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
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
|
/*
* Copyright (c) 2022 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 "ClKernelRuntime.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "src/core/CL/CLUtils.h"
#include "src/dynamic_fusion/sketch/gpu/GpuKernelSourceCode.h"
#include "src/gpu/cl/ClKernelLibrary.h"
#include "support/Cast.h"
namespace arm_compute
{
namespace experimental
{
namespace dynamic_fusion
{
using namespace arm_compute::opencl;
void ClKernelRuntime::configure(const ClCompileContext &compile_ctx, const GpuKernelSourceCode &code)
{
// Create kernel from kernel source string
opencl::ClKernelLibrary &klib = opencl::ClKernelLibrary::get();
_kernel = static_cast<cl::Kernel>(compile_ctx.create_kernel(code.name(),
"" /* Program name: Used to as part of a unique string for built kernel cache. Not needed */,
code.code(),
klib.kernel_path() /* Kernel path: Used in cases of embedded kernels */,
code.build_options().options(),
false /* Is source binary */));
// Configure execution window
IClKernel::configure_internal(code.window());
// Set config id for lws tuning
_config_id = code.config_id();
// Set kernel arguments
_arguments = code.arguments();
}
inline void ClKernelRuntime::add_tensor_argument(unsigned int &idx, const GpuKernelArgumentInfo &arg, const ICLTensor *tensor, const Window &arg_slice, std::vector<cl::Image2D> &cl_images)
{
switch(arg.type)
{
case GpuKernelArgumentInfo::Type::Scalar:
{
ARM_COMPUTE_ERROR("Unsupported yet");
break;
}
case GpuKernelArgumentInfo::Type::Vector:
{
add_1D_tensor_argument(idx, tensor, arg_slice);
break;
}
case GpuKernelArgumentInfo::Type::Image:
{
add_2D_tensor_argument(idx, tensor, arg_slice);
break;
}
case GpuKernelArgumentInfo::Type::Image_Reinterpret_As_3D:
{
add_2D_tensor_argument(idx, tensor, arg_slice);
const unsigned int total_cross_plane_pad = tensor->info()->padding().top + tensor->info()->padding().bottom;
_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(total_cross_plane_pad));
break;
}
case GpuKernelArgumentInfo::Type::Image_Export_To_ClImage2D:
{
const TensorShape shape2d(tensor->info()->dimension(0) / 4, tensor->info()->dimension(1) * tensor->info()->dimension(2) * tensor->info()->dimension(3));
const size_t image_row_pitch = tensor->info()->strides_in_bytes()[1];
cl::Image2D tensor_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), tensor->cl_buffer(), shape2d, tensor->info()->data_type(), image_row_pitch);
cl_images.push_back(tensor_image2d);
_kernel.setArg(idx++, tensor_image2d);
break;
}
case GpuKernelArgumentInfo::Type::Image_3D:
{
add_2D_tensor_argument(idx, tensor, arg_slice);
_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(tensor->info()->strides_in_bytes()[2]));
break;
}
case GpuKernelArgumentInfo::Type::Image_3D_Export_To_ClImage2D:
{
const TensorShape shape2d(tensor->info()->dimension(0) / 4, tensor->info()->dimension(1) * tensor->info()->dimension(2) * tensor->info()->dimension(3));
const size_t image_row_pitch = tensor->info()->strides_in_bytes()[1];
cl::Image2D tensor_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), tensor->cl_buffer(), shape2d, tensor->info()->data_type(), image_row_pitch);
cl_images.push_back(tensor_image2d);
_kernel.setArg(idx++, tensor_image2d);
_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(tensor->info()->strides_in_bytes()[2]));
break;
}
case GpuKernelArgumentInfo::Type::Tensor_3D:
{
add_3D_tensor_argument(idx, tensor, arg_slice);
break;
}
case GpuKernelArgumentInfo::Type::Tensor_4D:
{
add_4D_tensor_argument(idx, tensor, arg_slice);
break;
}
case GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer:
{
add_4d_tensor_nhwc_argument(idx, tensor);
break;
}
case GpuKernelArgumentInfo::Type::Tensor_4D_t_Image:
{
const size_t image_w = tensor->info()->dimension(0) / 4;
const size_t image_h = tensor->info()->tensor_shape().total_size_upper(1);
const size_t image_stride_y = tensor->info()->strides_in_bytes()[1];
cl::Image2D tensor_image2d = create_image2d_from_buffer(CLKernelLibrary::get().context(), tensor->cl_buffer(),
TensorShape(image_w, image_h), tensor->info()->data_type(), image_stride_y);
cl_images.push_back(tensor_image2d);
_kernel.setArg(idx++, tensor_image2d);
add_4d_tensor_nhwc_argument(idx, tensor);
break;
}
default:
{
ARM_COMPUTE_ERROR("Unsupported");
}
}
}
void ClKernelRuntime::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
Window slice = window.first_slice_window_3D();
// Don't slice matrix along the z dimension if matrix has just 2 dimensions and matrix A more than 2
// This scenario can happen when the matrix multiplication is used to perform a convolution operation
Window slice_fixed_z = slice;
slice_fixed_z.set(Window::DimX, Window::Dimension(0, 1, 1));
slice_fixed_z.set(Window::DimY, Window::Dimension(0, 1, 1));
/// NOTE: Parameters extracted from old kernels. So far they seem to be constant
/// but we may need to make them into another configuration passed from GpuWorkloadSourceCode if needed in the future
constexpr bool slide_along_dimz = true;
constexpr bool skip_sliding_window = false;
constexpr bool use_dummy_work_items = false;
unsigned int idx = 0;
do
{
// Set kernel arguments
Window arg_slice = slice;
// CLImages created from tensor arguments. Need to be retained until enqueue
std::vector<cl::Image2D> cl_images;
for(auto id_arg : _arguments)
{
const auto arg = id_arg.second;
auto tensor = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(id_arg.first));
ARM_COMPUTE_ERROR_ON_NULLPTR(tensor);
ARM_COMPUTE_ERROR_ON_NULLPTR(tensor->info());
if(!slide_along_dimz)
{
// The stride_z for matrix must be zero if we do not slice
ARM_COMPUTE_ERROR_ON(tensor->info()->strides_in_bytes()[3] != 0);
arg_slice = slice_fixed_z;
}
add_tensor_argument(idx, *arg.kernel_argument_info(), tensor, arg_slice, cl_images);
}
// Dispatch kernel
enqueue(queue, *this, slice, lws_hint(), use_dummy_work_items);
}
while(skip_sliding_window && window.slide_window_slice_3D(slice));
}
} // namespace dynamic_fusion
} // namespace experimental
} // namespace arm_compute
|
