COMPMID-1704: Collapse the 4th dimension in CLPoolingLayerKernel
Change-Id: I76e57af6608b55b6f59a5d06aecc30063ee4c3cc
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/155733
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Michele DiGiorgio <michele.digiorgio@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/core/CL/cl_kernels/pooling_layer.cl b/src/core/CL/cl_kernels/pooling_layer.cl
index 0808353..7d15d10 100644
--- a/src/core/CL/cl_kernels/pooling_layer.cl
+++ b/src/core/CL/cl_kernels/pooling_layer.cl
@@ -489,7 +489,11 @@
const int pad_x, const int pad_y, const int stride_x, const int stride_y)
{
int start_x = get_global_id(1) * stride_x - pad_x;
+#if defined(DST_DEPTH)
+ int start_y = (get_global_id(2) % DST_DEPTH) * stride_y - pad_y;
+#else /* defined(DST_DEPTH) */
int start_y = get_global_id(2) * stride_y - pad_y;
+#endif /* defined(DST_DEPTH) */
#if !defined(EXCLUDE_PADDING)
upper_bound_w += pad_x;
@@ -522,30 +526,43 @@
* @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes)
* @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] input_stride_w Stride of the source tensor in W dimension (in bytes)
+ * @param[in] input_step_w input_stride_w * number of elements along W processed per workitem(in bytes)
* @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image
* @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr
- * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes)
+ * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes)
* @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes)
+ * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes)
* @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes)
* @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] output_stride_w Stride of the destination tensor in W dimension (in bytes)
+ * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes)
* @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image
*/
__kernel void pooling_layer_MxN_nhwc(
- TENSOR3D_DECLARATION(input),
- TENSOR3D_DECLARATION(output))
+ TENSOR4D_DECLARATION(input),
+ TENSOR4D_DECLARATION(output))
{
// Get pixels pointer
- Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input);
- Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output);
+#if defined(DST_DEPTH)
+ Tensor4D input = CONVERT_TO_TENSOR4D_STRUCT(input, DST_DEPTH);
+ Tensor4D output = CONVERT_TO_TENSOR4D_STRUCT(output, DST_DEPTH);
+#else /* defined(DST_DEPTH) */
+ Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input);
+ Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output);
+#endif /* defined(DST_DEPTH) */
VEC_DATA_TYPE(DATA_TYPE, 8)
vdata = INITIAL_VALUE;
DATA_TYPE sdata = INITIAL_VALUE;
- const int idx_width = get_global_id(1) * STRIDE_X;
+ const int idx_width = get_global_id(1) * STRIDE_X;
+#if defined(DST_DEPTH)
+ const int idx_height = (get_global_id(2) % DST_DEPTH) * STRIDE_Y;
+#else /* defined(DST_DEPTH) */
const int idx_height = get_global_id(2) * STRIDE_Y;
+#endif /* defined(DST_DEPTH) */
for(int y = 0; y < POOL_SIZE_Y; ++y)
{
@@ -555,8 +572,14 @@
int x1 = select(x, PAD_X - idx_width - 1, x + idx_width - PAD_X < 0 || x + idx_width - PAD_X >= MAX_WIDTH);
x1 = select(x1, PAD_X - idx_width - 1, y != y1);
+#if defined(DST_DEPTH)
+ VEC_DATA_TYPE(DATA_TYPE, 8)
+ data0 = vload8(0, (__global DATA_TYPE *)tensor4D_offset(&input, 0, x1 - PAD_X, y1 - PAD_Y, 0));
+#else /* defined(DST_DEPTH) */
VEC_DATA_TYPE(DATA_TYPE, 8)
data0 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, x1 - PAD_X, y1 - PAD_Y));
+#endif /* defined(DST_DEPTH) */
+
#if defined(POOL_L2)
// Raise to power of 2 for L2 Pooling
data0 *= data0;
diff --git a/src/core/CL/cl_kernels/pooling_layer_quantized.cl b/src/core/CL/cl_kernels/pooling_layer_quantized.cl
index 17d893a..58d8987 100644
--- a/src/core/CL/cl_kernels/pooling_layer_quantized.cl
+++ b/src/core/CL/cl_kernels/pooling_layer_quantized.cl
@@ -126,7 +126,11 @@
const int pad_x, const int pad_y, const int stride_x, const int stride_y)
{
int start_x = get_global_id(1) * stride_x - pad_x;
- int start_y = get_global_id(2) * stride_y - pad_y;
+#if defined(DST_DEPTH)
+ int start_y = (get_global_id(2) % DST_DEPTH) * stride_y - pad_y;
+#else /* defined(DST_DEPTH) */
+ int start_y = get_global_id(2) * stride_y - pad_y;
+#endif /* defined(DST_DEPTH) */
const int end_x = min(start_x + pool_size_x, upper_bound_w);
const int end_y = min(start_y + pool_size_y, upper_bound_h);
@@ -153,39 +157,58 @@
* @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes)
* @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] input_stride_w Stride of the source tensor in W dimension (in bytes)
+ * @param[in] input_step_w input_stride_w * number of elements along W processed per workitem(in bytes)
* @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image
* @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr
- * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes)
