#include "arm_compute/core/Validate.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
+#include <algorithm>
+#include <cmath>
+
using namespace arm_compute;
CLFullyConnectedLayer::CLFullyConnectedLayer()
- : _conv_function(), _gemm_function(), _transpose_kernel(), _acc_biases_kernel(), _run_func(), _weights_transpose(), _is_first_run(true), _run_acc_biases(false)
+ : _im2col_kernel(), _transpose_kernel(), _transpose1xW_kernel(), _interleave4x4_kernel(), _mm_kernel(), _accumulate_biases_kernel(), _im2col_output(), _interleave4x4_output(), _transpose_output(),
+ _transpose1xW_output(), _is_first_run(true), _transpose_weights(true), _fc_after_conv(true), _batched_fc_layer(false), _accumulate_biases(false)
{
}
-void CLFullyConnectedLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *biases, ICLTensor *output)
+void CLFullyConnectedLayer::configure_conv_fc_wb(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
{
- ARM_COMPUTE_ERROR_ON((weights->info()->num_dimensions() != 2) && (weights->info()->num_dimensions() != 4));
+ ARM_COMPUTE_ERROR_ON(weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2)));
- // Make sure that in the fully connected layer connected to fully connected layer case, the first dimension of the weights and input are same.
- ARM_COMPUTE_ERROR_ON((weights->info()->num_dimensions() == 2) && (input->info()->dimension(0) != weights->info()->dimension(0)));
+ // If the fully connected layer is called after a convolution layer, the input tensor must be linearized
- if(weights->info()->num_dimensions() != 2)
- {
- _conv_function.configure(input, weights, biases, output, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::FLOOR));
- _run_func = &CLFullyConnectedLayer::run_conv;
- return;
- }
+ // Initialize output tensor for im2col
+ TensorShape shape_im2col;
+ shape_im2col.set(0, weights->info()->dimension(1));
+ shape_im2col.set(1, input->info()->dimension(3));
+ shape_im2col.set(2, input->info()->dimension(4));
+ shape_im2col.set(3, input->info()->dimension(5));
+ _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, input->info()->data_type()));
- TensorShape shape_trans(weights->info()->dimension(1), weights->info()->dimension(0));
- _weights_transpose.allocator()->init(TensorInfo(shape_trans, 1, weights->info()->data_type()));
+ // Initialize output tensor for interleave 4x4
+ TensorShape shape_interleaved = _im2col_output.info()->tensor_shape();
+ shape_interleaved.set(0, shape_interleaved.x() * 4);
+ shape_interleaved.set(1, std::ceil(static_cast<float>(shape_interleaved.y()) / 4));
+ _interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, input->info()->data_type()));
- // Configure kernels
- _transpose_kernel.configure(weights, &_weights_transpose);
- _gemm_function.configure(input, &_weights_transpose, nullptr, output, 1.0f, 0.0f);
- if(biases != nullptr)
- {
- _acc_biases_kernel.configure(output, biases);
- _run_acc_biases = true;
- }
+ // Initialize output tensor for transpose 1xW
+ TensorShape shape_transposed1xW(weights->info()->dimension(1) * 4, static_cast<size_t>(std::ceil(weights->info()->dimension(0) / 4.f)));
+ _transpose1xW_output.allocator()->init(TensorInfo(shape_transposed1xW, 1, weights->info()->data_type()));
+
+ // Configure im2col kernel
+ _im2col_kernel.configure(input, &_im2col_output, std::make_pair(1, 1), PadStrideInfo(1, 1, 0, 0), false);
+
+ // Configure interleave4x4 kernel
+ _interleave4x4_kernel.configure(&_im2col_output, &_interleave4x4_output);
- _run_func = &CLFullyConnectedLayer::run_fc;
+ // Configure transpose 1xW kernel
+ _transpose1xW_kernel.configure(weights, &_transpose1xW_output);
- // Allocate intermediate buffers
- _weights_transpose.allocator()->allocate();
+ // Configure matrix multiply kernel
+ _mm_kernel.configure(&_interleave4x4_output, &_transpose1xW_output, output, 1.0f);
+
+ // Allocate the tensors once all the configure methods have been called
+ _im2col_output.allocator()->allocate();
+ _interleave4x4_output.allocator()->allocate();
+ _transpose1xW_output.allocator()->allocate();
}
-void CLFullyConnectedLayer::run_conv()
+void CLFullyConnectedLayer::configure_fc_fc_wb(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
{
- _conv_function.run();
+ // Initialize output tensor for interleave 4x4
+ TensorShape shape_interleaved = input->info()->tensor_shape();
+ shape_interleaved.set(0, shape_interleaved.x() * 4);
+ shape_interleaved.set(1, std::ceil(static_cast<float>(shape_interleaved.y()) / 4));
+ _interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, input->info()->data_type()));
+
+ // Initialize output tensor for transpose 1xW
+ TensorShape shape_transposed1xW(weights->info()->dimension(1) * 4, static_cast<size_t>(std::ceil(weights->info()->dimension(0) / 4.f)));
+ _transpose1xW_output.allocator()->init(TensorInfo(shape_transposed1xW, 1, weights->info()->data_type()));
+
+ // Configure interleave4x4 kernel
+ _interleave4x4_kernel.configure(input, &_interleave4x4_output);
+
+ // Configure transpose 1xW kernel
+ _transpose1xW_kernel.configure(weights, &_transpose1xW_output);
+
+ // Configure matrix multiply kernel
+ _mm_kernel.configure(&_interleave4x4_output, &_transpose1xW_output, output, 1.0f);
