COMPMID-935 - Implementing Convolution with Winograd on OpenCL (Part 1)

This patch enables GEMM to execute multiple batches in parallel

https://confluence.arm.com/display/MLENG/Winograd%3A+batched+GEMM

Change-Id: I66222db041dd35e82af11fbb262fd1ebd3ca4b2f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/120866
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 14 insertions, 7 deletions

diff --git a/src/core/CL/kernels/CLGEMMInterleave4x4Kernel.cpp b/src/core/CL/kernels/CLGEMMInterleave4x4Kernel.cpp
index 241dd8549d..d12255ff24 100644
--- a/src/core/CL/kernels/CLGEMMInterleave4x4Kernel.cpp
+++ b/src/core/CL/kernels/CLGEMMInterleave4x4Kernel.cpp
@@ -80,8 +80,12 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen
         output_access.set_valid_region(win, input->valid_region());
     }
 
+    // Collapse along the Z direction
+    // This collapse needs to be here in order to tune the Z dimension of LWS
+    Window collapsed = win.collapse(win, Window::DimZ);
+
     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
-    return std::make_pair(err, win);
+    return std::make_pair(err, collapsed);
 }
 } // namespace
 
@@ -136,6 +140,10 @@ void CLGEMMInterleave4x4Kernel::configure(const ICLTensor *input, ICLTensor *out
     _config_id += support::cpp11::to_string(output->info()->dimension(0));
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(1));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(output->info()->dimension(2));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(output->info()->dimension(3));
 }
 
 Status CLGEMMInterleave4x4Kernel::validate(const ITensorInfo *input, const ITensorInfo *output, int mult_interleave4x4_height)
@@ -160,15 +168,14 @@ void CLGEMMInterleave4x4Kernel::run(const Window &window, cl::CommandQueue &queu
      *
      * After this operation, the output matrix will have the following shape: [ height * 4, width / 4 ]
      */
-    Window in_slice  = window.first_slice_window_2D();
-    Window out_slice = window.first_slice_window_2D();
+    Window slice = window.first_slice_window_3D();
 
     do
     {
         unsigned int idx = 0;
-        add_2D_tensor_argument(idx, _input, in_slice);
-        add_2D_tensor_argument(idx, _output, out_slice);
-        enqueue(queue, *this, in_slice, _lws_hint);
+        add_3D_tensor_argument(idx, _input, slice);
+        add_3D_tensor_argument(idx, _output, slice);
+        enqueue(queue, *this, slice, _lws_hint);
     }
-    while(window.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice));
+    while(window.slide_window_slice_3D(slice));
 }