COMPMID-1277 - Optimizing CLIm2ColKernel for NHWC.
This patch includes:
- Im2Col optimizations for NHWC using a new data layout
- Refactoring of CLIm2ColKernel adding validation method and auto-init
- Removed im2col_reduced from CLIm2ColKernel and created a new kernel CLFlattenLayerKernel
Change-Id: I1620640b6796baa268324b33ae92cdd8de53e27c
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/141241
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
diff --git a/src/core/CL/cl_kernels/im2col.cl b/src/core/CL/cl_kernels/im2col.cl
index d034b30..274ec20 100644
--- a/src/core/CL/cl_kernels/im2col.cl
+++ b/src/core/CL/cl_kernels/im2col.cl
@@ -35,13 +35,12 @@
#error "Element size not support"
#endif // ELEMENT_SIZE
-#if defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(KERNEL_DEPTH)
-/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 1x1 and the stride_x = 1
+#if defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(SRC_DEPTH)
+/** This opencl kernel performs im2col when the kernel size is 1x1, the stride_x = 1 and the data layout is NCHW
*
- * @note This kernel computes 4 elements
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
- * @note The kernel depth must be passed at compile time using -DKERNEL_DEPTH: e.g. -DKERNEL_DEPTH=3
+ * @note The number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3
* @note The stride along the Y direction must be passed at compile time using -DSTRIDE_Y: e.g. -DSTRIDE_Y=1
* @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.
*
@@ -62,16 +61,16 @@
* @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
-__kernel void im2col1x1_stridex1_dchw(
+__kernel void im2col1x1_stridex1_nchw(
TENSOR3D_DECLARATION(src),
IMAGE_DECLARATION(dst),
uint src_stride_w,
uint dst_stride_w)
{
- const uint xc = get_global_id(0) * 4; // x coordinate in the convolved tensor
- const uint yc = get_global_id(1); // y coordinate in the convolved tensor
- const uint ch = get_global_id(2) % KERNEL_DEPTH; // input feature map
- const uint batch = get_global_id(2) / KERNEL_DEPTH; // batch size
+ const uint xc = get_global_id(0) * 4; // x coordinate in the convolved tensor
+ const uint yc = get_global_id(1); // y coordinate in the convolved tensor
+ const uint ch = get_global_id(2) % SRC_DEPTH; // input feature map
+ const uint batch = get_global_id(2) / SRC_DEPTH; // batch size
// Clamp xc
// The strategy clamps at "xc" as it will be a valid value for sure
@@ -107,7 +106,7 @@
*(__global DATA_TYPE *)(output_ptr + yo.s3 * dst_stride_y) = data.s3;
#ifdef HAS_BIAS
- if(ch == (KERNEL_DEPTH - 1))
+ if(ch == (SRC_DEPTH - 1))
{
*((__global DATA_TYPE *)(output_ptr + yo.s0 * dst_stride_y) + 1) = 1.0f;
*((__global DATA_TYPE *)(output_ptr + yo.s1 * dst_stride_y) + 1) = 1.0f;
@@ -116,18 +115,16 @@
}
#endif // HAS_BIAS
}
-#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(KERNEL_DEPTH)
+#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(SRC_DEPTH)
-#define PTR_TO_VALUE(PTR, DATA_TYPE) *((__global DATA_TYPE *)(PTR))
-
-#if defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE)
-
-/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 5x5
+#if defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE)
+#if defined(DILATION_X) && defined(DILATION_Y)
+/** This opencl kernel performs a generic im2col implementation when the data layout is NCHW
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128
* @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
- * @note The kernel depth must be passed at compile time using -DKERNEL_DEPTH: e.g. -DKERNEL_DEPTH=3
+ * @note The kernel width, height and depth must be passed at compile time using -DKERNEL_WIDTH, -DKERNEL_HEIGHT and -DSRC_DEPTH: e.g. -DKERNEL_WIDTH=3, -DKERNEL_HEIGHT=3 and -DSRC_DEPTH=64
* @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2
* @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0
* @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
@@ -151,77 +148,65 @@
* @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
