using namespace arm_compute;
GCConvolutionLayerReshapeWeights::GCConvolutionLayerReshapeWeights()
- : _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
+ : _weights_reshape_kernel(), _weights_reshaped()
{
}
-void GCConvolutionLayerReshapeWeights::configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, bool transpose1xW)
+void GCConvolutionLayerReshapeWeights::configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output)
{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
if(biases != nullptr)
}
const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type());
- const unsigned bias_element = (append_biases) ? 1 : 0;
const IGCTensor *biases_to_use = (append_biases) ? biases : nullptr;
- _transpose1xW = transpose1xW;
-
- if(transpose1xW)
- {
- // Create tensor to store the reshaped weights
- const unsigned int mat_weights_cols = weights->info()->dimension(3);
- const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;
- TensorShape shape_wr(mat_weights_cols, mat_weights_rows);
- const DataType dt = weights->info()->data_type();
- const int fixed_point_position = weights->info()->fixed_point_position();
- TensorInfo info_wr(shape_wr, 1, dt, fixed_point_position);
-
- _weights_reshaped.allocator()->init(info_wr);
- _weights_reshape_kernel.configure(weights, biases_to_use, &_weights_reshaped);
- _weights_transposed_kernel.configure(&_weights_reshaped, output);
- _weights_reshaped.allocator()->allocate();
- }
- else
- {
- _weights_reshape_kernel.configure(weights, biases_to_use, output);
- }
+ _weights_reshape_kernel.configure(weights, biases_to_use, output);
}
void GCConvolutionLayerReshapeWeights::run()
{
GCScheduler::get().dispatch(_weights_reshape_kernel);
- if(_transpose1xW)
- {
- GCScheduler::get().dispatch(_weights_transposed_kernel);
- }
}
-GCConvolutionLayer::GCConvolutionLayer()
- : _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _fill_border(), _input_im2col_reshaped(), _input_interleaved_reshaped(),
- _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
+GCConvolutionLayer::GCConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
+ : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _mm_gemm(), _output_col2im_kernel(), _fill_border(), _activationlayer_function(), _original_weights(nullptr),
+ _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _is_first_run(true), _is_activationlayer_enabled(false)
{
}
-void GCConvolutionLayer::configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output, bool is_interleaved_transposed)
+void GCConvolutionLayer::configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info()));
+
+ _mm_gemm.configure(input, weights, nullptr, output, 1.f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */));
+}
+
+Status GCConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output)
{
- _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed);
+ // Perform validation step on Matrix multiply function
+ GCGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */));
+ return Status{};
}
-void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
+void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
+ const Size2D &dilation, const ActivationLayerInfo &act_info)
{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
- ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2));
+ ARM_COMPUTE_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!");
+ ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2));
ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
+ _is_first_run = true;
+ _original_weights = weights;
+
if(biases != nullptr)
{
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
- ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3));
+ ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
}
const DataType dt = input->info()->data_type();
- _append_bias = (biases != nullptr);
- _are_weights_reshaped = weights_info.are_reshaped();
+ // Set the GPU target for im2col and col2im
+ _input_im2col_kernel.set_target(GCScheduler::get().get_target());
+ _output_col2im_kernel.set_target(GCScheduler::get().get_target());
- const unsigned bias_element = (_append_bias) ? 1 : 0;
- const IGCTensor *biases_to_use = (_append_bias) ? biases : nullptr;
+ const bool append_bias = (biases != nullptr);
+ const unsigned bias_element = (append_bias) ? 1 : 0;
+ const IGCTensor *biases_to_use = (append_bias) ? biases : nullptr;
// Get parameters from conv_info
unsigned int stride_x = 0;
unsigned int conv_w = 0;
unsigned int conv_h = 0;
- const unsigned int kernel_width = (_are_weights_reshaped) ? weights_info.kernel_size().first : weights->info()->dimension(0);
- const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : weights->info()->dimension(1);
+ const unsigned int kernel_width = weights->info()->dimension(0);
+ const unsigned int kernel_height = weights->info()->dimension(1);
std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
- conv_info);
-
- // Check if its a "fully connected" convolution
- _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
- const bool run_interleaved = (!_is_fully_connected_convolution);
+ conv_info, dilation);
unsigned int mat_weights_cols = weights->info()->dimension(3);
unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;
- // Reshape weights if needed
- if(_are_weights_reshaped)
- {
- if(_is_fully_connected_convolution)
- {
- mat_weights_cols = weights->info()->dimension(0);
- mat_weights_rows = weights->info()->dimension(1);
- }
- else
- {
- mat_weights_cols = weights_info.num_kernels();
- const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4;
- mat_weights_rows = quarter_reshaped_cols + bias_element;
- }
- }
- else
- {
- if(_is_fully_connected_convolution)
- {
- // Create tensor to store the reshaped weights
- int num_elems_read_per_iteration_x = 1;
- if(dt == DataType::F16)
- {
- num_elems_read_per_iteration_x = 2;
- }
- TensorShape shape_wr((ceil_to_multiple(mat_weights_cols, num_elems_read_per_iteration_x)), mat_weights_rows);
- _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wr));
- _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false /* 1xW transpose */);
- }
- else
- {
- // Create tensor to store transposed weights
- const float transpose_width = 16.0f / input->info()->element_size();
- TensorShape shape_wt(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
- _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wt));
- _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, true /* 1xW transpose */);
- }
- weights = &_weights_reshaped;
- }
+ // _weights_reshaped will be auto configured in the kernel.
