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/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)