-__kernel void im2col_generic_nhwc(
+__kernel void im2col_generic_nchw(
TENSOR3D_DECLARATION(src),
IMAGE_DECLARATION(dst),
uint src_stride_w,
uint dst_stride_w)
{
- const int src_stride_y_int = (int)src_stride_y;
- const int src_stride_z_int = (int)src_stride_z;
- const int xc = get_global_id(1); // x coordinate in the convolved tensor
- const int yc = get_global_id(2) % CONVOLVED_HEIGHT; // y coordinate in the convolved tensor
- const int ch = get_global_id(0); // input feature map
- const int batch = get_global_id(2) / CONVOLVED_HEIGHT; // batch size
+ const int xc = get_global_id(0); // x coordinate in the convolved tensor
+ const int yc = get_global_id(1); // y coordinate in the convolved tensor
+ const int ch = get_global_id(2) % SRC_DEPTH; // input feature map
+ const int batch = get_global_id(2) / SRC_DEPTH; // batch size
// Calculate input indices
const int xi = xc * STRIDE_X - PAD_LEFT;
const int yi = yc * STRIDE_Y - PAD_TOP;
// Calculate output indices
- const int xo = ch * KERNEL_HEIGHT * KERNEL_WIDTH;
+ const int xo = ch * KERNEL_WIDTH * KERNEL_HEIGHT;
const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution
- // Get input and output address
- __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + xi * src_stride_y_int + yi * src_stride_z_int + ch * src_stride_x + batch * src_stride_w;
- __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + batch * dst_stride_w;
+ __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * src_stride_z + batch * src_stride_w;
+ __global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + batch * dst_stride_w)) + xo;
+ // Linearize convolution elements
for(int yk = 0; yk < KERNEL_HEIGHT; ++yk)
{
- const int dilated_offset_y = yk * DILATION_Y;
- const int y0 = yi + dilated_offset_y;
- if(y0 >= 0 && y0 < SRC_HEIGHT)
+ int y = yi + yk * DILATION_Y;
+ for(int xk = 0; xk < KERNEL_WIDTH; ++xk, ++output_ptr)
{
- int xk;
- for(xk = 0; xk < KERNEL_WIDTH; xk++)
+ int x = xi + xk * DILATION_X;
+#if PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
+ *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y));
+#else // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
+ if(x < 0 || x >= SRC_WIDTH || y < 0 || y >= SRC_HEIGHT)
{
- const int dilated_offset_x = xk * DILATION_X;
- const int x0 = xi + dilated_offset_x;
- if(x0 >= 0 && x0 < SRC_WIDTH)
- {
- *((__global DATA_TYPE *)output_ptr) = PTR_TO_VALUE(input_ptr + dilated_offset_x * src_stride_y + dilated_offset_y * src_stride_z, DATA_TYPE);
- }
- else
- {
- *((__global DATA_TYPE *)output_ptr) = PAD_VALUE;
- }
- output_ptr += 1 * sizeof(DATA_TYPE);
+ *output_ptr = PAD_VALUE;
}
- }
- else
- {
- for(int xk = 0; xk < KERNEL_WIDTH; xk++)
+ else
{
- *((__global DATA_TYPE *)output_ptr) = (DATA_TYPE)PAD_VALUE;
- output_ptr += 1 * dst_stride_x;
+ *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y));
}
+#endif // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
}
}
+
#ifdef HAS_BIAS
- if(ch == (KERNEL_DEPTH - 1))
+ if(ch == (SRC_DEPTH - 1))
{
- *((__global DATA_TYPE *)output_ptr) = 1.0f;
- output_ptr += 1 * dst_stride_x;
+ *output_ptr = 1.0f;
}
#endif // HAS_BIAS
}
+#endif // defined(DILATION_X) && defined(DILATION_Y)
-/** This kernel performs a reshaping of the input tensor (with layout NHWC) to a tensor used to perform convolution using GEMM when the kernel size is 3x3
+/** This opencl kernel performs im2col when the kernel size is 3x3 and the data layout is NCHW
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128
* @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
- * @note The kernel depth must be passed at compile time using -DKERNEL_DEPTH: e.g. -DKERNEL_DEPTH=3
+ * @note The number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3
* @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2
* @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0
* @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
@@ -244,122 +229,16 @@
* @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
