diff options
Diffstat (limited to 'src/core/CL/cl_kernels/direct_convolution3x3.cl')
| -rw-r--r-- | src/core/CL/cl_kernels/direct_convolution3x3.cl | 155 |
1 files changed, 151 insertions, 4 deletions
diff --git a/src/core/CL/cl_kernels/direct_convolution3x3.cl b/src/core/CL/cl_kernels/direct_convolution3x3.cl index 28da544f89..26f24e187b 100644 --- a/src/core/CL/cl_kernels/direct_convolution3x3.cl +++ b/src/core/CL/cl_kernels/direct_convolution3x3.cl @@ -40,6 +40,8 @@ MULQ_SAT_IMPL(qs32x8, qs32x8) #endif /* FIXED_POINT_POSITION */ +#if defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) + #if STRIDE_X == 1 #define CONVOLUTION1x3(acc, src_row_ptr, weights_row_ptr) CONVOLUTION1x3_STRIDE1(acc, src_row_ptr, weights_row_ptr) #elif STRIDE_X == 2 /* STRIDE_X == 1 */ @@ -77,11 +79,12 @@ MULQ_SAT_IMPL(qs32x8, qs32x8) /** 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 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. + * @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_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) @@ -111,7 +114,6 @@ MULQ_SAT_IMPL(qs32x8, qs32x8) * @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 */ -#if defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) __kernel void direct_convolution3x3( TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(dst), @@ -152,4 +154,149 @@ __kernel void direct_convolution3x3( vstore8(CONVERT_SAT(pixels0, VEC_DATA_TYPE(DATA_TYPE, 8)), 0, (__global DATA_TYPE *)dst.ptr); } -#endif // defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) +#endif //defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) + +#if defined(WEIGHTS_DEPTH) + +#define CONVOLUTION1x3_BIFROST(acc, src0, src1, weights_row0) \ + ({ \ + acc.s0 = mad(src0.s0, weights_row0.s0, acc.s0); \ + acc.s1 = mad(src0.s1, weights_row0.s0, acc.s1); \ + acc.s2 = mad(src0.s2, weights_row0.s0, acc.s2); \ + acc.s3 = mad(src0.s3, weights_row0.s0, acc.s3); \ + acc.s0 = mad(src0.s1, weights_row0.s1, acc.s0); \ + acc.s1 = mad(src0.s2, weights_row0.s1, acc.s1); \ + acc.s2 = mad(src0.s3, weights_row0.s1, acc.s2); \ + acc.s3 = mad(src1.s0, weights_row0.s1, acc.s3); \ + acc.s0 = mad(src0.s2, weights_row0.s2, acc.s0); \ + acc.s1 = mad(src0.s3, weights_row0.s2, acc.s1); \ + acc.s2 = mad(src1.s0, weights_row0.s2, acc.s2); \ + acc.s3 = mad(src1.s1, weights_row0.s2, acc.s3); \ + }) + +/** An optimized direct convolution 3x3 OpenCL kernel for Bifrost architectures when the data type is F32 + * + * @note This OpenCL kernel works only with stride_x and stride_y equal to 1 + * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH + * @note In case biases, -DHAS_BIAS must to be passed at compile + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: 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[out] weights_ptr Pointer to the weights tensor. Supported data types: same as @p weights_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_f32_bifrost( + TENSOR3D_DECLARATION(src), + TENSOR3D_DECLARATION(dst), + TENSOR3D_DECLARATION(weights), +#ifdef HAS_BIAS + VECTOR_DECLARATION(biases), +#endif /* defined(HAS_BIAS) */ + unsigned int weights_stride_w) +{ + // Get the kernel index + const int kernel_index = get_global_id(2); + + Image src = CONVERT_TO_IMAGE_STRUCT(src); + Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst); + + float4 pixels0 = 0; + float4 pixels1 = 0; + float4 pixels2 = 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); + + // Note: Since each work-item computes 4x3 elements, we need to load 5 rows from the input tensor + + for(ushort d = 0; d < (ushort)WEIGHTS_DEPTH; ++d) + { + // Load the weights + float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); + float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); + float3 weights_row2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y)); + float4 src0; + float2 src1; + + // Load values from row0 of input tensor + 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); + + // 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); + + // 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); + + // 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); + + // 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); + + src_addr += src_stride_z; + weights_addr += weights_stride_z; + } + +#ifdef HAS_BIAS + Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); + + float4 bias = (float4) * ((__global float *)(vector_offset(&biases, kernel_index))); + + pixels0 += bias; + pixels1 += bias; + pixels2 += 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)); +} +#endif // defined(WEIGHTS_DEPTH) |