arm_compute v17.06
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp
index bb47bf9..f0bbc35 100644
--- a/src/runtime/CL/functions/CLConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp
@@ -33,83 +33,155 @@
using namespace arm_compute;
-CLConvolutionLayer::CLConvolutionLayer()
- : _input_im2col_kernel(), _weights_reshape_kernel(), _input_interleave_kernel(), _weights_transposed_kernel(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(),
- _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _is_first_run(false), _has_bias(false), _is_fc(false)
+CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights()
+ : _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false)
{
}
-void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info)
+void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose1xW)
+{
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases, output);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases, output);
+ ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
+
+ if(biases != nullptr)
+ {
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+ ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
+ ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
+ }
+
+ const bool _has_bias = (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) + (_has_bias ? 1 : 0);
+ TensorShape shape_wr(mat_weights_cols, mat_weights_rows);
+ const DataType dt = weights->info()->data_type();
+ TensorInfo info_wr(shape_wr, 1, dt);
+
+ _weights_reshaped.allocator()->init(info_wr);
+ _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped);
+ _weights_transposed_kernel.configure(&_weights_reshaped, output);
+ _weights_reshaped.allocator()->allocate();
+ }
+ else
+ {
+ _weights_reshape_kernel.configure(weights, biases, output);
+ }
+}
+
+void CLConvolutionLayerReshapeWeights::run()
+{
+ cl::CommandQueue q = CLScheduler::get().queue();
+ CLScheduler::get().enqueue(_weights_reshape_kernel);
+ if(_transpose1xW)
+ {
+ CLScheduler::get().enqueue(_weights_transposed_kernel);
+ }
+}
+
+CLConvolutionLayer::CLConvolutionLayer()
+ : _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(),
+ _weights_transposed(), _gemm_output(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false)
+{
+}
+
+void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
- ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2));
+ ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2));
ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
if(biases != nullptr)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
- ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3));
+ ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3));
ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
}
- _has_bias = (biases != nullptr);
- _is_first_run = true;
+ _has_bias = (biases != nullptr);
+ _are_weights_reshaped = weights_info.are_reshaped();
// Get parameters for conv_info
- unsigned int stride_x, stride_y, pad_x, pad_y = 0;
+ unsigned int stride_x = 0;
+ unsigned int stride_y = 0;
+ unsigned int pad_x = 0;
+ unsigned int pad_y = 0;
std::tie(stride_x, stride_y) = conv_info.stride();
std::tie(pad_x, pad_y) = conv_info.pad();
- bool is_same_dimension = true;
- // Make sure the input and weights have same low three dimensions
- for(int i = 0; i < 3; i++)
- {
- is_same_dimension = (is_same_dimension) && (input->info()->dimension(i) == weights->info()->dimension(i));
- }
-
- // Run the fully connected path if is_same_dimension is true and conv_stride_x/conv_stride_y are 1, and conv_pad_x/conv_pad_y are 0 and skip col2im
- _is_fc = (is_same_dimension) && ((stride_x & stride_y) == 1) && ((pad_x | pad_y) == 0);
-
// Get convolved dimensions
unsigned int conv_w = 0;
unsigned int conv_h = 0;
- std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights->info()->dimension(0),
+
+ const unsigned int kernel_width = _are_weights_reshaped ? weights_info.kernel_size() : weights->info()->dimension(0);
+ std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width,
stride_x, stride_y, pad_x, pad_y, conv_info.round());
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");
+ // Check if its a "fully connected" convolution
+ _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
+
// Create tensor to store the reshaped weights
- const size_t mat_weights_cols = weights->info()->dimension(3);
- const size_t mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + ((_has_bias) ? 1 : 0);
- const TensorShape shape_wr(mat_weights_cols, mat_weights_rows);
- _weights_reshaped.allocator()->init(TensorInfo(shape_wr, 1, weights->info()->data_type()));
-
- // Create tensor to store transposed weights
- TensorShape shape_wt(mat_weights_rows * 4, static_cast<size_t>(std::ceil(mat_weights_cols / 4.f)));
- TensorInfo info_wt(shape_wt, 1, weights->info()->data_type());
- _weights_transposed.allocator()->init(info_wt);