-__kernel void im2col3x3_nhwc(
+__kernel void im2col3x3_nchw(
TENSOR3D_DECLARATION(src),
IMAGE_DECLARATION(dst),
uint src_stride_w,
uint dst_stride_w)
{
- const int src_stride_y_int = (int)src_stride_y;
- const int src_stride_z_int = (int)src_stride_z;
- const int xc = get_global_id(1); // x coordinate in the convolved tensor
- const int yc = get_global_id(2) % CONVOLVED_HEIGHT; // y coordinate in the convolved tensor
- const int ch = get_global_id(0); // input feature map
- const int batch = get_global_id(2) / CONVOLVED_HEIGHT; // batch size
-
- // Calculate input indices
- const int xi = xc * STRIDE_X - PAD_LEFT;
- const int yi = yc * STRIDE_Y - PAD_TOP;
-
- // Calculate output indices
- const int xo = ch * 9; // 3x3
- const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution
-
- // Get input and output address
- __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + xi * src_stride_y_int + yi * src_stride_z_int + ch * src_stride_x + batch * src_stride_w;
- __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + batch * dst_stride_w;
-
- VEC_DATA_TYPE(DATA_TYPE, 3)
- row0 = (VEC_DATA_TYPE(DATA_TYPE, 3))(PAD_VALUE);
- VEC_DATA_TYPE(DATA_TYPE, 3)
- row1 = (VEC_DATA_TYPE(DATA_TYPE, 3))(PAD_VALUE);
- VEC_DATA_TYPE(DATA_TYPE, 3)
- row2 = (VEC_DATA_TYPE(DATA_TYPE, 3))(PAD_VALUE);
-
- const int3 y = (int3)yi + (int3)(0, 1, 2);
- // Guard against reading outside the input buffer, there is no padding in Z so we check if ry is inside the buffer.
- if(y.s0 >= 0 && y.s0 < SRC_HEIGHT)
- {
- row0 = (VEC_DATA_TYPE(DATA_TYPE, 3))(
- PTR_TO_VALUE(input_ptr + 0 * src_stride_y, DATA_TYPE),
- PTR_TO_VALUE(input_ptr + 1 * src_stride_y, DATA_TYPE),
- PTR_TO_VALUE(input_ptr + 2 * src_stride_y, DATA_TYPE));
- }
-
- if(y.s1 >= 0 && y.s1 < SRC_HEIGHT)
- {
- row1 = (VEC_DATA_TYPE(DATA_TYPE, 3))(
- PTR_TO_VALUE(input_ptr + 0 * src_stride_y + 1 * src_stride_z, DATA_TYPE),
- PTR_TO_VALUE(input_ptr + 1 * src_stride_y + 1 * src_stride_z, DATA_TYPE),
- PTR_TO_VALUE(input_ptr + 2 * src_stride_y + 1 * src_stride_z, DATA_TYPE));
- }
-
- if(y.s2 >= 0 && y.s2 < SRC_HEIGHT)
- {
- row2 = (VEC_DATA_TYPE(DATA_TYPE, 3))(
- PTR_TO_VALUE(input_ptr + 0 * src_stride_y + 2 * src_stride_z, DATA_TYPE),
- PTR_TO_VALUE(input_ptr + 1 * src_stride_y + 2 * src_stride_z, DATA_TYPE),
- PTR_TO_VALUE(input_ptr + 2 * src_stride_y + 2 * src_stride_z, DATA_TYPE));
- }
-
-#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
- // Put 0 if the value is out-of-bound
- const int3 x = (int3)xi + (int3)(0, 1, 2);
- VEC_DATA_TYPE(COND_DATA_TYPE, 3)
- cond0 = CONVERT((x >= (int3)0 && x < (int3)SRC_WIDTH), VEC_DATA_TYPE(COND_DATA_TYPE, 3));
- row0 = select((VEC_DATA_TYPE(DATA_TYPE, 3))PAD_VALUE, row0, cond0);
- row1 = select((VEC_DATA_TYPE(DATA_TYPE, 3))PAD_VALUE, row1, cond0);
- row2 = select((VEC_DATA_TYPE(DATA_TYPE, 3))PAD_VALUE, row2, cond0);
-#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0
- vstore8((VEC_DATA_TYPE(DATA_TYPE, 8))(row0.s012, row1.s012, row2.s01), 0, (__global DATA_TYPE *)output_ptr);
- *((__global DATA_TYPE *)output_ptr + 8) = row2.s2;
-
-#ifdef HAS_BIAS
- if(ch == (KERNEL_DEPTH - 1))
- {
- *((__global DATA_TYPE *)output_ptr + 9) = 1.0f;
- }
-#endif // HAS_BIAS
-}
-
-/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 3x3
- *
- * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
- * @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128
- * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
- * @note The kernel depth must be passed at compile time using -DKERNEL_DEPTH: e.g. -DKERNEL_DEPTH=3
- * @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2
- * @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0
- * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
- * @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: QASYMM8/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 Y 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] src_stride_w Stride of the source tensor in W dimension (in bytes).
- * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
- */
-__kernel void im2col3x3_dchw(
- TENSOR3D_DECLARATION(src),
- IMAGE_DECLARATION(dst),
- uint src_stride_w,
- uint dst_stride_w)
-{
- const int xc = get_global_id(0); // x coordinate in the convolved tensor
- const int yc = get_global_id(1); // y coordinate in the convolved tensor
- const int ch = get_global_id(2) % KERNEL_DEPTH; // input feature map
- const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size
+ const int xc = get_global_id(0); // x coordinate in the convolved tensor
+ const int yc = get_global_id(1); // y coordinate in the convolved tensor
+ const int ch = get_global_id(2) % SRC_DEPTH; // input feature map
+ const int batch = get_global_id(2) / SRC_DEPTH; // batch size
// Calculate input indices
const int xi = xc * STRIDE_X - PAD_LEFT;
@@ -402,19 +281,19 @@
*((__global DATA_TYPE *)output_ptr + 8) = row2.s2;
#ifdef HAS_BIAS
- if(ch == (KERNEL_DEPTH - 1))
+ if(ch == (SRC_DEPTH - 1))
{
*((__global DATA_TYPE *)output_ptr + 9) = 1.0f;
}
#endif // HAS_BIAS
}
-/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 5x5
+/** This opencl kernel performs im2col when the kernel size is 5x5 and the data layout is NCHW
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128
* @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
- * @note The kernel depth must be passed at compile time using -DKERNEL_DEPTH: e.g. -DKERNEL_DEPTH=3
+ * @note The number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3
* @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2
* @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0
* @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
@@ -437,16 +316,16 @@
* @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
-__kernel void im2col5x5_dchw(
+__kernel void im2col5x5_nchw(
TENSOR3D_DECLARATION(src),
IMAGE_DECLARATION(dst),
uint src_stride_w,
uint dst_stride_w)
{
- const int xc = get_global_id(0); // x coordinate in the convolved tensor
- const int yc = get_global_id(1); // y coordinate in the convolved tensor
- const int ch = get_global_id(2) % KERNEL_DEPTH; // input feature map
- const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size
+ const int xc = get_global_id(0); // x coordinate in the convolved tensor
+ const int yc = get_global_id(1); // y coordinate in the convolved tensor
+ const int ch = get_global_id(2) % SRC_DEPTH; // input feature map
+ const int batch = get_global_id(2) / SRC_DEPTH; // batch size
// Calculate input indices
const int xi = xc * STRIDE_X - PAD_LEFT;
@@ -576,20 +455,20 @@
}
#ifdef HAS_BIAS
- if(ch == (KERNEL_DEPTH - 1))
+ if(ch == (SRC_DEPTH - 1))
{
*((__global DATA_TYPE *)output_ptr) = 1.0f;
}
#endif // HAS_BIAS
}
-#endif // defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE)
+#endif // defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE)
-#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_DEPTH)
-/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM when the kernel size is 11x11
+#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH)
+/** This opencl kernel performs im2col when the kernel size is 11x11, we do not have paddings and the data layout is NCHW
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
- * @note The kernel depth must be passed at compile time using -DKERNEL_DEPTH: e.g. -DKERNEL_DEPTH=3
+ * @note The number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3
* @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
* @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.
