COMPMID-807: NHWC support in CLDirectConvolution.
Change-Id: I8738aca2cc0104e4c4d7c9605762ab59fce10a33
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/137333
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index ba6a629..4753524 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -213,10 +213,13 @@
{ "derivative", "derivative.cl" },
{ "dilate", "dilate.cl" },
{ "direct_convolution1x1", "direct_convolution1x1.cl" },
+ { "direct_convolution1x1_nhwc", "direct_convolution1x1.cl" },
{ "direct_convolution1x1_f32_bifrost", "direct_convolution1x1.cl" },
{ "direct_convolution3x3", "direct_convolution3x3.cl" },
+ { "direct_convolution3x3_nhwc", "direct_convolution3x3.cl" },
{ "direct_convolution3x3_f32_bifrost", "direct_convolution3x3.cl" },
{ "direct_convolution5x5", "direct_convolution5x5.cl" },
+ { "direct_convolution5x5_nhwc", "direct_convolution5x5.cl" },
{ "direct_convolution5x5_f32_bifrost", "direct_convolution5x5.cl" },
{ "direct_convolution_1x1_3x3_5x5_quantized", "direct_convolution_1x1_3x3_5x5_quantized.cl" },
{ "erode", "erode.cl" },
diff --git a/src/core/CL/cl_kernels/direct_convolution1x1.cl b/src/core/CL/cl_kernels/direct_convolution1x1.cl
index 7a308c9..cceeb0f 100644
--- a/src/core/CL/cl_kernels/direct_convolution1x1.cl
+++ b/src/core/CL/cl_kernels/direct_convolution1x1.cl
@@ -31,6 +31,122 @@
#if defined(DATA_TYPE) && defined(DATA_SIZE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
+#if defined(DATA_LAYOUT_NHWC)
+
+#define PTR_TO_VALUE(PTR, DATA_TYPE) *((__global DATA_TYPE *)(PTR))
+
+/** This kernel performs a direct convolution to convolve the low three dimensions of a tensor with data layout NHWC
+ *
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The data size must be passed at compile time using -DDATA_SIZE e.g. -DDATA_SIZE=32
+ * @note The convolution stride x must be passed at compile time using -DSTRIDE_X e.g. -DSTRIDE_X=1
+ * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
+ * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
+ *
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32
+ * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr
+ * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)
+ * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)
+ * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)
+ * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)
+ * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
+ * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr
+ * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)
+ * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor
+ * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension
+ */
+__kernel void direct_convolution1x1_nhwc(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst),
+ TENSOR3D_DECLARATION(weights),
+#ifdef HAS_BIAS
+ VECTOR_DECLARATION(biases),
+#endif /* defined(HAS_BIAS) */
+ unsigned int weights_stride_w)
+{
+ Image src = CONVERT_TO_IMAGE_STRUCT(src);
+ Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);
+ Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
+
+#ifdef HAS_BIAS
+ Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
+#endif /* defined(HAS_BIAS) */
+
+ VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)
+ values = 0;
+ const int id0 = get_global_id(0);
+ const int id1 = get_global_id(1);
+ const int id2 = get_global_id(2);
+ weights.ptr += id0 * weights_stride_w;
+ __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0) - src_stride_x * id0 + id2 * STRIDE_Y * (int)src_stride_z;
+
+ for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
+ {
+ DATA_TYPE weight = *(__global DATA_TYPE *)weights.ptr;
+#if STRIDE_X == 1
+ VEC_DATA_TYPE(DATA_TYPE, 8)
+ col0 = (VEC_DATA_TYPE(DATA_TYPE, 8))(
+ PTR_TO_VALUE(src_addr + 0 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(src_addr + 1 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(src_addr + 2 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(src_addr + 3 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(src_addr + 4 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(src_addr + 5 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(src_addr + 6 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(src_addr + 7 * src_stride_y, DATA_TYPE));
+#elif STRIDE_X == 2 /* STRIDE_X == 1 */
+ VEC_DATA_TYPE(DATA_TYPE, 8)
+ col0 = (VEC_DATA_TYPE(DATA_TYPE, 8))(
+ PTR_TO_VALUE(src_addr + 0 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(src_addr + 2 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(src_addr + 4 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(src_addr + 6 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(src_addr + 8 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(src_addr + 10 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(src_addr + 12 * src_stride_y, DATA_TYPE),
+ PTR_TO_VALUE(src_addr + 14 * src_stride_y, DATA_TYPE));
+#else /* STRIDE_X not equals 1 or 2 */
+#error "STRIDE_X larger than 2 is not supported"
+#endif /* STRIDE_X == 2 */
+ values = ADD_OP(values, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))weight, col0));
+
+ src_addr += src_stride_x;
+ weights.ptr += weights_stride_x;
+ }
+
+#ifdef HAS_BIAS
+ values = ADD_OP(values, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, id0))));
+#endif /* defined(HAS_BIAS) */
+
+ *((__global DATA_TYPE *)dst.ptr) = values.s0;
+ *((__global DATA_TYPE *)(dst.ptr + 1 * dst_stride_y)) = values.s1;
+ *((__global DATA_TYPE *)(dst.ptr + 2 * dst_stride_y)) = values.s2;
+ *((__global DATA_TYPE *)(dst.ptr + 3 * dst_stride_y)) = values.s3;
+ *((__global DATA_TYPE *)(dst.ptr + 4 * dst_stride_y)) = values.s4;
+ *((__global DATA_TYPE *)(dst.ptr + 5 * dst_stride_y)) = values.s5;
+ *((__global DATA_TYPE *)(dst.ptr + 6 * dst_stride_y)) = values.s6;
+ *((__global DATA_TYPE *)(dst.ptr + 7 * dst_stride_y)) = values.s7;
+}
+#endif // defined(DATA_LAYOUT_NHWC)
+
#if STRIDE_X == 3
#define INPUT_PIXEL_STR(data_size) extract_input_stride3_##data_size
#define INPUT_PIXEL(data_size) INPUT_PIXEL_STR(data_size)
@@ -46,7 +162,7 @@
*
* @param[in] input_pixel Pointer to the first pixel.
*
- * @return extracted input pixels.
+ * @return extracted input values.
