Compute Library
 20.08
GCFullyConnectedLayer.cpp
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2  * Copyright (c) 2017-2020 Arm Limited.
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25 
28 #include "support/MemorySupport.h"
29 
30 #include <algorithm>
31 
32 using namespace arm_compute;
33 
35 {
36  auto k = arm_compute::support::cpp14::make_unique<GCTransposeKernel>();
37  k->configure(input, output);
38  _kernel = std::move(k);
39 }
40 
41 GCFullyConnectedLayer::GCFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager)
42  : _memory_group(std::move(memory_manager)), _weights_manager(std::move(weights_manager)), _im2col_kernel(), _reshape_weights_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(),
43  _reshape_weights_output(), _original_weights(nullptr), _are_weights_reshaped(true), _is_fc_after_conv(true), _accumulate_biases(false)
44 {
45 }
46 
47 void GCFullyConnectedLayer::configure_conv_fc(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output)
48 {
49  ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))));
50 
51  const DataType dt = input->info()->data_type();
52 
53  // If the fully connected layer is called after a convolution layer, the input tensor must be linearized
54 
55  // Initialize output tensor for im2col
56  TensorShape shape_im2col;
57  shape_im2col.set(0, input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2));
58  shape_im2col.set(1, input->info()->dimension(3));
59  shape_im2col.set(2, input->info()->dimension(4));
60  shape_im2col.set(3, input->info()->dimension(5));
61  _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, dt));
62 
63  // Configure im2col kernel
64  _memory_group.manage(&_im2col_output);
65  _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
66 
67  // Configure matrix multiply kernel
68  _mm_kernel.configure(&_im2col_output, weights, output, 1.0f, false);
69 
70  // Allocate the output tensor for im2col once all the configure methods have been called
71  _im2col_output.allocator()->allocate();
72 }
73 
74 void GCFullyConnectedLayer::configure_fc_fc(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output)
75 {
76  ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));
77 
78  // Configure matrix multiply kernel
79  _mm_kernel.configure(input, weights, output, 1.0f, false);
80 }
81 
84 {
88 
89  _original_weights = weights;
90  _are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
91  _is_fc_after_conv = true;
92  _accumulate_biases = false;
93 
94  if(biases != nullptr)
95  {
97 
98  _accumulate_biases = true;
99 
100  // Configure accumulate biases kernel
101  _accumulate_biases_kernel.configure(output, biases);
102  }
103 
104  // With the Fully Connected layer we can have 4 different cases:
105  // 1) Convolution layer -> Fully Connected layer without batches
106  // 2) Fully Connected layer -> Fully Connected layer without batches
107  // 3) Convolution layer -> Fully Connected layer with batches
108  // 4) Fully Connected layer -> Fully Connected layer with batches
109 
110  const IGCTensor *weights_to_use = weights;
111 
112  if(!_are_weights_reshaped)
113  {
114  weights_to_use = &_reshape_weights_output;
115 
116  // Reshape the weights
117  _reshape_weights_kernel.configure(weights, &_reshape_weights_output);
118  }
119 
120  // Check if we have a fully connected layer with batches
121  const bool is_batched_fc_layer = output->info()->dimension(1) > 1;
122 
123  if(is_batched_fc_layer)
124  {
125  _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->info()->tensor_shape().cbegin() + 3,
126  input->info()->tensor_shape().cend(),
127  output->info()->tensor_shape().cbegin() + 1));
128  }
129  else
130  {
131  _is_fc_after_conv = input->info()->num_dimensions() > 1;
132  }
133 
134  if(_is_fc_after_conv)
135  {
136  // Fully Connected layer after a Convolution Layer without batches
137  configure_conv_fc(input, weights_to_use, output);
138  }
139  else
140  {
141  // Fully Connected layer after a Fully Connected Layer without batches
142  configure_fc_fc(input, weights_to_use, output);
143  }
144 
145  ARM_COMPUTE_ERROR_ON(fc_info.retain_internal_weights && _reshape_weights_output.gc_buffer() == 0);
146  _are_weights_reshaped = _are_weights_reshaped || fc_info.retain_internal_weights;
147 }
148 
150 {
151  prepare();
152 
153  MemoryGroupResourceScope scope_mg(_memory_group);
154 
155  // Linearize input if it comes from a convolutional layer
156  if(_is_fc_after_conv)
157  {
158  GCScheduler::get().dispatch(_im2col_kernel, false);
159  }
160 
161  if(!_are_weights_reshaped || _is_fc_after_conv)
162  {
164  }
165 
166  // Run matrix multiply
167  GCScheduler::get().dispatch(_mm_kernel, !_accumulate_biases);
168 
169  // Accumulate biases if provided
170  if(_accumulate_biases)
171  {
173 
174  GCScheduler::get().dispatch(_accumulate_biases_kernel);
175  }
176 }
177 
179 {
180  // Reshape of the weights (happens only once)
181  if(!_are_weights_reshaped)
182  {
183  ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
184 
185  // Run reshape weights kernel and mark weights as unused
186  _reshape_weights_output.allocator()->allocate();
187  _reshape_weights_kernel.run();
188 
189  // Mark original weights tensor as unused
190  _original_weights->mark_as_unused();
191 
192  _are_weights_reshaped = true;
193  }
194 }
void configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, FullyConnectedLayerInfo fc_info=FullyConnectedLayerInfo())
Set the input and output tensors.
