1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
|
# RUN: %PYTHON %s | FileCheck %s
from mlir.ir import *
from mlir.dialects import builtin
from mlir.dialects import linalg
from mlir.dialects import std
from mlir.dialects.linalg.opdsl.lang import *
# This tests miscellaneous features of the emitter that are not tested by the
# matmul, convolution, or, pooling tests. The features include:
# - constant defined in the body
# - fix/predefined types
# - exponential functions
# - custom op names.
@linalg_structured_op
def fill_rng_poly(
min=ScalarDef(F64),
max=ScalarDef(F64),
seed=ScalarDef(I32),
O=TensorDef(T, S.M, S.N, output=True)):
multiplier = TypeFn.cast(I32, const(1103515245))
increment = TypeFn.cast(I32, const(12345))
rand1 = (TypeFn.cast(I32, index(D.m)) + seed) * multiplier + increment
rand2 = (TypeFn.cast(I32, index(D.n)) + rand1) * multiplier + increment
inv_range = TypeFn.cast(F64, const(2.3283064e-10))
offset = TypeFn.cast(F64, const(2147483647))
scaling = (max - min) * inv_range
O[D.m, D.n] = TypeFn.cast(T,
(offset + TypeFn.cast(F64, rand2)) * scaling + min)
@linalg_structured_op
def soft_plus_poly(
I=TensorDef(T, S.M, S.N), O=TensorDef(U, S.M, S.N, output=True)):
O[D.m, D.n] = ArithFn.log(
TypeFn.cast(U, const(1.0)) + TypeFn.cast(U, ArithFn.exp(I[D.m, D.n])))
@linalg_structured_op(op_name="custom_op_name")
def non_default_op_name(I=TensorDef(T, S.N), O=TensorDef(T, S.N, output=True)):
O[D.n] = I[D.n]
with Context() as ctx, Location.unknown():
module = Module.create()
f32 = F32Type.get()
f64 = F64Type.get()
i32 = IntegerType.get_signless(32)
with InsertionPoint(module.body):
# CHECK-LABEL: @test_i32_fill_rng
# CHECK: ^{{.*}}(%[[MIN:.+]]: f64, %[[MAX:.+]]: f64, %[[SEED:.+]]: i32, %{{.*}}
# CHECK-DAG: %[[IDX0:.+]] = linalg.index 0 : index
# CHECK-DAG: %[[IDX0_CAST:.+]] = arith.index_cast %[[IDX0]] : index to i32
# CHECK-DAG: %[[RND0:.+]] = arith.addi %[[IDX0_CAST]], %[[SEED]] : i32
# CHECK-DAG: %[[CST0:.+]] = arith.constant 1103515245 : i64
# CHECK-DAG: %[[CST0_CAST:.+]] = arith.trunci %[[CST0]] : i64 to i32
# Skip the remaining random number computation and match the scaling logic.
# CHECK-DAG: %[[DIFF:.+]] = arith.subf %[[MAX]], %[[MIN]] : f64
# CHECK-DAG: %[[CST3:.+]] = arith.constant 2.3283063999999999E-10 : f64
# CHECK-DAG: %[[FACT:.+]] = arith.mulf %[[DIFF]], %[[CST3]] : f64
# CHECK-DAG: %[[RND4:.+]] = arith.mulf %{{.+}}, %[[FACT]] : f64
# CHECK-DAG: %[[RND5:.+]] = arith.addf %[[RND4]], %[[MIN]] : f64
# CHECK-DAG: %{{.*}} = arith.fptosi %[[RND5]] : f64 to i32
@builtin.FuncOp.from_py_func(f64, f64, i32,
RankedTensorType.get((4, 16), i32))
def test_i32_fill_rng(min, max, seed, init_result):
return fill_rng_poly(min, max, seed, outs=[init_result])
# CHECK-LABEL: @test_f32_soft_plus
# CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[OUT:.+]]: f32)
# CHECK-NEXT: %[[C1:.+]] = arith.constant 1.000000e+00 : f64
# CHECK-NEXT: %[[C1_CAST:.+]] = arith.truncf %[[C1]] : f64 to f32
# CHECK-NEXT: %[[EXP:.+]] = math.exp %[[IN]] : f32
# CHECK-NEXT: %[[SUM:.+]] = arith.addf %[[C1_CAST]], %[[EXP]] : f32
# CHECK-NEXT: %[[LOG:.+]] = math.log %[[SUM]] : f32
# CHECK-NEXT: linalg.yield %[[LOG]] : f32
# CHECK-NEXT: -> tensor<4x16xf32>
@builtin.FuncOp.from_py_func(
RankedTensorType.get((4, 16), f32), RankedTensorType.get((4, 16), f32))
def test_f32_soft_plus(input, init_result):
return soft_plus_poly(input, outs=[init_result])
# Just check that we don't assert out on name mismatch.
# CHECK-LABEL: @test_non_default_op_name
@builtin.FuncOp.from_py_func(
RankedTensorType.get((42,), f32), RankedTensorType.get((42,), f32))
def test_non_default_op_name(input, init_result):
return non_default_op_name(input, outs=[init_result])
print(module)
|