*/
inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride1(__global const DATA_TYPE *input_pixel)
{
@@ -57,7 +173,7 @@
*
* @param[in] input_pixel Pointer to the first pixel.
*
- * @return extracted input pixels.
+ * @return extracted input values.
*/
inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride2(__global const DATA_TYPE *input_pixel)
{
@@ -70,7 +186,7 @@
*
* @param[in] input_pixel Pointer to the first pixel.
*
- * @return extracted input pixels.
+ * @return extracted input values.
*/
inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3_32(__global const DATA_TYPE *input_pixel)
{
@@ -89,7 +205,7 @@
*
* @param[in] input_pixel Pointer to the first pixel.
*
- * @return extracted input pixels.
+ * @return extracted input values.
*/
inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3_16(__global const DATA_TYPE *input_pixel)
{
@@ -106,7 +222,7 @@
*
* @param[in] input_pixel Pointer to the first pixel.
*
- * @return extracted input pixels.
+ * @return extracted input values.
*/
inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3_8(__global const DATA_TYPE *input_pixel)
{
@@ -173,27 +289,26 @@
#endif /* defined(HAS_BIAS) */
VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)
- pixels = 0;
+ values = 0;
const uint z_index = get_global_id(2);
weights.ptr += z_index * weights_stride_w;
-
for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
{
DATA_TYPE weight = *(__global DATA_TYPE *)weights.ptr;
VEC_DATA_TYPE(DATA_TYPE, 8)
input_pixel = INPUT_PIXEL(DATA_SIZE)((__global DATA_TYPE *)src.ptr);
- pixels = ADD_OP(pixels, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))weight, input_pixel));
+ values = ADD_OP(values, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))weight, input_pixel));
src.ptr += src_stride_z;
weights.ptr += weights_stride_z;
}
#ifdef HAS_BIAS
- pixels = ADD_OP(pixels, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, z_index))));
+ values = ADD_OP(values, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, z_index))));
#endif /* defined(HAS_BIAS) */
- vstore8(CONVERT_SAT(pixels, VEC_DATA_TYPE(DATA_TYPE, 8)), 0, (__global DATA_TYPE *)dst.ptr);
+ vstore8(CONVERT_SAT(values, VEC_DATA_TYPE(DATA_TYPE, 8)), 0, (__global DATA_TYPE *)dst.ptr);
}
#endif // defined(DATA_TYPE) && defined(DATA_SIZE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
@@ -314,4 +429,4 @@
vstore4(acc2, 0, (__global float *)(dst.ptr + 2 * dst_stride_y));
vstore4(acc3, 0, (__global float *)(dst.ptr + 3 * dst_stride_y));
}
-#endif // defined(WEIGHTS_DEPTH)
\ No newline at end of file
+#endif // defined(WEIGHTS_DEPTH)
diff --git a/src/core/CL/cl_kernels/direct_convolution3x3.cl b/src/core/CL/cl_kernels/direct_convolution3x3.cl
index 824306f..08d25f6 100644
--- a/src/core/CL/cl_kernels/direct_convolution3x3.cl
+++ b/src/core/CL/cl_kernels/direct_convolution3x3.cl
@@ -66,6 +66,185 @@
acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1), (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2)); \
})
+#if defined(DATA_LAYOUT_NHWC)
+
+#define PTR_TO_VALUE(PTR, DATA_TYPE) *((__global DATA_TYPE *)(PTR))
+
+#if STRIDE_X == 1
+#define CONVOLUTION1x3_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x3_STRIDE_NHWC_STRIDE1(acc, row_ptr, weights_ptr)
+#elif STRIDE_X == 2 /* STRIDE_X == 1 */
+#define CONVOLUTION1x3_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x3_STRIDE_NHWC_STRIDE2(acc, row_ptr, weights_ptr)
+#else /* STRIDE_X not equals 1 or 2 */
+#error "STRIDE_X larger than 2 is not supported"
+#endif /* STRIDE_X == 2 */
+
+#define CONVOLUTION1x3_STRIDE_NHWC_STRIDE1(acc, row_ptr, weights_ptr) \
+ { \
+ VEC_DATA_TYPE(DATA_TYPE, 8) \
+ src0 = (VEC_DATA_TYPE(DATA_TYPE, 8))( \
+ PTR_TO_VALUE(row_ptr + 0 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 1 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 2 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 3 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 4 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 5 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 6 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 7 * src_stride_y, DATA_TYPE)); \
+ VEC_DATA_TYPE(DATA_TYPE, 2) \
+ src1 = (VEC_DATA_TYPE(DATA_TYPE, 2))( \
+ PTR_TO_VALUE(row_ptr + 8 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 9 * src_stride_y, DATA_TYPE)); \
+ VEC_DATA_TYPE(DATA_TYPE, 3) \
+ weights = (VEC_DATA_TYPE(DATA_TYPE, 3))( \
+ PTR_TO_VALUE((weights_ptr) + 0 * weights_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE((weights_ptr) + 1 * weights_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE((weights_ptr) + 2 * weights_stride_y, DATA_TYPE)); \
+ acc = ADD_OP(acc, MUL_OP(src0, (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s0)); \
+ acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0), (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s1)); \
+ acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01), (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s2)); \
+ }
+
+#define CONVOLUTION1x3_STRIDE_NHWC_STRIDE2(acc, row_ptr, weights_ptr) \
+ { \
+ VEC_DATA_TYPE(DATA_TYPE, 16) \
+ src0 = (VEC_DATA_TYPE(DATA_TYPE, 16))( \
+ PTR_TO_VALUE(row_ptr + 0 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 1 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 2 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 3 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 4 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 5 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 6 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 7 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 8 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 9 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 10 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 11 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 12 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 13 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 14 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 15 * src_stride_y, DATA_TYPE)); \
+ DATA_TYPE src1 = PTR_TO_VALUE(row_ptr + 16 * src_stride_y, DATA_TYPE); \
+ VEC_DATA_TYPE(DATA_TYPE, 3) \
+ weights = (VEC_DATA_TYPE(DATA_TYPE, 3))( \
+ PTR_TO_VALUE((weights_ptr) + 0 * weights_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE((weights_ptr) + 1 * weights_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE((weights_ptr) + 2 * weights_stride_y, DATA_TYPE)); \
+ \
+ acc = ADD_OP(acc, MUL_OP(src0.s02468ACE, (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s0)); \
+ acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF), (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s1)); \
+ acc = ADD_OP(acc, MUL_OP((VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1), (VEC_DATA_TYPE(DATA_TYPE, 8))weights.s2)); \
+ }
+
+/** This kernel performs a direct convolution to convolve the low three dimensions.
