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>

1 files changed, 16 insertions, 38 deletions

diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index fb90415e31..49549a0ad0 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -171,7 +171,6 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
     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 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
     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 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
                                                  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 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *
     // 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 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
     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 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
                                                  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 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI
 
     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)