*
@@ -610,16 +489,16 @@
* @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
-__kernel void im2col11x11_padx0_pady0_dchw(
+__kernel void im2col11x11_padx0_pady0_nchw(
TENSOR3D_DECLARATION(src),
IMAGE_DECLARATION(dst),
uint src_stride_w,
uint dst_stride_w)
{
- const int xc = get_global_id(0); // x coordinate in the convolved tensor
- const int yc = get_global_id(1); // y coordinate in the convolved tensor
- const int ch = get_global_id(2) % KERNEL_DEPTH; // input feature map
- const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size
+ const int xc = get_global_id(0); // x coordinate in the convolved tensor
+ const int yc = get_global_id(1); // y coordinate in the convolved tensor
+ const int ch = get_global_id(2) % SRC_DEPTH; // input feature map
+ const int batch = get_global_id(2) / SRC_DEPTH; // batch size
// Calculate input indices
const int xi = xc * STRIDE_X;
@@ -776,21 +655,21 @@
}
#ifdef HAS_BIAS
- if(ch == (KERNEL_DEPTH - 1))
+ if(ch == (SRC_DEPTH - 1))
{
*((__global DATA_TYPE *)output_ptr) = 1.0f;
}
#endif // HAS_BIAS
}
-#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_DEPTH)
+#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH)
-#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE)
-/** This kernel reshapes the input tensor to a tensor used to perform convolution using GEMM when
- * the kernel width is greater than 1 (except when the kernel size is 3x3) and pad_x == pad_y == 0.
+#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE)
+/** This opencl kernel performs im2col when the kernel size is greater than 1x1, we do not have paddings and the data layout is NCHW
*
* @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float.
* @note The vector size must be passed at compile time using -DVECTOR_SIZE e.g. -DVECTOR_SIZE=4.
* @note The width modulo vector size must be passed at compile time using -DWIDTH_MOD_VECTOR_SIZE e.g. -DWIDTH_MOD_VECTOR_SIZE=3.
+ * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
* @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
@@ -810,16 +689,16 @@
* @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
-__kernel void im2col_generic_padx0_pady0_dchw(
+__kernel void im2col_generic_padx0_pady0_nchw(
TENSOR3D_DECLARATION(src),
IMAGE_DECLARATION(dst),
uint src_stride_w,
uint dst_stride_w)
{
- const int xc = get_global_id(0); // x coordinate in the convolved tensor
- const int yc = get_global_id(1); // y coordinate in the convolved tensor
- const int ch = get_global_id(2) % KERNEL_DEPTH; // input feature map
- const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size
+ const int xc = get_global_id(0); // x coordinate in the convolved tensor
+ const int yc = get_global_id(1); // y coordinate in the convolved tensor
+ const int ch = get_global_id(2) % SRC_DEPTH; // input feature map
+ const int batch = get_global_id(2) / SRC_DEPTH; // batch size
// Calculate input indices
const int xi = xc * STRIDE_X;
@@ -855,21 +734,187 @@
} /* End of loop over KERNEL_HEIGHT */
#ifdef HAS_BIAS
- if(ch == (KERNEL_DEPTH - 1))
+ if(ch == (SRC_DEPTH - 1))
{
*output_ptr = 1.0f;
}
#endif // HAS_BIAS
}
-#endif //defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE)
+#endif //defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE)
-#if defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE)
-/** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM.
+#if defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE) && defined(VECTOR_SIZE) && defined(LAST_ACCESSED)
+
+#define VECTOR_N VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE)
+
+/** This kernel performs im2col when the kernel size is 3x3 and the data layout is NHWC
+ *
+ * @note This kernel computes VECTOR_SIZE elements
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
+ * @note The kernel depth must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3
+ * @note The stride along the Y direction must be passed at compile time using -DSTRIDE_Y: e.g. -DSTRIDE_Y=1
+ * @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: QASYMM8/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 Y 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] src_stride_w Stride of the source tensor in W dimension (in bytes).
+ * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
+ */
+__kernel void im2col3x3_nhwc(
+ TENSOR3D_DECLARATION(src),
+ IMAGE_DECLARATION(dst),
+ uint src_stride_w,
+ uint dst_stride_w)
+{
+ const int ch = min((int)(get_global_id(0) * VECTOR_SIZE), LAST_ACCESSED); // input feature map
+ const int yo = get_global_id(1);
+ const int batch = get_global_id(2); // batch size
+
+ // Calculate input indices
+ const int xi = (get_global_id(1) % CONVOLVED_WIDTH) * STRIDE_X;
+ const int yi = (get_global_id(1) / (int)CONVOLVED_WIDTH) * STRIDE_Y;
+
+ // Get input and output address
+ __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * sizeof(DATA_TYPE) + batch * (int)src_stride_w;
+ __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + ch * sizeof(DATA_TYPE) + yo * (int)dst_stride_y + batch * (int)dst_stride_w;
+
+ int yi_coord = 0;
+ int3 offset = 0;
+
+ // Clamp xi
+ int3 xi_offset = ((int3)xi + (int3)(0, 1, 2) * DILATION_X - (int3)PAD_LEFT);
+#if PAD_TOP != 0 || PAD_BOTTOM != 0
+#define CLAMP(x, min_val, max_val) min(max(x, min_val), max_val)
+ xi_offset = CLAMP(xi_offset, (int3)0, (int3)(SRC_WIDTH - 1));
+#endif // PAD_TOP != 0 || PAD_BOTTOM != 0
+ xi_offset *= (int3)src_stride_y;
+
+ // Out-of-bound condition for X
+ int3 x_cond = (((int3)xi + (int3)(0, 1, 2) * DILATION_X - (int3)PAD_LEFT) < (int3)0) || (((int3)xi + (int3)(0, 1, 2) * DILATION_X - (int3)PAD_LEFT) >= (int3)SRC_WIDTH);
+
+ // yi == 0
+ // Clamp yi
+ // yi_coord is casted to unsigned int in order to use just a min() operation
+ // A "-1" 32 bit signed variable converted to unsigned gives 4294967295
+ yi_coord = yi - (int)PAD_TOP;
+
+ // Clamp only if PAD_TOP or PAD_BOTTOM is not equal to 0
+#if PAD_TOP != 0 || PAD_BOTTOM != 0
+ yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1));
+#endif // PAD_TOP != 0 || PAD_BOTTOM != 0
+
+ // Compute offset
+ offset = xi_offset + (yi_coord * (int)src_stride_z);
+
+ // Load input values
+ VECTOR_N values0 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s0));
+ VECTOR_N values1 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s1));
+ VECTOR_N values2 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s2));
+
+#if PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
+ // Replace invalid values with PAD_VALUE
+ int y_cond = (int)((uint)(yi - (int)PAD_TOP) >= (uint)(SRC_HEIGHT));
+ values0 = select(values0, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s0));
+ values1 = select(values1, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s1));
+ values2 = select(values2, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s2));
+#endif // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
+
+ // yi == 1
+ // Clamp yi_coord (it can be negative if PAD_TOP > 1)
+ yi_coord = yi - (int)PAD_TOP + 1 * DILATION_Y;
+
+ // Clamp only if PAD_TOP or PAD_BOTTOM is not equal to 0
+#if PAD_TOP != 0 || PAD_BOTTOM != 0
+ yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1));
+#endif // PAD_TOP != 0 || PAD_BOTTOM != 0
+
+ // Compute offset
+ offset = xi_offset + (yi_coord * (int)src_stride_z);
+
+ // Load input values
+ VECTOR_N values3 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s0));
+ VECTOR_N values4 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s1));
+ VECTOR_N values5 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s2));
+
+#if PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
+ // Replace invalid values with zeros
+ y_cond = (int)((uint)(yi - (int)PAD_TOP + 1) >= (uint)(SRC_HEIGHT));
+ values3 = select(values3, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s0));
+ values4 = select(values4, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s1));
+ values5 = select(values5, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s2));
+#endif // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
+
+ // yi == 2
+ // Clamp yi_coord
+ yi_coord = yi - (int)PAD_TOP + 2 * DILATION_Y;
+
+ // Clamp only if PAD_TOP or PAD_BOTTOM is not equal to 0
+#if PAD_TOP != 0 || PAD_BOTTOM != 0
+ yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1));