+ *
+ * @note This OpenCL kernel works with stride_x = 1 and 2
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
+ * @note If biases are used then -DHAS_BIAS has to be passed at compile time
+ *
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32
+ * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr
+ * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)
+ * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)
+ * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)
+ * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)
+ * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
+ * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr
+ * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)
+ * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor
+ * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension
+ */
+__kernel void direct_convolution3x3_nhwc(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst),
+ TENSOR3D_DECLARATION(weights),
+#ifdef HAS_BIAS
+ VECTOR_DECLARATION(biases),
+#endif /* defined(HAS_BIAS) */
+ unsigned int weights_stride_w)
+{
+ Image src = CONVERT_TO_IMAGE_STRUCT(src);
+ Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);
+ Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
+
+ VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)
+ values0 = 0;
+ const int id0 = get_global_id(0);
+ const int id1 = get_global_id(1);
+ const int id2 = get_global_id(2);
+
+ __global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0);
+ __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0) - src_stride_x * id0 + ((id2 * STRIDE_Y) - PAD_TOP) * (int)src_stride_z;
+
+ weights_addr += id0 * weights_stride_w;
+
+ const int coordy = ((id2 * STRIDE_Y) - PAD_TOP);
+ for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
+ {
+#if PAD_TOP > 0
+ if(coordy < 0) // special case Z = -1 doesn't exists
+ {
+ //skip first row and load the two next ones
+ CONVOLUTION1x3_NHWC(values0, src_addr + 1 * (int)src_stride_z, (weights_addr + 1 * (int)weights_stride_z));
+ CONVOLUTION1x3_NHWC(values0, src_addr + 2 * (int)src_stride_z, (weights_addr + 2 * (int)weights_stride_z));
+ }
+ else if(coordy == (SRC_HEIGHT - PAD_TOP - 1))
+ {
+ // special case when computing the last row of the output we must read the last three rows from the input buffer (including padding) but the
+ // Z axis has no padding at all.
+ CONVOLUTION1x3_NHWC(values0, src_addr, (weights_addr + 0 * (int)weights_stride_z));
+ CONVOLUTION1x3_NHWC(values0, src_addr + 1 * (int)src_stride_z, (weights_addr + 1 * (int)weights_stride_z));
+ }
+ else
+ {
+ CONVOLUTION1x3_NHWC(values0, src_addr, (weights_addr + 0 * (int)weights_stride_z));
+ CONVOLUTION1x3_NHWC(values0, src_addr + 1 * (int)src_stride_z, (weights_addr + 1 * (int)weights_stride_z));
+ CONVOLUTION1x3_NHWC(values0, src_addr + 2 * (int)src_stride_z, (weights_addr + 2 * (int)weights_stride_z));
+ }
+#else // PAD_TOP > 0
+ CONVOLUTION1x3_NHWC(values0, src_addr, (weights_addr + 0 * (int)weights_stride_z));
+ CONVOLUTION1x3_NHWC(values0, src_addr + 1 * (int)src_stride_z, (weights_addr + 1 * (int)weights_stride_z));
+ CONVOLUTION1x3_NHWC(values0, src_addr + 2 * (int)src_stride_z, (weights_addr + 2 * (int)weights_stride_z));
+#endif // PAD_TOP > 0
+ src_addr += src_stride_x;
+ weights_addr += weights_stride_x;
+ }
+
+#ifdef HAS_BIAS
+ Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
+ values0 = ADD_OP(values0, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, id0))));
+#endif /* defined(HAS_BIAS) */
+
+ *((__global DATA_TYPE *)(dst.ptr + 0 * dst_stride_y)) = values0.s0;
+ *((__global DATA_TYPE *)(dst.ptr + 1 * dst_stride_y)) = values0.s1;
+ *((__global DATA_TYPE *)(dst.ptr + 2 * dst_stride_y)) = values0.s2;
+ *((__global DATA_TYPE *)(dst.ptr + 3 * dst_stride_y)) = values0.s3;
+ *((__global DATA_TYPE *)(dst.ptr + 4 * dst_stride_y)) = values0.s4;
+ *((__global DATA_TYPE *)(dst.ptr + 5 * dst_stride_y)) = values0.s5;
+ *((__global DATA_TYPE *)(dst.ptr + 6 * dst_stride_y)) = values0.s6;
+ *((__global DATA_TYPE *)(dst.ptr + 7 * dst_stride_y)) = values0.s7;
+}
+#endif // defined(DATA_LAYOUT_NHWC)
+
/** This kernel performs a direct convolution to convolve the low three dimensions.
