1 /*
2 * Copyright (c) 2018-2021 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 "arm_compute/runtime/CL/functions/CLRNNLayer.h"
25
26 #include "arm_compute/core/Helpers.h"
27 #include "arm_compute/core/Types.h"
28 #include "arm_compute/core/Utils.h"
29 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
30 #include "arm_compute/runtime/CL/CLScheduler.h"
31 #include "src/core/CL/kernels/CLFillBorderKernel.h"
32
33 #include "src/common/utils/Log.h"
34
35 namespace arm_compute
36 {
37 using namespace arm_compute::misc::shape_calculator;
38
CLRNNLayer(std::shared_ptr<IMemoryManager> memory_manager)39 CLRNNLayer::CLRNNLayer(std::shared_ptr<IMemoryManager> memory_manager)
40 : _memory_group(std::move(memory_manager)), _gemm_state_f(), _add_kernel(), _activation(), _fully_connected_kernel(), _copy(), _fully_connected_out(), _gemm_output(), _add_output(),
41 _is_prepared(false)
42 {
43 }
44
45 CLRNNLayer::~CLRNNLayer() = default;
46
validate(const ITensorInfo * input,const ITensorInfo * weights,const ITensorInfo * recurrent_weights,const ITensorInfo * bias,const ITensorInfo * hidden_state,const ITensorInfo * output,const ActivationLayerInfo & info)47 Status CLRNNLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *recurrent_weights, const ITensorInfo *bias, const ITensorInfo *hidden_state,
48 const ITensorInfo *output, const ActivationLayerInfo &info)
49 {
50 ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, recurrent_weights, bias, hidden_state, output);
51 ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(input, DataType::F16, DataType::F32);
52 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, recurrent_weights, bias, hidden_state, output);
53
54 const int idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
55 const int idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
56
57 ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_width) != weights->dimension(idx_width));
58 ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_height) != recurrent_weights->dimension(idx_width));
59 ARM_COMPUTE_RETURN_ERROR_ON(recurrent_weights->dimension(idx_width) != recurrent_weights->dimension(1));
60 ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() != 1);
61 ARM_COMPUTE_RETURN_ERROR_ON(bias->dimension(idx_width) != weights->dimension(idx_height));
62 ARM_COMPUTE_RETURN_ERROR_ON(hidden_state->dimension(idx_width) != weights->dimension(idx_height));
63 ARM_COMPUTE_RETURN_ERROR_ON(hidden_state->dimension(idx_height) != input->dimension(idx_height));
64 ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), hidden_state->tensor_shape());
65
66 auto shape_info = TensorInfo(compute_rnn_shape(recurrent_weights, hidden_state->dimension(idx_height)), 1, input->data_type());
67
68 ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, weights, bias, &shape_info));
69 ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(hidden_state, recurrent_weights, nullptr, &shape_info, 1.f, 0.f));
70 ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&shape_info, &shape_info, &shape_info, ConvertPolicy::SATURATE));
71 ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&shape_info, &shape_info, info));
72
73 return Status{};
74 }
75
configure(const ICLTensor * input,const ICLTensor * weights,const ICLTensor * recurrent_weights,const ICLTensor * bias,ICLTensor * hidden_state,ICLTensor * output,ActivationLayerInfo & info)76 void CLRNNLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *recurrent_weights, const ICLTensor *bias, ICLTensor *hidden_state, ICLTensor *output,
77 ActivationLayerInfo &info)
78 {
79 configure(CLKernelLibrary::get().get_compile_context(), input, weights, recurrent_weights, bias, hidden_state, output, info);
80 }
81
configure(const CLCompileContext & compile_context,const ICLTensor * input,const ICLTensor * weights,const ICLTensor * recurrent_weights,const ICLTensor * bias,ICLTensor * hidden_state,ICLTensor * output,ActivationLayerInfo & info)82 void CLRNNLayer::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *recurrent_weights, const ICLTensor *bias,
83 ICLTensor *hidden_state,
84 ICLTensor *output, ActivationLayerInfo &info)
85 {
86 ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, recurrent_weights, bias, hidden_state, output);
87 ARM_COMPUTE_ERROR_THROW_ON(CLRNNLayer::validate(input->info(), weights->info(), recurrent_weights->info(), bias->info(), hidden_state->info(), output->info(), info));
88 ARM_COMPUTE_LOG_PARAMS(input, weights, recurrent_weights, bias, hidden_state, output, info);
89
90 const int idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
91 TensorShape shape = compute_rnn_shape(recurrent_weights->info(), hidden_state->info()->dimension(idx_height));
92
93 _is_prepared = false;
94
95 _fully_connected_out.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
96 _gemm_output.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
97
98 // Manage intermediate buffers and configure
99 _memory_group.manage(&_fully_connected_out);
100 _fully_connected_kernel.configure(compile_context, input, weights, bias, &_fully_connected_out);
101
102 _memory_group.manage(&_gemm_output);
103 _gemm_state_f.configure(compile_context, hidden_state, recurrent_weights, nullptr, &_gemm_output, 1.f, 0.f);
104
105 _add_output.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
106 _memory_group.manage(&_add_output);
107
108 _add_kernel.configure(compile_context, &_fully_connected_out, &_gemm_output, &_add_output, ConvertPolicy::SATURATE);
109
110 _fully_connected_out.allocator()->allocate();
111 _gemm_output.allocator()->allocate();
112
113 _activation.configure(compile_context, &_add_output, hidden_state, info);
114 _add_output.allocator()->allocate();
115
116 _copy.configure(compile_context, hidden_state, output);
117 }
118
run()119 void CLRNNLayer::run()
120 {
121 prepare();
122
123 MemoryGroupResourceScope scope_mg(_memory_group);
124
125 _fully_connected_kernel.run();
126 _gemm_state_f.run();
127 _add_kernel.run();
128 _activation.run();
129
130 // copy hidden out to output
131 _copy.run();
132 }
133
prepare()134 void CLRNNLayer::prepare()
135 {
136 if(!_is_prepared)
137 {
138 _fully_connected_kernel.prepare();
139 _gemm_state_f.prepare();
140
141 _is_prepared = true;
142 }
143 }
144 } // namespace arm_compute
145