+#endif // PAD_TOP != 0 || PAD_BOTTOM != 0
+
+ // Compute offset
+ offset = xi_offset + (yi_coord * (int)src_stride_z);
+
+ // Load input values
+ VECTOR_N values6 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s0));
+ VECTOR_N values7 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s1));
+ VECTOR_N values8 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset.s2));
+
+#if PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
+ // Replace invalid values with PAD_VALUE
+ y_cond = (int)((uint)(yi - (int)PAD_TOP + 2) >= (uint)(SRC_HEIGHT));
+ values6 = select(values6, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s0));
+ values7 = select(values7, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s1));
+ values8 = select(values8, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond.s2));
+#endif // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0
+
+ // Store
+ VSTORE(VECTOR_SIZE)
+ (values0, 0, (__global DATA_TYPE *)(output_ptr) + 0 * SRC_DEPTH);
+ VSTORE(VECTOR_SIZE)
+ (values1, 0, (__global DATA_TYPE *)(output_ptr) + 1 * SRC_DEPTH);
+ VSTORE(VECTOR_SIZE)
+ (values2, 0, (__global DATA_TYPE *)(output_ptr) + 2 * SRC_DEPTH);
+ VSTORE(VECTOR_SIZE)
+ (values3, 0, (__global DATA_TYPE *)(output_ptr) + 3 * SRC_DEPTH);
+ VSTORE(VECTOR_SIZE)
+ (values4, 0, (__global DATA_TYPE *)(output_ptr) + 4 * SRC_DEPTH);
+ VSTORE(VECTOR_SIZE)
+ (values5, 0, (__global DATA_TYPE *)(output_ptr) + 5 * SRC_DEPTH);
+ VSTORE(VECTOR_SIZE)
+ (values6, 0, (__global DATA_TYPE *)(output_ptr) + 6 * SRC_DEPTH);
+ VSTORE(VECTOR_SIZE)
+ (values7, 0, (__global DATA_TYPE *)(output_ptr) + 7 * SRC_DEPTH);
+ VSTORE(VECTOR_SIZE)
+ (values8, 0, (__global DATA_TYPE *)(output_ptr) + 8 * SRC_DEPTH);
+
+#ifdef HAS_BIAS
+ if((ch + VECTOR_SIZE) >= SRC_DEPTH)
+ {
+ *((__global DATA_TYPE *)(output_ptr) - ch + SRC_DEPTH * 9) = 1.0f;
+ }
+#endif // HAS_BIAS
+}
+
+/** This opencl kernel performs a generic im2col implementation when the data layout is NHWC
*
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128
* @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34
- * @note The kernel width, height and depth must be passed at compile time using -DKERNEL_WIDTH, -DKERNEL_HEIGHT and -DKERNEL_DEPTH: e.g. -DKERNEL_WIDTH=3, -DKERNEL_HEIGHT=3 and -DKERNEL_DEPTH=64
+ * @note The kernel width, height and depth must be passed at compile time using -DKERNEL_WIDTH, -DKERNEL_HEIGHT and -DSRC_DEPTH: e.g. -DKERNEL_WIDTH=3, -DKERNEL_HEIGHT=3 and -DSRC_DEPTH=64
* @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2
* @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0
* @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
@@ -893,100 +938,64 @@
* @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes).
* @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes).
*/
-__kernel void im2col_generic_dchw(
+__kernel void im2col_generic_nhwc(
TENSOR3D_DECLARATION(src),
IMAGE_DECLARATION(dst),
uint src_stride_w,
uint dst_stride_w)
{
- const int xc = get_global_id(0); // x coordinate in the convolved tensor
- const int yc = get_global_id(1); // y coordinate in the convolved tensor
- const int ch = get_global_id(2) % KERNEL_DEPTH; // input feature map
- const int batch = get_global_id(2) / KERNEL_DEPTH; // batch size
+ const int ch = min((int)(get_global_id(0) * VECTOR_SIZE), LAST_ACCESSED); // input feature map
+ const int yo = get_global_id(1);
+ const int batch = get_global_id(2); // batch size
// Calculate input indices
- const int xi = xc * STRIDE_X - PAD_LEFT;
- const int yi = yc * STRIDE_Y - PAD_TOP;
+ const int xi = (get_global_id(1) % CONVOLVED_WIDTH) * STRIDE_X;
+ const int yi = (get_global_id(1) / (int)CONVOLVED_WIDTH) * STRIDE_Y;
- // Calculate output indices
- const int xo = ch * KERNEL_WIDTH * KERNEL_HEIGHT;
- const int yo = xc + yc * CONVOLVED_WIDTH; // Index of the convolution
+ // Get input and output address
+ __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * sizeof(DATA_TYPE) + batch * (int)src_stride_w;
+ __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + ch * sizeof(DATA_TYPE) + yo * (int)dst_stride_y + batch * (int)dst_stride_w;