*
* @note This OpenCL kernel works with stride_x = 1 and 2
@@ -117,7 +296,7 @@
Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)
- pixels0 = 0;
+ values0 = 0;
__global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0);
__global uchar *src_addr = (__global uchar *)offset(&src, 0, 0);
@@ -127,9 +306,9 @@
for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
{
- CONVOLUTION1x3(pixels0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y));
- CONVOLUTION1x3(pixels0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
- CONVOLUTION1x3(pixels0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
+ CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y));
+ CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
+ CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
src_addr += src_stride_z;
weights_addr += weights_stride_z;
@@ -138,10 +317,10 @@
#ifdef HAS_BIAS
Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
- pixels0 = ADD_OP(pixels0, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index))));
+ values0 = ADD_OP(values0, (VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index))));
#endif /* defined(HAS_BIAS) */
- vstore8(CONVERT_SAT(pixels0, VEC_DATA_TYPE(DATA_TYPE, 8)), 0, (__global DATA_TYPE *)dst.ptr);
+ vstore8(CONVERT_SAT(values0, VEC_DATA_TYPE(DATA_TYPE, 8)), 0, (__global DATA_TYPE *)dst.ptr);
}
#endif //defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
@@ -214,9 +393,9 @@
Image src = CONVERT_TO_IMAGE_STRUCT(src);
Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
- float4 pixels0 = 0;
- float4 pixels1 = 0;
- float4 pixels2 = 0;
+ float4 values0 = 0;
+ float4 values1 = 0;
+ float4 values2 = 0;
__global uchar *weights_addr = (__global uchar *)(weights_ptr + weights_offset_first_element_in_bytes + kernel_index * weights_stride_w);
__global uchar *src_addr = (__global uchar *)offset(&src, 0, 0);
@@ -236,39 +415,39 @@
src0 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
src1 = vload2(0, (__global float *)(src_addr + 0 * src_stride_y) + 4);
- CONVOLUTION1x3_BIFROST(pixels0, src0, src1, weights_row0);
+ CONVOLUTION1x3_BIFROST(values0, src0, src1, weights_row0);
// Load values from row1 of input tensor
src0 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y));
src1 = vload2(0, (__global float *)(src_addr + 1 * src_stride_y) + 4);
// Accumulate
- CONVOLUTION1x3_BIFROST(pixels0, src0, src1, weights_row1);
- CONVOLUTION1x3_BIFROST(pixels1, src0, src1, weights_row0);
+ CONVOLUTION1x3_BIFROST(values0, src0, src1, weights_row1);
+ CONVOLUTION1x3_BIFROST(values1, src0, src1, weights_row0);
// Load values from row2 of input tensor
src0 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y));
src1 = vload2(0, (__global float *)(src_addr + 2 * src_stride_y) + 4);
// Accumulate
- CONVOLUTION1x3_BIFROST(pixels0, src0, src1, weights_row2);
- CONVOLUTION1x3_BIFROST(pixels1, src0, src1, weights_row1);
- CONVOLUTION1x3_BIFROST(pixels2, src0, src1, weights_row0);
+ CONVOLUTION1x3_BIFROST(values0, src0, src1, weights_row2);
+ CONVOLUTION1x3_BIFROST(values1, src0, src1, weights_row1);
+ CONVOLUTION1x3_BIFROST(values2, src0, src1, weights_row0);
// Load values from row3 of input tensor
src0 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y));
src1 = vload2(0, (__global float *)(src_addr + 3 * src_stride_y) + 4);
// Accumulate
- CONVOLUTION1x3_BIFROST(pixels1, src0, src1, weights_row2);
- CONVOLUTION1x3_BIFROST(pixels2, src0, src1, weights_row1);
+ CONVOLUTION1x3_BIFROST(values1, src0, src1, weights_row2);
+ CONVOLUTION1x3_BIFROST(values2, src0, src1, weights_row1);
// Row4
src0 = vload4(0, (__global float *)(src_addr + 4 * src_stride_y));
src1 = vload2(0, (__global float *)(src_addr + 4 * src_stride_y) + 4);
// Accumulate
- CONVOLUTION1x3_BIFROST(pixels2, src0, src1, weights_row2);
+ CONVOLUTION1x3_BIFROST(values2, src0, src1, weights_row2);
src_addr += src_stride_z;
weights_addr += weights_stride_z;
@@ -279,13 +458,13 @@
float bias = (float) * ((__global float *)(vector_offset(&biases, kernel_index)));
- pixels0 += (float4)bias;
- pixels1 += (float4)bias;
- pixels2 += (float4)bias;
+ values0 += (float4)bias;
+ values1 += (float4)bias;
+ values2 += (float4)bias;
#endif /* defined(HAS_BIAS) */
- vstore4(pixels0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y));
- vstore4(pixels1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y));
- vstore4(pixels2, 0, (__global float *)(dst.ptr + 2 * dst_stride_y));
+ vstore4(values0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y));
+ vstore4(values1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y));
+ vstore4(values2, 0, (__global float *)(dst.ptr + 2 * dst_stride_y));
}
#endif // defined(WEIGHTS_DEPTH)
diff --git a/src/core/CL/cl_kernels/direct_convolution5x5.cl b/src/core/CL/cl_kernels/direct_convolution5x5.cl
index e678f6f..70be058 100644
--- a/src/core/CL/cl_kernels/direct_convolution5x5.cl
+++ b/src/core/CL/cl_kernels/direct_convolution5x5.cl
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -69,6 +69,190 @@
acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s468a, src0.sCE, src1.s02) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1; \
})
+#if defined(DATA_LAYOUT_NHWC)
+
+#define PTR_TO_VALUE(PTR, DATA_TYPE) *((__global DATA_TYPE *)(PTR))
+
+#if STRIDE_X == 1
+#define CONVOLUTION1x5_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x5_STRIDE1_NHWC(acc, row_ptr, weights_ptr)
+#elif STRIDE_X == 2 /* STRIDE_X == 1 */
+#define CONVOLUTION1x5_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x5_STRIDE2_NHWC(acc, row_ptr, weights_ptr)
+#else /* STRIDE_X not equals 1 or 2 */
+#error "STRIDE_X larger than 2 is not supported"
+#endif /* STRIDE_X == 2 */
+
+#define CONVOLUTION1x5_STRIDE1_NHWC(acc, row_ptr, weights_ptr) \
+ ({ \
+ VEC_DATA_TYPE(DATA_TYPE, 8) \
+ src0 = (VEC_DATA_TYPE(DATA_TYPE, 8))( \
+ PTR_TO_VALUE(row_ptr + 0 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 1 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 2 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 3 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 4 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 5 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 6 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 7 * src_stride_y, DATA_TYPE)); \
+ VEC_DATA_TYPE(DATA_TYPE, 4) \
+ src1 = (VEC_DATA_TYPE(DATA_TYPE, 4))( \
+ PTR_TO_VALUE(row_ptr + 8 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 9 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 10 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 11 * src_stride_y, DATA_TYPE)); \
+ VEC_DATA_TYPE(DATA_TYPE, 4) \
+ weights_values0 = (VEC_DATA_TYPE(DATA_TYPE, 4))( \
+ PTR_TO_VALUE(weights_ptr + 0 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 1 * weights_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(weights_ptr + 2 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 3 * weights_stride_y, DATA_TYPE)); \
+ DATA_TYPE weights_value1 = PTR_TO_VALUE(weights_ptr + 4 * weights_stride_y, DATA_TYPE); \
+ acc += src0 * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0; \
+ acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1; \
+ acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \
+ acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s345, src0.s67, src1.s012) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3; \
+ acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s45, src0.s67, src1.s0123) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1; \
+ })
+
+#define CONVOLUTION1x5_STRIDE2_NHWC(acc, row_ptr, weights_ptr) \
+ ({ \
+ VEC_DATA_TYPE(DATA_TYPE, 16) \
+ src0 = (VEC_DATA_TYPE(DATA_TYPE, 16))( \
+ PTR_TO_VALUE(row_ptr + 0 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 1 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 2 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 3 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 4 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 5 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 6 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 7 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 8 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 9 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 10 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 11 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 12 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 13 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 14 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 15 * src_stride_y, DATA_TYPE)); \
+ VEC_DATA_TYPE(DATA_TYPE, 4) \
+ src1 = (VEC_DATA_TYPE(DATA_TYPE, 4))( \
+ PTR_TO_VALUE(row_ptr + 16 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 17 * src_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(row_ptr + 18 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 19 * src_stride_y, DATA_TYPE)); \
+ VEC_DATA_TYPE(DATA_TYPE, 4) \
+ weights_values0 = (VEC_DATA_TYPE(DATA_TYPE, 4))( \
+ PTR_TO_VALUE(weights_ptr + 0 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 1 * weights_stride_y, DATA_TYPE), \
+ PTR_TO_VALUE(weights_ptr + 2 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 3 * weights_stride_y, DATA_TYPE)); \
+ DATA_TYPE weights_value1 = PTR_TO_VALUE(weights_ptr + 4 * weights_stride_y, DATA_TYPE); \
+ acc += src0.s02468ACE * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0; \
+ acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1; \
+ acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2; \
+ \
+ acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s3579, src0.sBDF, src1.s1) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3; \
+ acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s468a, src0.sCE, src1.s02) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1; \
+ })
+
+/** This kernel performs a direct convolution to convolve the low three dimensions in a tensor with the NHWC data layout
+ *
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
+ * @note If biases are used then -DHAS_BIAS has to be passed at compile time
+ *
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32
+ * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
+ * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr
+ * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)
+ * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)
+ * @param[in] weights_step_y weights_stride_y * number of elements along y processed per workitem(in bytes)
+ * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)
+ * @param[in] weights_step_z weights_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
+ * @param[in] biases_ptr Pointer to the biases tensor. Same as @p src_ptr
+ * @param[in] biases_stride_x Stride of the biases tensor in X dimension (in bytes)
+ * @param[in] biases_step_x biases_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] biases_offset_first_element_in_bytes The offset of the first element in the biases tensor
+ * @param[in] weights_stride_w Stride of the weights tensor in the 4th dimension
+ */
+__kernel void direct_convolution5x5_nhwc(
+ TENSOR3D_DECLARATION(src),
+ TENSOR3D_DECLARATION(dst),
+ TENSOR3D_DECLARATION(weights),
+#ifdef HAS_BIAS
+ VECTOR_DECLARATION(biases),
+#endif /* defined(HAS_BIAS) */
+ unsigned int weights_stride_w)
+{
+ Image src = CONVERT_TO_IMAGE_STRUCT(src);
+ Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);
+ Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
+
+ VEC_DATA_TYPE(DATA_TYPE, 8)
+ values0 = 0;
+
+ const int id0 = get_global_id(0);
+ const int id1 = get_global_id(1);
+ const int id2 = get_global_id(2);
+
+ __global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0);
+ __global uchar *src_addr = (__global uchar *)offset(&src, 0, 0) - src_stride_x * id0 + ((id2 * STRIDE_Y) - PAD_TOP) * (int)src_stride_z;
+
+ weights_addr += id0 * weights_stride_w;
+ const int coordy = id2 - PAD_TOP;
+
+ for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
+ {
+#if(PAD_TOP)
+ if(coordy < 0) // special case Z = -1 doesn't exists
+ {
+ //skip first row and load the two next ones
+ CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+ CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+ CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+ CONVOLUTION1x5_NHWC(values0, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z));
+ }
+ else if(coordy == (DST_HEIGHT - PAD_TOP - 1))
+ {
+ // special case when computing the last row of the output we must read the last three rows from the input buffer (including padding) but the
+ // Z axis has no padding at all.
+ CONVOLUTION1x5_NHWC(values0, src_addr, weights_addr);
+ CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+ CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+ CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+ }
+ else
+ {
+ CONVOLUTION1x5_NHWC(values0, src_addr, weights_addr);
+ CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+ CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+ CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+ CONVOLUTION1x5_NHWC(values0, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z));
+ }
+#else //PAD_TOP > 0
+ CONVOLUTION1x5_NHWC(values0, src_addr, weights_addr);
+ CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+ CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+ CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+ CONVOLUTION1x5_NHWC(values0, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z));
+#endif // PAD_TOP > 0
+
+ src_addr += src_stride_x;
+ weights_addr += weights_stride_x;
+ }
+
+#ifdef HAS_BIAS
+ Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
+ values0 += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, id0)));
+#endif /* defined(HAS_BIAS) */
+
+ *((__global DATA_TYPE *)(dst.ptr + 0 * dst_stride_y)) = values0.s0;
+ *((__global DATA_TYPE *)(dst.ptr + 1 * dst_stride_y)) = values0.s1;
+ *((__global DATA_TYPE *)(dst.ptr + 2 * dst_stride_y)) = values0.s2;
+ *((__global DATA_TYPE *)(dst.ptr + 3 * dst_stride_y)) = values0.s3;
+ *((__global DATA_TYPE *)(dst.ptr + 4 * dst_stride_y)) = values0.s4;
+ *((__global DATA_TYPE *)(dst.ptr + 5 * dst_stride_y)) = values0.s5;
+ *((__global DATA_TYPE *)(dst.ptr + 6 * dst_stride_y)) = values0.s6;
+ *((__global DATA_TYPE *)(dst.ptr + 7 * dst_stride_y)) = values0.s7;
+}
+
+#endif // defined(DATA_LAYOUT_NHWC)
+
/** This kernel performs a direct convolution to convolve the low three dimensions.