void configure(const IGCTensor *input, IGCTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, const Size2D &dilation=Size2D(1U, 1U))
Set the input and output of the kernel.
virtual size_t num_dimensions() const =0
The number of dimensions of the tensor (rank)
Shape of a tensor.
Definition: TensorShape.h:39
virtual size_t dimension(size_t index) const =0
Return the size of the requested dimension.
#define ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(...)
Definition: Validate.h:543
void dispatch(IGCKernel &kernel, bool flush=true)
Schedule the execution of the passed kernel if possible.
Definition: GCScheduler.cpp:77
bool retain_internal_weights
Retain internal reshaped weights.
Definition: Types.h:1585
void run() override final
Run the kernels contained in the function.
bool is_used() const
Flags if the tensor is used or not.
Definition: ITensor.cpp:163
1 channel, 1 F32 per channel
void memory_barrier()
Defines a barrier ordering memory transactions.
Definition: GCScheduler.cpp:86
#define ARM_COMPUTE_ERROR_ON(cond)
If the condition is true then an error message is printed and an exception thrown.
Definition: Error.h:466
Fully connected layer info.
Definition: Types.h:1580
Interface for GLES Compute tensor.
Definition: IGCTensor.h:35
void init(const TensorInfo &input, size_t alignment=0)
Initialize a tensor based on the passed TensorInfo.
Copyright (c) 2017-2020 Arm Limited.
1 channel, 1 F16 per channel
ITensorInfo * info() const override
Interface to be implemented by the child class to return the tensor's metadata.
Definition: Tensor.cpp:33
void mark_as_unused() const
Marks a tensor as unused.
Definition: ITensor.cpp:168
void manage(IMemoryManageable *obj) override
Sets a object to be managed by the given memory group.
Definition: MemoryGroup.h:79
static GCScheduler & get()
Access the scheduler singleton.
Definition: GCScheduler.cpp:70
GLuint gc_buffer() const override
Interface to be implemented by the child class to return the tensor's gles compute buffer id.
Definition: GCTensor.cpp:54
virtual const TensorShape & tensor_shape() const =0
Size for each dimension of the tensor.
void prepare() override
Prepare the function for executing.
bool are_weights_reshaped
Reshape the weights tensor if false.
Definition: Types.h:1584
void configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed=true, const GEMMReshapeInfo &reshape_info=GEMMReshapeInfo())
Initialise the kernel's input, output and alpha.
virtual ITensorInfo * info() const =0
Interface to be implemented by the child class to return the tensor's metadata.
void run() override
Run the kernels contained in the function.
Padding and stride information class.
Definition: Types.h:689
Weights manager interface to handle weights transformations.
virtual void allocate()=0
Interface to be implemented by the child class to allocate the tensor.
#define ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(t, c,...)
Definition: Validate.h:790
void configure(const IGCTensor *input, IGCTensor *output)
Set the input and output tensors.
std::array< T, num_max_dimensions >::const_iterator cbegin() const
Returns a read-only (constant) iterator that points to the first element in the dimension array.
Definition: Dimensions.h:210
Memory group resources scope handling class.
Definition: IMemoryGroup.h:82
Class for specifying the size of an image or rectangle.
Definition: Size2D.h:34
bool transpose_weights
Transpose weights if true.
Definition: Types.h:1583
TensorShape & set(size_t dimension, size_t value, bool apply_dim_correction=true)
Accessor to set the value of one of the dimensions.
Definition: TensorShape.h:78
void configure(IGCTensor *accum, const IGCTensor *biases)
Set the accumulate buffer and the biases of the kernel.
Store the tensor's metadata.
Definition: TensorInfo.h:45
static constexpr size_t num_max_dimensions
Number of dimensions the tensor has.
Definition: Dimensions.h:45
DataType
Available data types.
Definition: Types.h:77
ITensorAllocator * allocator()
Return a pointer to the tensor's allocator.
Definition: GCTensor.cpp:34
GCFullyConnectedLayer(std::shared_ptr< IMemoryManager > memory_manager=nullptr, IWeightsManager *weights_manager=nullptr)
Constructor.