-
+ size_t mat_weights_cols = weights->info()->dimension(3);
+ size_t mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + ((_has_bias) ? 1 : 0);
+ if(_are_weights_reshaped)
+ {
+ mat_weights_cols = output->info()->dimension(2);
+ const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4;
+ mat_weights_rows = (_has_bias ? 1 + quarter_reshaped_cols : quarter_reshaped_cols);
+ }
+ else
+ {
+ if(_is_fully_connected_convolution)
+ {
+ // Create tensor to store the reshaped weights
+ TensorShape shape_wr(mat_weights_cols, mat_weights_rows);
+ TensorInfo info_wr(shape_wr, 1, weights->info()->data_type());
+ _weights_reshaped.allocator()->init(info_wr);
+ _reshape_weights.configure(weights, biases, &_weights_reshaped, false);
+ weights = &_weights_reshaped;
+ }
+ else
+ {
+ // Create tensor to store transposed weights
+ TensorShape shape_wt(mat_weights_rows * 4, static_cast<size_t>(std::ceil(mat_weights_cols / 4.f)));
+ TensorInfo info_wt(shape_wt, 1, weights->info()->data_type());
+ _weights_transposed.allocator()->init(info_wt);
+ _reshape_weights.configure(weights, biases, &_weights_transposed, true);
+ weights = &_weights_transposed;
+ }
+ }
// Create tensor to store im2col reshaped inputs
const size_t mat_input_cols = mat_weights_rows;
- const size_t mat_input_rows = _is_fc ? (input->info()->dimension(3)) : (conv_w * conv_h);
+ const size_t mat_input_rows = conv_w * conv_h;
TensorShape shape_im2col = input->info()->tensor_shape();
shape_im2col.set(0, mat_input_cols);
shape_im2col.set(1, mat_input_rows);
shape_im2col.set(2, 1);
- if(_is_fc)
- {
- shape_im2col.set(3, 1);
- }
_input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, input->info()->data_type()));
- // Create tensor to prepare input tensor for GEMM
- TensorShape shape_interleaved = shape_im2col;
- shape_interleaved.set(0, shape_interleaved.x() * 4);
- shape_interleaved.set(1, std::ceil(static_cast<float>(shape_interleaved.y()) / 4));
- _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, input->info()->data_type()));
+ // Create tensor (interleave) to prepare input tensor for GEMM
+ if(!_is_fully_connected_convolution)
+ {
+ TensorShape shape_interleaved = shape_im2col;
+ shape_interleaved.set(0, shape_interleaved.x() * 4);
+ shape_interleaved.set(1, std::ceil(static_cast<float>(shape_interleaved.y()) / 4.f));
+ _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, input->info()->data_type()));
+ }
// Create GEMM output tensor
TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape();
@@ -119,48 +191,57 @@
// Configure kernels
_input_im2col_kernel.configure(input, &_input_im2col_reshaped, std::make_pair(conv_w, conv_h), conv_info, _has_bias);
- _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
- _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped);
- _weights_transposed_kernel.configure(&_weights_reshaped, &_weights_transposed);
- if(_is_fc)
+ _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h));
+
+ if(_is_fully_connected_convolution)
{
- _mm_kernel.configure(&_input_interleaved_reshaped, &_weights_transposed, output, 1.0f);
+ _mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f);
}
else
{
- _mm_kernel.configure(&_input_interleaved_reshaped, &_weights_transposed, &_gemm_output, 1.0f);
- _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h));
+ _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
+ _mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f);
}
- // Allocate intermediate tensors
- _weights_reshaped.allocator()->allocate();
- _weights_transposed.allocator()->allocate();
+ if(!_are_weights_reshaped)
+ {
+ if(!_is_fully_connected_convolution)
+ {
+ _weights_transposed.allocator()->allocate();
+ }
+ else
+ {
+ _weights_reshaped.allocator()->allocate();
+ }
+ }
+
_input_im2col_reshaped.allocator()->allocate();
- _input_interleaved_reshaped.allocator()->allocate();
+ if(!_is_fully_connected_convolution)
+ {
+ _input_interleaved_reshaped.allocator()->allocate();
+ }
_gemm_output.allocator()->allocate();
}
void CLConvolutionLayer::run()
{
// Run weights reshaping (Runs once for every configure)
- if(_is_first_run)
+ if(!_are_weights_reshaped)
{
- _is_first_run = false;
- CLScheduler::get().enqueue(_weights_reshape_kernel);
- CLScheduler::get().enqueue(_weights_transposed_kernel);
+ _are_weights_reshaped = true;
+ _reshape_weights.run();
}
// Run input reshaping
CLScheduler::get().enqueue(_input_im2col_kernel);
- CLScheduler::get().enqueue(_input_interleave_kernel);
+ if(!_is_fully_connected_convolution)
+ {
+ CLScheduler::get().enqueue(_input_interleave_kernel);
+ }
// Runs matrix multiply on reshaped matrices
CLScheduler::get().enqueue(_mm_kernel);
// Reshape output matrix
-
- if(!_is_fc)
- {
- CLScheduler::get().enqueue(_output_col2im_kernel, false);
- }
+ CLScheduler::get().enqueue(_output_col2im_kernel, false);
}