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
@@ -119,7 +303,7 @@
Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
VEC_DATA_TYPE(DATA_TYPE, 8)
- pixels0 = 0;
+ values0 = 0;
__global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0);
__global uchar *src_addr = (__global uchar *)offset(&src, 0, 0);
@@ -129,11 +313,11 @@
for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
{
- CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)src_addr, (__global DATA_TYPE *)weights_addr);
- CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
- CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
- CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y));
- CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_y));
+ CONVOLUTION1x5(values0, (__global DATA_TYPE *)src_addr, (__global DATA_TYPE *)weights_addr);
+ CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
+ CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
+ CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y));
+ CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_y));
src_addr += src_stride_z;
weights_addr += weights_stride_z;
@@ -142,10 +326,10 @@
#ifdef HAS_BIAS
Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
- pixels0 += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index)));
+ values0 += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index)));
#endif /* defined(HAS_BIAS) */
- vstore8(pixels0, 0, (__global DATA_TYPE *)dst.ptr);
+ vstore8(values0, 0, (__global DATA_TYPE *)dst.ptr);
}
#endif // defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
@@ -226,8 +410,8 @@
Image src = CONVERT_TO_IMAGE_STRUCT(src);
Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
- float4 pixels0 = 0.0f;
- float4 pixels1 = 0.0f;
+ float4 values0 = 0.0f;
+ float4 values1 = 0.0f;
__global uchar *weights_addr = (__global uchar *)(weights_ptr + weights_offset_first_element_in_bytes + kernel_index * weights_stride_w);
__global uchar *src_addr = (__global uchar *)offset(&src, 0, 0);
@@ -247,14 +431,14 @@
src0 = vload8(0, (__global float *)(src_addr + 0 * src_stride_y));
// Accumulate
- CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row00, weights_row01);
+ CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01);
// Load values from row1 of input tensor
src0 = vload8(0, (__global float *)(src_addr + 1 * src_stride_y));
// Accumulate
- CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row10, weights_row11);
- CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row00, weights_row01);
+ CONVOLUTION1x5_BIFROST(values0, src0, weights_row10, weights_row11);
+ CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01);
// Load values from row2 of input tensor
src0 = vload8(0, (__global float *)(src_addr + 2 * src_stride_y));
@@ -264,8 +448,8 @@
weights_row01 = *((__global float *)(weights_addr + 2 * weights_stride_y) + 4);
// Accumulate
- CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row00, weights_row01);
- CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row10, weights_row11);
+ CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01);
+ CONVOLUTION1x5_BIFROST(values1, src0, weights_row10, weights_row11);
// Load values from row3 of input tensor
src0 = vload8(0, (__global float *)(src_addr + 3 * src_stride_y));
@@ -275,8 +459,8 @@
weights_row11 = *((__global float *)(weights_addr + 3 * weights_stride_y) + 4);
// Accumulate
- CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row10, weights_row11);
- CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row00, weights_row01);
+ CONVOLUTION1x5_BIFROST(values0, src0, weights_row10, weights_row11);
+ CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01);
// Load values from row4 of input tensor
src0 = vload8(0, (__global float *)(src_addr + 4 * src_stride_y));
@@ -285,14 +469,14 @@
weights_row00 = vload4(0, (__global float *)(weights_addr + 4 * weights_stride_y));
weights_row01 = *((__global float *)(weights_addr + 4 * weights_stride_y) + 4);
- CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row00, weights_row01);
- CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row10, weights_row11);
+ CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01);
+ CONVOLUTION1x5_BIFROST(values1, src0, weights_row10, weights_row11);
// Load values from row5 of input tensor
src0 = vload8(0, (__global float *)(src_addr + 5 * src_stride_y));
// Accumulate
- CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row00, weights_row01);
+ CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01);
src_addr += src_stride_z;
weights_addr += weights_stride_z;
@@ -303,11 +487,11 @@
float4 bias = (float4) * ((__global float *)(vector_offset(&biases, kernel_index)));
- pixels0 += bias;
- pixels1 += bias;
+ values0 += bias;
+ values1 += bias;
#endif /* defined(HAS_BIAS) */
- vstore4(pixels0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y));
- vstore4(pixels1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y));
+ vstore4(values0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y));
+ vstore4(values1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y));
}
#endif // defined(WEIGHTS_DEPTH)
diff --git a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
index 7f7437d..754f0d8 100644
--- a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
+++ b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
@@ -47,19 +47,20 @@
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(0) != weights->dimension(1),
- "Weights should have same width as length");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(0) != 1 && weights->dimension(0) != 3 && weights->dimension(0) != 5,
+
+ const DataLayout data_layout = input->data_layout();
+ const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != weights->dimension(height_idx), "Weights should have same width and height");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != 1 && weights->dimension(width_idx) != 3 && weights->dimension(width_idx) != 5,
"Kernel sizes other than 1x1, 3x3 or 5x5 are not supported");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(2) != input->dimension(2),
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(channel_idx) != input->dimension(channel_idx),
"Weights feature map dimension should match the respective input's one");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(0) != weights->dimension(1),
- "Only rectangular weights are supported!");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->num_dimensions() > 4,
- "Weights can be at most 4 dimensional");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((weights->dimension(0) == 1) && std::get<0>(conv_info.stride()) > 3,
- "Strides larger than 3 not supported for 1x1 convolution.");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((weights->dimension(0) == 3 || weights->dimension(0) == 5) && std::get<0>(conv_info.stride()) > 2,
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->num_dimensions() > 4, "Weights can be at most 4 dimensional");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((weights->dimension(width_idx) == 1) && std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported for 1x1 convolution.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((weights->dimension(width_idx) == 3 || weights->dimension(width_idx) == 5) && std::get<0>(conv_info.stride()) > 2,
"Strides larger than 2 not supported for 3x3 convolution.");
if(biases != nullptr)
@@ -89,36 +90,27 @@
return Status{};
}
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, const GPUTarget target)
+inline bool can_run_optimized_kernel_for_bifrost(GPUTarget gpu_target, unsigned int conv_stride_x, unsigned int conv_stride_y, unsigned int kernel_size,
+ DataType data_type, DataLayout data_layout)
{
- const unsigned int kernel_size = weights->dimension(0);
- const DataType data_type = input->data_type();
+ return gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::G76) && (kernel_size <= 5)
+ && (conv_stride_x == 1) && (conv_stride_y == 1) && (data_type == DataType::F32) && (data_layout == DataLayout::NCHW);
+}
- // Get convolved dimensions
- TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*input, *weights, conv_info);
+inline void setup_num_elems(unsigned int &num_elems_read_per_iteration_x, unsigned int &num_elems_read_per_iteration_y,
+ unsigned int &num_elems_written_per_iteration_x, unsigned int &num_elems_written_per_iteration_y,
+ unsigned int kernel_size, const PadStrideInfo &conv_info, const GPUTarget target, ITensorInfo *input)
+{
+ const DataType data_type = input->data_type();
+ const DataLayout data_layout = input->data_layout();
+ unsigned int conv_stride_x = std::get<0>(conv_info.stride());
+ unsigned int conv_stride_y = std::get<1>(conv_info.stride());
- // Output auto inizialitation if not yet initialized
- // FIXME: input->clone()->set_tensor_shape(output_shape) doesn't work with subtensors for grouped direct convolutions (AlexNet).