+ * @param[in] output_stride_x Stride of the destination tensor in X dimension (in bytes)
* @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes)
+ * @param[in] output_stride_y Stride of the destination tensor in Y dimension (in bytes)
* @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] output_stride_z Stride of the destination tensor in Z dimension (in bytes)
* @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] output_stride_w Stride of the destination tensor in W dimension (in bytes)
+ * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes)
* @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image
*/
__kernel void pooling_layer_MxN_quantized_nhwc(
- TENSOR3D_DECLARATION(input),
- TENSOR3D_DECLARATION(output))
+ TENSOR4D_DECLARATION(input),
+ TENSOR4D_DECLARATION(output))
{
// Get pixels pointer
+#if defined(DST_DEPTH)
+ Tensor4D input = CONVERT_TO_TENSOR4D_STRUCT(input, DST_DEPTH);
+ Tensor4D output = CONVERT_TO_TENSOR4D_STRUCT(output, DST_DEPTH);
+#else /* defined(DST_DEPTH) */
Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input);
Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output);
+#endif /* defined(DST_DEPTH) */
int8 vdata = 0;
- const int idx_width = get_global_id(1) * STRIDE_X;
+ const int idx_width = get_global_id(1) * STRIDE_X;
+#if defined(DST_DEPTH)
+ const int idx_height = (get_global_id(2) % DST_DEPTH) * STRIDE_Y;
+#else /* defined(DST_DEPTH) */
const int idx_height = get_global_id(2) * STRIDE_Y;
+#endif /* defined(DST_DEPTH) */
for(int y = 0; y < POOL_SIZE_Y; ++y)
{
int y1 = select(y, PAD_Y - idx_height, y + idx_height < PAD_Y || y + idx_height > MAX_HEIGHT);
for(int x = 0; x < POOL_SIZE_X; ++x)
{
- int x1 = select(x, PAD_X - idx_width - 1, x + idx_width < PAD_X || x + idx_width > MAX_WIDTH);
- x1 = select(x1, PAD_X - idx_width - 1, y != y1);
+ int x1 = select(x, PAD_X - idx_width - 1, x + idx_width < PAD_X || x + idx_width > MAX_WIDTH);
+ x1 = select(x1, PAD_X - idx_width - 1, y != y1);
+
+#if defined(DST_DEPTH)
+ uchar8 data = vload8(0, (__global uchar *)tensor4D_offset(&input, 0, x1 - PAD_X, y1 - PAD_Y, 0));
+#else /* defined(DST_DEPTH) */
uchar8 data = vload8(0, (__global uchar *)tensor3D_offset(&input, 0, x1 - PAD_X, y1 - PAD_Y));
- int8 data0 = convert_int8(data);
- vdata = POOL_OP(vdata, data0);
+#endif /* defined(DST_DEPTH) */
+
+ int8 data0 = convert_int8(data);
+ vdata = POOL_OP(vdata, data0);
}
}
diff --git a/src/core/CL/kernels/CLPoolingLayerKernel.cpp b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
index df13068..bd21ea0 100644
--- a/src/core/CL/kernels/CLPoolingLayerKernel.cpp
+++ b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
@@ -257,6 +257,8 @@
build_opts.add_option_if(exclude_padding, "-DEXCLUDE_PADDING");
build_opts.add_option("-DMAX_WIDTH=" + support::cpp11::to_string(input->info()->dimension(idx_width)));
build_opts.add_option("-DMAX_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(idx_height)));
+ build_opts.add_option_if(output->info()->tensor_shape().total_size_upper(3) > 1,
+ "-DDST_DEPTH=" + support::cpp11::to_string(output->info()->dimension(idx_height)));
std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "pooling_layer_MxN_quantized_nhwc" : "pooling_layer_MxN_nhwc";
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
break;
@@ -315,12 +317,14 @@
unsigned int pool_stride_y = 0;
std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
+ // Collapse window
+ Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
+
switch(_input->info()->data_layout())
{
case DataLayout::NCHW:
{
- Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ);
- Window slice = window_collapsed.first_slice_window_3D();
+ Window slice = window_collapsed.first_slice_window_3D();
do
{
// Upsample input by pool size
@@ -343,21 +347,23 @@
}
case DataLayout::NHWC:
{
- Window slice = window.first_slice_window_3D();
+ const size_t total_batches = _output->info()->tensor_shape().total_size_upper(3);
- Window in_slice = window.first_slice_window_3D();
+ Window slice = window_collapsed.first_slice_window_4D();
+ Window in_slice = window_collapsed.first_slice_window_4D();
in_slice.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _num_elems_processed_per_iteration));
in_slice.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), pool_stride_x));
in_slice.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), pool_stride_y));
+ in_slice.set(3, Window::Dimension(0, total_batches, 1));
do
{
// Set inputs
unsigned int idx = 0;
- add_3D_tensor_argument(idx, _input, in_slice);
- add_3D_tensor_argument(idx, _output, slice);
+ add_4D_tensor_argument(idx, _input, in_slice);
+ add_4D_tensor_argument(idx, _output, slice);
enqueue(queue, *this, slice, lws_hint());
}
- while(window.slide_window_slice_3D(slice) && window.slide_window_slice_3D(in_slice));
+ while(window.slide_window_slice_4D(slice) && window.slide_window_slice_4D(in_slice));
break;
}
default: