COMPMID-1277 - Optimizing CLIm2ColKernel for NHWC.
This patch includes:
- Im2Col optimizations for NHWC using a new data layout
- Refactoring of CLIm2ColKernel adding validation method and auto-init
- Removed im2col_reduced from CLIm2ColKernel and created a new kernel CLFlattenLayerKernel
Change-Id: I1620640b6796baa268324b33ae92cdd8de53e27c
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/141241
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index fb90415..49549a0 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -171,7 +171,6 @@
const DataLayout data_layout = input->info()->data_layout();
const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
- const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES);
const unsigned int kernel_width = weights->info()->dimension(idx_width);
@@ -193,7 +192,6 @@
ICLTensor *gemm_output_to_use = output;
ICLTensor *gemm_output_staged_to_use = output;
- const unsigned bias_element = (_append_bias && !_skip_im2col) ? 1 : 0;
const ICLTensor *biases_to_use = (_append_bias && !_skip_im2col) ? biases : nullptr;
// Get parameters from conv_info
@@ -212,7 +210,6 @@
dilation);
unsigned int mat_weights_cols = weights->info()->dimension(idx_kernels);
- unsigned int mat_weights_rows = weights->info()->dimension(idx_width) * weights->info()->dimension(idx_height) * weights->info()->dimension(idx_channel) + bias_element;
// _weights_reshaped will be auto configured in the kernel.
// Just append biases and do not transpose 1xW as it will be reshaped in CLGEMM
@@ -223,25 +220,13 @@
// Create tensor to store im2col reshaped inputs
if(!_skip_im2col)
{
- // Calculate im2col shape
- // For OpenCL the batch size is on the third dimension
- // TODO (giaiod01): Use auto-init COMPMID-1277
- TensorShape shape_im2col = input->info()->tensor_shape();
- if(shape_im2col.num_dimensions() >= 3)
- {
- shape_im2col.remove_dimension(2);
- }
- shape_im2col.set(0, mat_weights_rows);
- shape_im2col.set(1, conv_w * conv_h);
-
- // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
- TensorInfo im2col_reshaped_info(shape_im2col, 1, data_type);
- im2col_reshaped_info.set_quantization_info(input->info()->quantization_info());
- _im2col_output.allocator()->init(im2col_reshaped_info);
_memory_group.manage(&_im2col_output);
- // Configure and tune im2col
+ // Configure and tune im2col. im2col output shape is auto-initialized
_im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, _append_bias, dilation);
+
+ // Set quantization info
+ _im2col_output.info()->set_quantization_info(input->info()->quantization_info());
CLScheduler::get().tune_kernel_static(_im2col_kernel);
// Update GEMM input
@@ -350,11 +335,10 @@
const ITensorInfo *gemm_output_staged_to_use = output;
const ITensorInfo *weights_to_use = weights;
- const bool is_nhwc = data_layout == DataLayout::NHWC;
- const bool is_quantized = is_data_type_quantized_asymmetric(data_type);
- const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1) && !is_quantized;
- const bool append_bias = (biases != nullptr) && (!is_quantized);
- const unsigned bias_element = (append_bias && !skip_im2col) ? 1 : 0;
+ const bool is_nhwc = data_layout == DataLayout::NHWC;
+ const bool is_quantized = is_data_type_quantized_asymmetric(data_type);
+ const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1) && !is_quantized;
+ const bool append_bias = (biases != nullptr) && (!is_quantized);
ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_channel) != input->dimension(idx_channel));
ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
@@ -391,7 +375,6 @@
dilation);
unsigned int mat_weights_cols = weights->dimension(idx_kernels);
- unsigned int mat_weights_rows = weights->dimension(idx_width) * weights->dimension(idx_height) * weights->dimension(idx_channel) + bias_element;
// Output tensor auto inizialitation if not yet initialized
ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayerReshapeWeights::validate(weights, is_quantized ? nullptr : biases, nullptr));
@@ -400,19 +383,14 @@
if(!skip_im2col)
{
- // Create tensor info for im2col reshaped inputs
- // For OpenCL the batch size is on the third dimension
- // TODO (giaiod01): Use auto-init COMPMID-1277
- TensorShape shape_im2col = input->tensor_shape();
- if(input->tensor_shape().num_dimensions() >= 3)
- {
- shape_im2col.remove_dimension(2);
- }
- shape_im2col.set(0, mat_weights_rows);
- shape_im2col.set(1, conv_w * conv_h);
- im2col_reshaped_info = TensorInfo(shape_im2col, 1, data_type);
- im2col_reshaped_info.set_quantization_info(input->quantization_info());
- ARM_COMPUTE_RETURN_ON_ERROR(CLIm2ColKernel::validate(input, &im2col_reshaped_info, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation));
+ const Size2D kernel_dims(kernel_width, kernel_height);
+
+ // Output tensor auto initialization if not yet initialized
+ TensorShape expected_output_shape = compute_im2col_conv_shape(input, kernel_dims, conv_info, append_bias, dilation, true);
+
+ auto_init_if_empty(im2col_reshaped_info, input->clone()->set_tensor_shape(expected_output_shape));
+
+ ARM_COMPUTE_RETURN_ON_ERROR(CLIm2ColKernel::validate(input, &im2col_reshaped_info, kernel_dims, conv_info, append_bias, dilation));
gemm_input_to_use = &im2col_reshaped_info;
}
else if(append_bias)