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
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
|
/*
* Copyright (c) 2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "arm_compute/runtime/CL/tuners/BifrostTuner.h"
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLKernels.h"
#include "arm_compute/core/utils/misc/Cast.h"
namespace arm_compute
{
namespace tuners
{
namespace
{
/** Tunes a @ref CLDirectConvolutionLayerKernel for a bifrost target
*
* @param[in] k Kernels to tune
*/
void tune_direct_convolution_kernel(CLDirectConvolutionLayerKernel &k)
{
cl::NDRange lws_hint = k.lws_hint();
const GPUTarget gpu_target = k.get_target();
const DataType dt = k._input->info()->data_type();
const TensorShape weights_shape = k._weights->info()->tensor_shape();
const TensorShape inputs_shape = k._input->info()->tensor_shape();
const size_t kernel_size = weights_shape.x();
const unsigned int stride_x = k._conv_stride_x;
const unsigned int stride_y = k._conv_stride_y;
if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72) && (kernel_size <= 5) && (stride_x == 1) && (stride_y == 1) && (dt == DataType::F32))
{
// Through extensive experimentation with over 30 representative tensor
// shapes, we found a small number of local work size configurations
// that result in nearly optimal execution times. Selecting the right
// lws for a given shape, however, required a complex decision tree,
// until we constructed a simple feature as described below.
//
// We started from the number of multiply-accumulate operations for a
// convolution layer, which is equal to the product of the input
// dimensions 0..2 and the weights dimensions 0..2. Unfortunately,
// this resulted in ties between distinct shapes that required distinct
// lws configurations. Replacing the width of the input with the kernel
// size, however, resulted in nearly optimal predictions. We use underscores
// in variable names to indicate when they are intentionally misleading.
const size_t product_of_weights_dimensions = weights_shape[0] * weights_shape[1] * weights_shape[2];
const size_t product_of_input_dimensions_ = inputs_shape[0] * inputs_shape[1] * inputs_shape[2];
const float mega_ops_ = 1e-6 * product_of_weights_dimensions * product_of_input_dimensions_;
switch(kernel_size)
{
case 1:
{
if(mega_ops_ < 1.f)
{
lws_hint = cl::NDRange(1, 1, 8);
}
else if(mega_ops_ < 7.f)
{
lws_hint = cl::NDRange(1, 1, 4);
}
else
{
lws_hint = cl::NDRange(1, 1, 2);
}
break;
}
case 3:
{
if(mega_ops_ < 1.f)
{
lws_hint = cl::NDRange(1, 1, 8);
}
else if(mega_ops_ < 13.f)
{
lws_hint = cl::NDRange(2, 1, 4);
}
else if(mega_ops_ < 50.f)
{
lws_hint = cl::NDRange(3, 1, 4);
}
else
{
lws_hint = cl::NDRange(2, 1, 6);
}
break;
}
case 5:
{
if(mega_ops_ < 2.f || mega_ops_ > 80.f)
{
lws_hint = cl::NDRange(2, 1, 4);
}
else
{
lws_hint = cl::NDRange(2, 1, 8);
}
break;
}
default:
break;
}
k.set_lws_hint(lws_hint);
}
}
} // namespace
void BifrostTuner::tune_kernel_static(ICLKernel &kernel)
{
// Continue on tuning if dynamic tuning
if(dynamic_cast<CLDirectConvolutionLayerKernel *>(&kernel) != nullptr)
{
tune_direct_convolution_kernel(*utils::cast::polymorphic_downcast<CLDirectConvolutionLayerKernel *>(&kernel));
}
}
void BifrostTuner::tune_kernel_dynamic(ICLKernel &kernel)
{
ARM_COMPUTE_UNUSED(kernel);
}
} // namespace tuners
} // namespace arm_compute
|