+
+ // Allocate the tensors once all the configure methods have been called
+ _interleave4x4_output.allocator()->allocate();
+ _transpose1xW_output.allocator()->allocate();
}
-void CLFullyConnectedLayer::run_fc()
+void CLFullyConnectedLayer::configure_conv_fc_nb(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
{
- if(_is_first_run)
+ ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))));
+
+ // If the fully connected layer is called after a convolution layer, the input tensor must be linearized
+
+ // Initialize output tensor for im2col
+ TensorShape shape_im2col;
+ shape_im2col.set(0, weights->info()->dimension(1));
+ shape_im2col.set(1, 1);
+ _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, input->info()->data_type()));
+
+ // Configure im2col kernel
+ _im2col_kernel.configure(input, &_im2col_output, std::make_pair(1, 1), PadStrideInfo(1, 1, 0, 0), false);
+
+ // Configure matrix multiply kernel
+ _mm_kernel.configure(&_im2col_output, weights, output, 1.0f);
+
+ // Allocate the output tensor for im2col once all the configure methods have been called
+ _im2col_output.allocator()->allocate();
+}
+
+void CLFullyConnectedLayer::configure_fc_fc_nb(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output)
+{
+ ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1));
+
+ // Configure matrix multiply kernel
+ _mm_kernel.configure(input, weights, output, 1.0f);
+}
+
+void CLFullyConnectedLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose_weights)
+{
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
+ ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() != 2);
+
+ const ICLTensor *weights_to_use = weights;
+
+ _is_first_run = true;
+ _transpose_weights = transpose_weights;
+ _fc_after_conv = true;
+ _batched_fc_layer = false;
+ _accumulate_biases = false;
+
+ if(biases != nullptr)
{
- _is_first_run = false;
- CLScheduler::get().enqueue(_transpose_kernel);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+
+ _accumulate_biases = true;
+
+ // Configure accumulate biases kernel
+ _accumulate_biases_kernel.configure(output, biases);
+ }
+
+ // Check if we need to transpose the weights
+ if(_transpose_weights)
+ {
+ // Initialize the output tensor for transpose
+ TensorShape shape_transposed(weights->info()->dimension(1), weights->info()->dimension(0));
+ _transpose_output.allocator()->init(TensorInfo(shape_transposed, 1, weights->info()->data_type()));
+ _transpose_kernel.configure(weights, &_transpose_output);
+
+ weights_to_use = &_transpose_output;
+ }
+
+ // With the Fully Connected layer we can have 4 different cases:
+ // 1) Convolution layer -> Fully Connected layer without batches
+ // 2) Fully Connected layer -> Fully Connected layer without batches
+ // 3) Convolution layer -> Fully Connected layer with batches
+ // 4) Fully Connected layer -> Fully Connected layer with batches
+
+ // Check if we have a fully connected layer with batches
+ _batched_fc_layer = (output->info()->dimension(1) > 1);
+
+ if(_batched_fc_layer)
+ {
+ _fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(input->info()->tensor_shape().cbegin() + 3,
+ input->info()->tensor_shape().cend(),
+ output->info()->tensor_shape().cbegin() + 1));
+
+ if(_fc_after_conv)
+ {
+ // Fully Connected layer after a Convolution Layer with batches
+ configure_conv_fc_wb(input, weights_to_use, output);
+ }
+ else
+ {
+ // Fully Connected layer after a Fully Connected Layer with batches
+ configure_fc_fc_wb(input, weights_to_use, output);
+ }
}
+ else
+ {
+ _fc_after_conv = (weights_to_use->info()->dimension(1) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2)));
- _gemm_function.run();
+ if(_fc_after_conv)
+ {
+ // Fully Connected layer after a Convolution Layer without batches
+ configure_conv_fc_nb(input, weights_to_use, output);
+ }
+ else
+ {
+ // Fully Connected layer after a Fully Connected Layer without batches
+ configure_fc_fc_nb(input, weights_to_use, output);
+ }
+ }
- if(_run_acc_biases)
+ // Allocate the transpose tensor if the transpose_weights flag is true and once all the configure methods have been called
+ if(_transpose_weights)
{
- CLScheduler::get().enqueue(_acc_biases_kernel);
+ _transpose_output.allocator()->allocate();
}
}
void CLFullyConnectedLayer::run()
{
- ARM_COMPUTE_ERROR_ON(_run_func == nullptr);
- (this->*_run_func)();
+ // The reshape of the weights happens only once
+ if(_is_first_run)
+ {
+ _is_first_run = false;
+
+ if(_transpose_weights)
+ {
+ CLScheduler::get().enqueue(_transpose_kernel);
+ }
+
+ if(_batched_fc_layer)
+ {
+ CLScheduler::get().enqueue(_transpose1xW_kernel);
+ }
+ }
+
+ // Linearize input if it comes from a convolutional layer
+ if(_fc_after_conv)
+ {
+ CLScheduler::get().enqueue(_im2col_kernel, false);
+ }
+
+ // Interleave input
+ if(_batched_fc_layer)
+ {
+ CLScheduler::get().enqueue(_interleave4x4_kernel, false);
+ }
+
+ // Run matrix multiply
+ CLScheduler::get().enqueue(_mm_kernel, !_accumulate_biases);
+
+ // Accumulate biases if provided
+ if(_accumulate_biases)
+ {
+ CLScheduler::get().enqueue(_accumulate_biases_kernel);
+ }
}
projects / platform / upstream / armcl.git / blobdiff