+ // Just append biases and do not transpose 1xW as it will be reshaped in GCGEMM
+ _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped);
+
+ weights = &_weights_reshaped;
// Create tensor to store im2col reshaped inputs
const unsigned int mat_input_cols = mat_weights_rows;
TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->info()->fixed_point_position());
_input_im2col_reshaped.allocator()->init(im2col_reshaped_info);
-
- // Create tensor (interleave) to prepare input tensor for GEMM
- if(run_interleaved)
- {
- TensorShape shape_interleaved = shape_im2col;
- shape_interleaved.set(0, shape_interleaved.x() * 4);
- shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
-
- TensorInfo interleaved_info(shape_interleaved, 1, dt, input->info()->fixed_point_position());
- _input_interleaved_reshaped.allocator()->init(interleaved_info);
- }
+ _memory_group.manage(&_input_im2col_reshaped);
// Create GEMM output tensor
TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape();
TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
_gemm_output.allocator()->init(info_gemm);
+ _memory_group.manage(&_gemm_output);
- // Configure kernels
if(dt == DataType::F16)
{
BorderSize border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left());
input->info()->extend_padding(border_size);
_fill_border.configure(input, border_size, BorderMode::CONSTANT, PixelValue(0)); // for PAD of im2col fp16: consider it as border
}
- _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias);
+ // Configure im2col
+ _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation);
+
+ // Configure GEMM
+ configure_mm(&_input_im2col_reshaped, weights, &_gemm_output);
- // Configure matrix multiply
- if(run_interleaved)
- {
- _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
- configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output);
- _input_interleaved_reshaped.allocator()->allocate();
- }
- else
- {
- configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, false);
- }
_input_im2col_reshaped.allocator()->allocate();
// Configure Col2Im
ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
// Allocate intermediate tensor
- if(!_are_weights_reshaped)
+ _weights_reshaped.allocator()->allocate();
+
+ //Configure Activation Layer
+ _is_activationlayer_enabled = act_info.enabled();
+
+ if(_is_activationlayer_enabled)
{
- _weights_reshaped.allocator()->allocate();
+ _activationlayer_function.configure(output, nullptr, act_info);
}
+
+ ARM_COMPUTE_UNUSED(weights_info);
}
void GCConvolutionLayer::run()
{
// Run weights reshaping (Runs once for every configure)
- if(!_are_weights_reshaped)
+ if(_is_first_run)
{
- _are_weights_reshaped = true;
+ ARM_COMPUTE_ERROR_ON(!_original_weights->is_used());
+
_reshape_weights.run();
+ _is_first_run = false;
+
+ // Mark original weights tensor as unused
+ _original_weights->mark_as_unused();
}
+ _memory_group.acquire();
+
// Run im2col
GCScheduler::get().dispatch(_fill_border);
GCScheduler::get().memory_barrier();
GCScheduler::get().dispatch(_input_im2col_kernel);
- if(!_is_fully_connected_convolution)
- {
- GCScheduler::get().memory_barrier();
- // Run interleave4x4
- GCScheduler::get().dispatch(_input_interleave_kernel);
- }
-
- GCScheduler::get().memory_barrier();
- // Runs matrix multiply on reshaped matrices
- GCScheduler::get().dispatch(_mm_kernel);
+ // Run gemm on reshaped matrices
+ _mm_gemm.run();
GCScheduler::get().memory_barrier();
// Reshape output matrix
GCScheduler::get().dispatch(_output_col2im_kernel, false);
+
+ _memory_group.release();
+
+ GCScheduler::get().memory_barrier();
+ // Run Activation Layer
+ if(_is_activationlayer_enabled)
+ {
+ _activationlayer_function.run();
+ }
}
projects / platform / upstream / armcl.git / blobdiff