COMPMID-420, COMPMID-414 - Port CLConvolutionLayer and CLFullyConnectedLayer to use 8 bit fixed point
Change-Id: I1cb1b4d7711ad7b569ee691e13a5df1b3430292b
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/79565
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp
index b29bf8f..96d04dc 100644
--- a/src/runtime/CL/functions/CLConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp
@@ -41,7 +41,7 @@
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::F16, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
@@ -63,8 +63,9 @@
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);
+ 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, &_weights_reshaped);
@@ -95,23 +96,27 @@
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(input, 1, DataType::QS8, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
+ ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights, output);
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_MISMATCHING_FIXED_POINT(input, biases);
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);
}
+ const DataType dt = input->info()->data_type();
+ const int fixed_point_position = input->info()->fixed_point_position();
+
_has_bias = (biases != nullptr);
_are_weights_reshaped = weights_info.are_reshaped();
- // Get parameters for conv_info
+ // Get parameters from conv_info
unsigned int stride_x = 0;
unsigned int stride_y = 0;
unsigned int pad_x = 0;
@@ -123,8 +128,8 @@
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 = (_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);
std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height,
conv_info);
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");
@@ -132,9 +137,10 @@
// Check if its a "fully connected" convolution
_is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
- // Create tensor to store the reshaped weights
- 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);
+ 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) + (_has_bias ? 1 : 0);
+
+ // Reshape weights if needed
if(_are_weights_reshaped)
{
mat_weights_cols = output->info()->dimension(2);
@@ -147,49 +153,48 @@
{
// 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());
+ TensorInfo info_wr(shape_wr, 1, dt, fixed_point_position);
_weights_reshaped.allocator()->init(info_wr);
- _reshape_weights.configure(weights, biases, &_weights_reshaped, false);
- weights = &_weights_reshaped;
+ _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */);
}
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;
+ 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)));
+ TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position);
+ _weights_reshaped.allocator()->init(info_wt);
+ _reshape_weights.configure(weights, biases, &_weights_reshaped, true /* 1xW transpose */);
}
+ weights = &_weights_reshaped;
}
+
// Create tensor to store im2col reshaped inputs
- const size_t mat_input_cols = mat_weights_rows;
- const size_t mat_input_rows = conv_w * conv_h;
- TensorShape shape_im2col = input->info()->tensor_shape();
+ const unsigned int mat_input_cols = mat_weights_rows;
+ const unsigned int 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);
- _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, input->info()->data_type()));
+ _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position));
// 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()));
+ shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
+ _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position));
}
// Create GEMM output tensor
TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape();
shape_gemm.set(0, mat_weights_cols);
shape_gemm.set(1, mat_input_rows);
- _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, input->info()->data_type()));
+ _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, dt, fixed_point_position));
// Configure kernels
_input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias);
- _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h));
-
if(_is_fully_connected_convolution)
{
_mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f);
@@ -199,19 +204,13 @@
_input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
_mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f);
}
+ _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h));
+ // Allocate intermediate tensor
if(!_are_weights_reshaped)
{
- if(!_is_fully_connected_convolution)
- {
- _weights_transposed.allocator()->allocate();
- }
- else
- {
- _weights_reshaped.allocator()->allocate();
- }
+ _weights_reshaped.allocator()->allocate();
}
-
_input_im2col_reshaped.allocator()->allocate();
if(!_is_fully_connected_convolution)
{