diff options
Diffstat (limited to 'src/core/CL/cl_kernels/tile_helpers.h')
| -rw-r--r-- | src/core/CL/cl_kernels/tile_helpers.h | 62 |
1 files changed, 50 insertions, 12 deletions
diff --git a/src/core/CL/cl_kernels/tile_helpers.h b/src/core/CL/cl_kernels/tile_helpers.h index ecabecb8fb..f36f273e1d 100644 --- a/src/core/CL/cl_kernels/tile_helpers.h +++ b/src/core/CL/cl_kernels/tile_helpers.h @@ -542,8 +542,8 @@ * @param[in] Y Starting Y index * @param[in] X Starting X index * @param[in] C Starting C index - * @param[in] TENSOR_HEIGHT Number of elements to load from Y (height) dimension * @param[in] TENSOR_WIDTH Number of elements to load from X (width) dimension + * @param[in] TENSOR_HEIGHT Number of elements to load from Y (height) dimension * @param[in] STRIDE_Y Stride Y (in bytes) * @param[out] xi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect X coordinate * @param[out] yi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect Y coordinate @@ -563,6 +563,43 @@ }) \ }) +/** Load a tile from global memory (tensor) when the tensor is stored using a NDHWC layout using indirect X, Y and Z coordinates + * + * @param[in] DATA_TYPE Data type + * @param[in] TILE_AREA Number of elements to load from Y (height) dimension * Number of elements to load from X (width) dimension + * @param[in] TILE_CHANNELS Number of elements to load from C (channel) dimension + * @param[in] TENSOR_TYPE Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image). Currently BUFFER only is supported + * In case of cl_image, only TILE_CHANNELS multiples of 4 are supported (4, 8, 16) + * @param[in] TENSOR Tensor basename + * @param[in] B Starting batch index + * @param[in] Z Starting Z index + * @param[in] Y Starting Y index + * @param[in] X Starting X index + * @param[in] C Starting C index + * @param[in] TENSOR_WIDTH Number of elements to load from X (width) dimension + * @param[in] TENSOR_HEIGHT Number of elements to load from Y (height) dimension + * @param[in] TENSOR_DEPTH Number of elements to load from Z (depth) dimension + * @param[in] STRIDE_Y Stride Y (in bytes) + * @param[out] xi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect X coordinate + * @param[out] yi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect Y coordinate + * @param[out] zi A tile with (TILE_WIDTH x TILE_HEIGHT) values with the indirect Z coordinate + * @param[out] dst Output tile + */ +#define T_LOAD_NDHWC_INDIRECT(DATA_TYPE, TILE_AREA, TILE_CHANNELS, TENSOR_TYPE, TENSOR, B, Z, Y, X, C, TENSOR_WIDTH, TENSOR_HEIGHT, TENSOR_DEPTH, STRIDE_Y, xi, yi, zi, dst) \ + ({ \ + LOOP_UNROLLING(int, _i, 0, 1, TILE_AREA, \ + { \ + int _src_y = (X) + xi[_i].v + ((Y) + yi[_i].v) * (TENSOR_WIDTH) + ((Z) + zi[_i].v) * (TENSOR_WIDTH * TENSOR_HEIGHT); \ + _src_y += (B) * (int)(TENSOR_WIDTH) * (int)(TENSOR_HEIGHT) * (int)(TENSOR_DEPTH); \ + int _src_valid_y = (((X) + xi[_i].v) >= 0 && ((X) + xi[_i].v) < (int)(TENSOR_WIDTH) && ((Y) + yi[_i].v) >= 0 && ((Y) + yi[_i].v) < (int)(TENSOR_HEIGHT) \ + && ((Z) + zi[_i].v) >= 0 && ((Z) + zi[_i].v) < (int)(TENSOR_DEPTH)); \ + if(_src_valid_y != 0) \ + { \ + dst[_i].v = V_LOAD(DATA_TYPE, TILE_CHANNELS, TENSOR_TYPE, TENSOR, C, _src_y, STRIDE_Y); \ + } \ + }) \ + }) + /** Store a