- auto_init_if_empty(*output, output_shape,
- 1,
- input->data_type(),
- input->quantization_info());
+ const bool run_optimized_bifrost = can_run_optimized_kernel_for_bifrost(target, conv_stride_x, conv_stride_y, kernel_size, data_type, data_layout);
- unsigned int conv_stride_x = std::get<0>(conv_info.stride());
- unsigned int conv_stride_y = std::get<1>(conv_info.stride());
- unsigned int conv_pad_left = conv_info.pad_left();
- unsigned int conv_pad_top = conv_info.pad_top();
-
- unsigned int num_elems_read_per_iteration_x = 0;
- unsigned int num_elems_read_per_iteration_y = 0;
- unsigned int num_elems_written_per_iteration_x = 0;
- unsigned int num_elems_written_per_iteration_y = 0;
-
- if(gpu_target_is_in(target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::G76) && (kernel_size <= 5) && (conv_stride_x == 1)
- && (conv_stride_y == 1) && (data_type == DataType::F32))
+ if(run_optimized_bifrost)
{
// Configure kernel window
-
switch(kernel_size)
{
case 1:
@@ -218,22 +210,124 @@
}
}
+ if(data_layout == DataLayout::NHWC)
+ {
+ num_elems_written_per_iteration_x = 1;
+ num_elems_read_per_iteration_x = 1;
+ switch(kernel_size)
+ {
+ case 1:
+ switch(conv_stride_x)
+ {
+ case 1:
+ num_elems_read_per_iteration_y = 8;
+ num_elems_written_per_iteration_y = 8;
+ break;
+ case 2:
+ num_elems_read_per_iteration_y = 16;
+ num_elems_written_per_iteration_y = 8;
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Invalid convolution stride X");
+ }
+ break;
+ case 3:
+ switch(conv_stride_x)
+ {
+ case 1:
+ num_elems_read_per_iteration_y = 10;
+ num_elems_written_per_iteration_y = 8;
+ break;
+ case 2:
+ num_elems_read_per_iteration_y = 17;
+ num_elems_written_per_iteration_y = 8;
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Invalid convolution stride X");
+ }
+ break;
+ case 5:
+ switch(conv_stride_x)
+ {
+ case 1:
+ num_elems_read_per_iteration_y = 12;
+ num_elems_written_per_iteration_y = 8;
+ break;
+ case 2:
+ num_elems_read_per_iteration_y = 20;
+ num_elems_written_per_iteration_y = 8;
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Invalid convolution stride X");
+ }
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Not implemented.");
+ break;
+ }
+ }
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, const GPUTarget target)
+{
+ const DataLayout data_layout = input->data_layout();
+ const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const unsigned int kernel_size = weights->dimension(width_idx);
+
+ // Get convolved dimensions
+ TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*input, *weights, conv_info);
+
+ // Output auto inizialitation if not yet initialized
+ // FIXME: input->clone()->set_tensor_shape(output_shape) doesn't work with subtensors for grouped direct convolutions (AlexNet).
