncOp.from_py_func(MemRefType.get((12, -1), f32))
79      def fill_buffer(out):
80        zero = arith.ConstantOp(value=FloatAttr.get(f32, 0.), result=f32).result
81        linalg.FillOp(output=out, value=zero)
82
83  print(module)
84
85
86# CHECK-LABEL: TEST: testStructuredOpOnTensors
87@run
88def testStructuredOpOnTensors():
89  with Context() as ctx, Location.unknown():
90    module = Module.create()
91    f32 = F32Type.get()
92    tensor_type = RankedTensorType.get((2, 3, 4), f32)
93    with InsertionPoint(module.body):
94      func = builtin.FuncOp(
95          name="matmul_test",
96          type=FunctionType.get(
97              inputs=[tensor_type, tensor_type], results=[tensor_type]))
98      with InsertionPoint(func.add_entry_block()):
99        lhs, rhs = func.entry_block.arguments
100        result = linalg.MatmulOp([lhs, rhs], results=[tensor_type]).result
101        std.ReturnOp([result])
102
103  # CHECK: %[[R:.*]] = linalg.matmul ins(%arg0, %arg1 : tensor<2x3x4xf32>, tensor<2x3x4xf32>) -> tensor<2x3x4xf32>
104  print(module)
105
106
107# CHECK-LABEL: TEST: testStructuredOpOnBuffers
108@run
109def testStructuredOpOnBuffers():
110  with Context() as ctx, Location.unknown():
111    module = Module.create()
112    f32 = F32Type.get()
113    memref_type = MemRefType.get((2, 3, 4), f32)
114    with InsertionPoint(module.body):
115      func = builtin.FuncOp(
116          name="matmul_test",
117          type=FunctionType.get(
118              inputs=[memref_type, memref_type, memref_type], results=[]))
119      with InsertionPoint(func.add_entry_block()):
120        lhs, rhs, result = func.entry_block.arguments
121        # TODO: prperly hook up the region.
122        linalg.MatmulOp([lhs, rhs], outputs=[result])
123        std.ReturnOp([])
124
125  # CHECK: linalg.matmul ins(%arg0, %arg1 : memref<2x3x4xf32>, memref<2x3x4xf32>) outs(%arg2 : memref<2x3x4xf32>)
126  print(module)
127
128
129# CHECK-LABEL: TEST: testNamedStructuredOpCustomForm
130@run
131def testNamedStructuredOpCustomForm():
132  with Context() as ctx, Location.unknown():
133    module = Module.create()
134    f32 = F32Type.get()
135    with InsertionPoint(module.body):
136
137      @builtin.FuncOp.from_py_func(
138          RankedTensorType.get((4, 16), f32), RankedTensorType.get((16, 8),
139                                                                   f32))
140      def named_form(lhs, rhs):
141        init_result = linalg.InitTensorOp([4, 8], f32)
142        # First check the named form with custom format
143        #      CHECK: linalg.matmul
144        #  CHECK-NOT: linalg.memoized_indexing_maps
145        # CHECK-SAME:    ins(%{{.*}} : tensor<4x16xf32>, tensor<16x8xf32>)
146        # CHECK-SAME:   outs(%{{.*}} : tensor<4x8xf32>)
147        # CHECK-SAME:   -> tensor<4x8xf32>
148        # CHECK-NEXT: return
149        return linalg.matmul(lhs, rhs, outs=[init_result.result])
150
151  print(module)
152
153
154# CHECK-LABEL: TEST: testNamedStructuredOpGenericForm
155@run
156def testNamedStructuredOpGenericForm():
157  with Context() as ctx, Location.unknown():
158    module = Module.create()
159    f32 = F32Type.get()
160    with InsertionPoint(module.body):
161
162      @builtin.FuncOp.from_py_func(
163          RankedTensorType.get((4, 16), f32), RankedTensorType.get((16, 8),
164                                                                   f32))
165      def named_form(lhs, rhs):
166        init_result = linalg.InitTensorOp([4, 8], f32)
167        #      CHECK: "linalg.matmul"(%{{.*}})
168        # CHECK-NEXT:  ^bb0(%{{.*}}: f32, %{{.*}}: f32, %{{.*}}: f32):
169        # CHECK-NEXT:    arith.mulf{{.*}} (f32, f32) -> f32
170        # CHECK-NEXT:    arith.addf{{.*}} (f32, f32) -> f32
171        # CHECK-NEXT:    linalg.yield{{.*}} (f32) -> ()
172        # CHECK-NEXT:    {linalg.memoized_indexing_maps{{.*}}operand_segment_sizes = dense<[2, 1]> : vector<2xi32>} :
173        # CHECK-SAME: (tensor<4x16xf32>, tensor<16x8xf32>, tensor<4x8xf32>) -> tensor<4x8xf32>
174        return linalg.matmul(lhs, rhs, outs=[init_result.result])
175
176  module.operation.print(print_generic_op_form=True)
177
178
179# CHECK-LABEL: TEST: testNamedStructuredAsGenericOp
180@run
181def testNamedStructuredAsGenericOp():
182  with Context() as ctx, Location.unknown():
183    module = Module.create()
184    f32 = F32Type.get()
185    with InsertionPoint(module.body):
186
187      @builtin.FuncOp.from_py_func(
188          RankedTensorType.get((4, 16), f32), RankedTensorType.get((16, 8),
189                                                                   f32))
190      def generic_form(lhs, rhs):
191        init_result = linalg.InitTensorOp([4, 8], f32)
192        # CHECK: linalg.generic
193        return linalg.matmul(
194            lhs, rhs, outs=[init_result.result], emit_generic=True)
195
196  print(module)
197
198
199# CHECK-LABEL: TEST: testOpResultFromOtherOp
200@run
201def testOpResultFromOtherOp():
202  with Context(), Location.unknown():
203    module = Module.create()
204    f32 = F32Type.get()
205    with InsertionPoint(module.body):
206
207      @builtin.FuncOp.from_py_func(
208          RankedTensorType.get((4, 16), f32), RankedTensorType.get((16, 8),
209                                                                   f32))
210      def pass_an_op_directly(arg0, arg1):
211        one = arith.ConstantOp(F32Type.get(), 1.0)
212        # CHECK: %[[LHS:.*]] = linalg.fill
213        lhs = linalg.FillOp(arg0, one)
214        # CHECK: %[[RHS:.*]] = linalg.fill
215        rhs = linalg.FillOp(arg1, one)
216        # CHECK: %[[INIT:.*]] = linalg.init_tensor
217        init = linalg.InitTensorOp([4, 8], f32)
218        # CHECK: linalg.matmul
219        # CHECK: ins(%[[LHS]], %[[RHS]]
220        # CHECK: outs(%[[INIT]]
221        return linalg.matmul(lhs, rhs, outs=init)
222
223  print(module)
224