- __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * src_stride_z + batch * src_stride_w;
- __global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + batch * dst_stride_w)) + xo;
-
- // Linearize convolution elements
+ int i = 0;
for(int yk = 0; yk < KERNEL_HEIGHT; ++yk)
{
- int y = yi + yk * DILATION_Y;
- for(int xk = 0; xk < KERNEL_WIDTH; ++xk, ++output_ptr)
+ // Clamp yi_coord
+ int yi_coord = yi + yk * DILATION_Y - (int)PAD_TOP;
+ yi_coord = CLAMP(yi_coord, (int)0, (int)(SRC_HEIGHT - 1));
+
+ // Out-of-bound condition for Y
+ int y_border_condition = ((yi + yk * DILATION_Y - (int)PAD_TOP) < (int)0) || ((yi + yk * DILATION_Y - (int)PAD_TOP) >= (int)SRC_HEIGHT);
+
+ for(int xk = 0; xk < KERNEL_WIDTH; ++xk)
{
- int x = xi + xk * DILATION_X;
-#if PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
- *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y));
-#else // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
- if(x < 0 || x >= SRC_WIDTH || y < 0 || y >= SRC_HEIGHT)
- {
- *output_ptr = PAD_VALUE;
- }
- else
- {
- *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y));
- }
-#endif // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0
+ // Clamp xi_coord
+ int xi_coord = (xi + xk * DILATION_X - (int)PAD_LEFT);
+ xi_coord = CLAMP(xi_coord, (int)0, (int)(SRC_WIDTH - 1));
+
+ // Out-of-bound condition for X
+ int x_border_condition = ((xi + xk * DILATION_X - (int)PAD_LEFT) < (int)0) || ((xi + xk * DILATION_X - (int)PAD_LEFT) >= (int)SRC_WIDTH);
+
+ int offset = xi_coord * (int)src_stride_y + (yi_coord * (int)src_stride_z);
+
+ VECTOR_N values0 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset));
+
+ // Replace with PAD_VALUE if the value is out-of-bound
+ values0 = select(values0, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))x_border_condition || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(y_border_condition));
+
+ // Store
+ VSTORE(VECTOR_SIZE)
+ (values0, 0, (__global DATA_TYPE *)(output_ptr) + i * (int)SRC_DEPTH);
+
+ i++;
}
}
#ifdef HAS_BIAS
- if(ch == (KERNEL_DEPTH - 1))
+ if((ch + VECTOR_SIZE) >= SRC_DEPTH)
{
- *output_ptr = 1.0f;
+ *((__global DATA_TYPE *)(output_ptr) - ch + SRC_DEPTH * KERNEL_WIDTH * KERNEL_HEIGHT) = 1.0f;
}
#endif // HAS_BIAS
}
-#endif // defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE)
-
-/**This kernel reshapes the input tensor to a tensor used to perform convolution using GEMM when
- * the kernel width and height are the same of width and height of the input tensor
- *
- * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float
- * @note In case biases will be added in late stage, -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: QASYMM8/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 Y 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. 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_offset_first_element_in_bytes The offset of the first element in the destination tensor
- * @param[in] width The width of the input tensor
- * @param[in] height The height of the input tensor
- */
-__kernel void im2col_reduced_dchw(
- TENSOR3D_DECLARATION(src),
- VECTOR_DECLARATION(dst),
- uint width, uint height)
-{
- Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
-
- const uint image_size = width * height;
-
- __global uchar *tmp_out_ptr = dst_ptr + dst_offset_first_element_in_bytes + (get_global_id(0) + get_global_id(1) * width + get_global_id(2) * image_size) * dst_stride_x;
-
- *((__global DATA_TYPE *)tmp_out_ptr) = *((__global DATA_TYPE *)src.ptr);
-
-#ifdef HAS_BIAS
- // If it is the last thread in the 3 dimensional workgroup
- if(get_global_id(0) == (get_global_size(0) - 1) && get_global_id(1) == (get_global_size(1) - 1) && get_global_id(2) == (get_global_size(2) - 1))
- {
- tmp_out_ptr += dst_stride_x;
- *((__global DATA_TYPE *)tmp_out_ptr) = (DATA_TYPE)1.0f;
- }
-#endif // HAS_BIAS
-}
+#endif // defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE) && defined(VECTOR_SIZE) && defined(LAST_ACCESSED)
#endif // defined(DATA_TYPE) && defined(ELEMENT_SIZE)