1 /*
2 * Copyright (c) 2017-2021 Arm Limited.
3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24 #include "src/cpu/kernels/CpuWeightsReshapeKernel.h"
25
26 #include "arm_compute/core/Helpers.h"
27 #include "arm_compute/core/Validate.h"
28 #include "src/core/helpers/AutoConfiguration.h"
29 #include "src/core/helpers/WindowHelpers.h"
30
31 namespace arm_compute
32 {
33 namespace cpu
34 {
35 namespace kernels
36 {
37 namespace
38 {
get_output_shape(const ITensorInfo * src,bool has_bias)39 TensorShape get_output_shape(const ITensorInfo *src, bool has_bias)
40 {
41 TensorShape output_shape{ src->tensor_shape() };
42
43 output_shape.collapse(3);
44 const size_t tmp_dim = output_shape[0];
45 output_shape.set(0, output_shape[1]);
46 output_shape.set(1, tmp_dim + (has_bias ? 1 : 0));
47
48 return output_shape;
49 }
50
validate_arguments(const ITensorInfo * src,const ITensorInfo * biases,const ITensorInfo * dst)51 Status validate_arguments(const ITensorInfo *src, const ITensorInfo *biases, const ITensorInfo *dst)
52 {
53 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
54 //Note: ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src) is not needed here as this kernel doesn't use CPU FP16 instructions.
55 ARM_COMPUTE_RETURN_ERROR_ON(src->data_type() == DataType::UNKNOWN);
56
57 if(biases != nullptr)
58 {
59 ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(src->data_type()));
60 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases);
61 ARM_COMPUTE_RETURN_ERROR_ON((src->num_dimensions() == 4) && (biases->num_dimensions() != 1));
62 ARM_COMPUTE_RETURN_ERROR_ON((src->num_dimensions() == 5) && (biases->num_dimensions() != 2));
63 ARM_COMPUTE_RETURN_ERROR_ON((src->num_dimensions() == 4) && (biases->dimension(0) != src->tensor_shape()[3]));
64 ARM_COMPUTE_RETURN_ERROR_ON((src->num_dimensions() == 5) && (biases->dimension(0) != src->tensor_shape()[3] || biases->dimension(1) != src->tensor_shape()[4]));
65 }
66
67 // Checks performed when output is configured
68 if(dst->total_size() != 0)
69 {
70 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(dst->tensor_shape(), get_output_shape(src, biases != nullptr));
71 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, dst);
72 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(src, dst);
73 }
74
75 return Status{};
76 }
77 } // namespace
78
configure(const ITensorInfo * src,const ITensorInfo * biases,ITensorInfo * dst)79 void CpuWeightsReshapeKernel::configure(const ITensorInfo *src, const ITensorInfo *biases, ITensorInfo *dst)
80 {
81 ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
82
83 // Output tensor auto inizialitation if not yet initialized
84 auto_init_if_empty(*dst, src->clone()->set_tensor_shape(get_output_shape(src, (biases != nullptr))));
85
86 // Perform validation step
87 ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src,
88 biases,
89 dst));
90
91 // Configure kernel
92 Window window = calculate_max_window(*src, Steps());
93 window.set(Window::DimX, Window::Dimension(0, src->dimension(0), src->dimension(0)));
94 window.set(Window::DimY, Window::Dimension(0, src->dimension(1), src->dimension(1)));
95 window.set(Window::DimZ, Window::Dimension(0, src->dimension(2), src->dimension(2)));
96 ICpuKernel::configure(window);
97 }
98
validate(const ITensorInfo * src,const ITensorInfo * biases,const ITensorInfo * dst)99 Status CpuWeightsReshapeKernel::validate(const ITensorInfo *src, const ITensorInfo *biases, const ITensorInfo *dst)
100 {
101 ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, biases, dst));
102 return Status{};
103 }
104
run_op(ITensorPack & tensors,const Window & window,const ThreadInfo & info)105 void CpuWeightsReshapeKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
106 {
107 ARM_COMPUTE_UNUSED(info);
108 ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
109 ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
110
111 auto src = tensors.get_const_tensor(TensorType::ACL_SRC);
112 auto biases = tensors.get_const_tensor(TensorType::ACL_BIAS);
113 auto dst = tensors.get_tensor(TensorType::ACL_DST);
114
115 const unsigned int kernel_size_x = src->info()->dimension(0);
116 const unsigned int kernel_size_y = src->info()->dimension(1);
117 const unsigned int kernel_depth = src->info()->dimension(2);
118 const unsigned int input_stride_x = src->info()->strides_in_bytes().x();
119 const unsigned int input_stride_y = src->info()->strides_in_bytes().y();
120 const unsigned int input_stride_z = src->info()->strides_in_bytes().z();
121 const unsigned int output_stride_y = dst->info()->strides_in_bytes().y();
122
123 // Create iterators
124 Iterator in(src, window);
125 execute_window_loop(window, [&](const Coordinates & id)
126 {
127 // Get column index
128 const int kernel_idx = id[3];
129 const int kernel_idz = id[4];
130
131 // Setup pointers
132 const uint8_t *tmp_input_ptr = in.ptr();
133 uint8_t *tmp_output_ptr = dst->ptr_to_element(Coordinates(kernel_idx, 0, kernel_idz));
134 const uint8_t *curr_input_row_ptr = tmp_input_ptr;
135 const uint8_t *curr_input_depth_ptr = tmp_input_ptr;
136
137 // Linearize volume
138 for(unsigned int d = 0; d < kernel_depth; ++d)
139 {
140 for(unsigned int j = 0; j < kernel_size_y; ++j)
141 {
142 for(unsigned int i = 0; i < kernel_size_x; ++i)
143 {
144 std::memcpy(tmp_output_ptr, tmp_input_ptr, src->info()->element_size());
145 tmp_input_ptr += input_stride_x;
146 tmp_output_ptr += output_stride_y;
147 }
148 curr_input_row_ptr += input_stride_y;
149 tmp_input_ptr = curr_input_row_ptr;
150 }
151 curr_input_depth_ptr += input_stride_z;
152 curr_input_row_ptr = curr_input_depth_ptr;
153 tmp_input_ptr = curr_input_depth_ptr;
154 }
155
156 // Add bias
157 if(biases != nullptr)
158 {
159 std::memcpy(tmp_output_ptr, biases->ptr_to_element(Coordinates(kernel_idx, kernel_idz)), src->info()->element_size());
160 }
161 },
162 in);
163 }
name() const164 const char *CpuWeightsReshapeKernel::name() const
165 {
166 return "CpuWeightsReshapeKernel";
167 }
168 } // namespace kernels
169 } // namespace cpu
170 } // namespace arm_compute