+ auto_init_if_empty(*output, output_shape,
+ 1,
+ input->data_type(),
+ input->quantization_info());
+
+ unsigned int num_elems_read_per_iteration_x = 0;
+ unsigned int num_elems_read_per_iteration_y = 0;
+ unsigned int num_elems_written_per_iteration_x = 0;
+ unsigned int num_elems_written_per_iteration_y = 0;
+
+ unsigned int conv_pad_left = conv_info.pad_left();
+ unsigned int conv_pad_top = conv_info.pad_top();
+ unsigned int conv_stride_x = std::get<0>(conv_info.stride());
+ unsigned int conv_stride_y = std::get<1>(conv_info.stride());
+
+ setup_num_elems(num_elems_read_per_iteration_x, num_elems_read_per_iteration_y,
+ num_elems_written_per_iteration_x, num_elems_written_per_iteration_y,
+ kernel_size, conv_info, target, input);
+
// Create window and update padding
bool window_changed = false;
Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y));
- AccessWindowRectangle input_access(input, -conv_pad_left, -conv_pad_top,
- num_elems_read_per_iteration_x, num_elems_read_per_iteration_y,
- conv_stride_x, conv_stride_y);
- AccessWindowStatic weights_access(weights, 0, 0, kernel_size, kernel_size);
- AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);
-
- window_changed = update_window_and_padding(win, input_access, weights_access, output_access);
-
- output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
-
- Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
- return std::make_pair(err, win);
+ if(data_layout == DataLayout::NHWC)
+ {
+ AccessWindowStatic input_access(input, 0, -conv_pad_left,
+ num_elems_read_per_iteration_x,
+ ceil_to_multiple(input->dimension(1) + conv_info.pad_right(), num_elems_read_per_iteration_y));
+ AccessWindowStatic weights_access(weights, 0, 0, weights->dimension(0), weights->dimension(1));
+ AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);
+ window_changed = update_window_and_padding(win, input_access, weights_access, output_access);
+ output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, win);
+ }
+ else if(data_layout == DataLayout::NCHW)
+ {
+ AccessWindowRectangle input_access(input, -conv_pad_left, -conv_pad_top, num_elems_read_per_iteration_x, num_elems_read_per_iteration_y, conv_stride_x, conv_stride_y);
+ AccessWindowStatic weights_access(weights, 0, 0, kernel_size, kernel_size);
+ AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y);
+ window_changed = update_window_and_padding(win, input_access, weights_access, output_access);
+ output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, win);
+ }
+ else
+ {
+ ARM_COMPUTE_ERROR("Not supported");
+ }
}
} // namespace
@@ -251,7 +345,12 @@
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
- const unsigned int kernel_size = weights->info()->dimension(0);
+ const DataLayout data_layout = input->info()->data_layout();
+ const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+
+ const unsigned int kernel_size = weights->info()->dimension(width_idx);
const DataType data_type = input->info()->data_type();
// Get convolved dimensions
@@ -274,7 +373,19 @@
_conv_stride_x = std::get<0>(conv_info.stride());
_conv_stride_y = std::get<1>(conv_info.stride());
- _border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left());
+
+ if(data_layout == DataLayout::NHWC)
+ {
+ _border_size = BorderSize(conv_info.pad_left(), 0, conv_info.pad_right(), 0);
+ }
+ else if(data_layout == DataLayout::NCHW)
+ {
+ _border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left());
+ }
+ else
+ {
+ ARM_COMPUTE_ERROR("Not supported");
+ }
_input = input;
_weights = weights;
@@ -285,14 +396,19 @@
std::stringstream kernel_name;
kernel_name << "direct_convolution" << kernel_size << "x" << kernel_size;
+ if(data_layout == DataLayout::NHWC)
+ {
+ kernel_name << "_" << lower_string(string_from_data_layout(data_layout));
+ }
CLBuildOptions build_options;
build_options.add_option_if(_biases != nullptr, std::string("-DHAS_BIAS"));
- if(gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::G76) && (kernel_size <= 5) && (_conv_stride_x == 1)
- && (_conv_stride_y == 1) && (data_type == DataType::F32))
+ const bool run_optimized_for_bifrost = can_run_optimized_kernel_for_bifrost(gpu_target, _conv_stride_x, _conv_stride_y, kernel_size, data_type, data_layout);
+
+ if(run_optimized_for_bifrost)
{
- build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(2))));
+ build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(channel_idx))));
kernel_name << "_f32_bifrost";
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name.str(), build_options.options()));
@@ -304,10 +420,20 @@
build_options.add_option_if(is_quantized_asymm, std::string("-DKERNEL_SIZE=" + support::cpp11::to_string(kernel_size)));
build_options.add_option(std::string("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)));
build_options.add_option(std::string("-DDATA_SIZE=" + get_data_size_from_data_type(data_type)));
- build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(2))));
+ build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(channel_idx))));
build_options.add_option(std::string("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x)));
+ if(data_layout == DataLayout::NHWC)
+ {
+ build_options.add_option(std::string("-DDATA_LAYOUT_NHWC=1"));
+ build_options.add_option(std::string("-DDST_HEIGHT=" + support::cpp11::to_string(_output->info()->dimension(height_idx))));
+ build_options.add_option(std::string("-DDST_WIDTH=" + support::cpp11::to_string(_output->info()->dimension(width_idx))));
+ build_options.add_option(std::string("-DSRC_HEIGHT=" + support::cpp11::to_string(_input->info()->dimension(height_idx))));
+ build_options.add_option(std::string("-DSRC_WIDTH=" + support::cpp11::to_string(_input->info()->dimension(width_idx))));
+ build_options.add_option(std::string("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())));
+ build_options.add_option(std::string("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())));
+ build_options.add_option(std::string("-DSTRIDE_Y=" + support::cpp11::to_string(_conv_stride_y)));
+ }
build_options.add_option(std::string("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(data_type)));
-
// Create kernel
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(is_quantized_asymm ? "direct_convolution_1x1_3x3_5x5_quantized" : kernel_name.str(),
build_options.options()));
@@ -353,9 +479,11 @@
_config_id += "_";
_config_id += support::cpp11::to_string(_conv_stride_y);
_config_id += "_";
- _config_id += support::cpp11::to_string(output->info()->dimension(0));
+ _config_id += support::cpp11::to_string(output->info()->dimension(width_idx));
_config_id += "_";
- _config_id += support::cpp11::to_string(output->info()->dimension(1));
+ _config_id += support::cpp11::to_string(output->info()->dimension(height_idx));
+ _config_id += "_";
+ _config_id += lower_string(string_from_data_layout(data_layout));
}
Status CLDirectConvolutionLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
@@ -378,12 +506,16 @@
win_in.adjust(Window::DimX, -_border_size.left, true);
win_in.adjust(Window::DimY, -_border_size.top, true);
- win_in.set_dimension_step(Window::DimX, window.x().step() * _conv_stride_x);
- win_in.set_dimension_step(Window::DimY, window.y().step() * _conv_stride_y);
- Window slice_in = win_in.first_slice_window_3D();
+ const DataLayout data_layout = _input->info()->data_layout();
+ const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
- unsigned int idx1 = 2 * num_arguments_per_3D_tensor();
+ win_in.set_dimension_step(width_idx, window[width_idx].step() * _conv_stride_x);
+ win_in.set_dimension_step(height_idx, window[height_idx].step() * _conv_stride_y);
+
+ Window slice_in = win_in.first_slice_window_3D();
+ unsigned int idx1 = 2 * num_arguments_per_3D_tensor();
add_3D_tensor_argument(idx1, _weights, slice);
if(_biases != nullptr)
@@ -400,7 +532,6 @@
unsigned int idx = 0;
add_3D_tensor_argument(idx, _input, slice_in);
add_3D_tensor_argument(idx, _output, slice);
-
enqueue(queue, *this, slice, _lws_hint);
}
while(window.slide_window_slice_3D(slice) && win_in.slide_window_slice_3D(slice_in));