tile to global memory (tensor) using an indirect Y index tile and conditionally use a different length for the store * * @note If WIDTH1_CONDITION is true, the store will use the WIDTH1 length for the store @@ -588,7 +625,7 @@ LOOP_UNROLLING(int, _i, 0, 1, HEIGHT, \ { \ VSTORE_PARTIAL(WIDTH0, WIDTH1) \ - (src[HEIGHT - 1 - _i].v, 0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (X) * sizeof(DATA_TYPE) + (indirect_y[HEIGHT - 1 - _i].v) * STRIDE_Y)); \ + (CONVERT(src[HEIGHT - 1 - _i].v, VEC_DATA_TYPE(DATA_TYPE, WIDTH0)), 0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (X) * sizeof(DATA_TYPE) + (indirect_y[HEIGHT - 1 - _i].v) * STRIDE_Y)); \ }) \ } \ else \ @@ -596,7 +633,7 @@ LOOP_UNROLLING(int, _i, 0, 1, HEIGHT, \ { \ VSTORE(WIDTH0) \ - (src[HEIGHT - 1 - _i].v, 0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (X) * sizeof(DATA_TYPE) + (indirect_y[HEIGHT - 1 - _i].v) * STRIDE_Y)); \ + (CONVERT(src[HEIGHT - 1 - _i].v, VEC_DATA_TYPE(DATA_TYPE, WIDTH0)), 0, (__global DATA_TYPE *)(TENSOR##_ptr + TENSOR##_offset_first_element_in_bytes + (X) * sizeof(DATA_TYPE) + (indirect_y[HEIGHT - 1 - _i].v) * STRIDE_Y)); \ }) \ } \ }) @@ -892,18 +929,18 @@ * @note Performs: LHS + RHS[broadcasted] = DST * @note Both tiles must have same data type * - * @param[in] DATA_TYPE LHS/RHS/DST data type - * @param[in] M0 Number of LHS rows - * @param[in] N0 Number of LHS columns - * @param[in] lhs LHS tile - * @param[in] rhs RHS tile - * @param[out] dst DST tile + * @param[in] DST_DATA_TYPE DST data type + * @param[in] M0 Number of LHS rows + * @param[in] N0 Number of LHS columns + * @param[in] lhs LHS tile + * @param[in] rhs RHS tile + * @param[out] dst DST tile */ -#define T_ADD_BROADCAST_X(DATA_TYPE, M0, N0, lhs, rhs, dst) \ +#define T_ADD_BROADCAST_X(DST_DATA_TYPE, M0, N0, lhs, rhs, dst) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ - dst[_m0].v = lhs[_m0].v + rhs[0].v; \ + dst[_m0].v = CONVERT(lhs[_m0].v, VEC_DATA_TYPE(DST_DATA_TYPE, N0)) + CONVERT(rhs[0].v, VEC_DATA_TYPE(DST_DATA_TYPE, N0)); \ }) \ }) @@ -926,6 +963,7 @@ #define T_MMUL(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, LHS_LAYOUT, RHS_LAYOUT, lhs, rhs, dst) T_MMUL_##LHS_LAYOUT##_##RHS_LAYOUT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) #define T_MMUL_NT_T(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_##LHS_DATA_TYPE##_##RHS_DATA_TYPE##_##DST_DATA_TYPE(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) #define T_MMUL_NT_T_float_float_float(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_FLOAT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_T_half_half_float(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_FLOAT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) #define T_MMUL_NT_T_half_half_half(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_FLOAT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) #define T_MMUL_NT_T_char_char_int(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_INTEGER8(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) #define T_MMUL_NT_T_uchar_uchar_uint(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_INTEGER8(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) @@ -938,7 +976,7 @@ { \ LOOP_UNROLLING(int, _k, 0, 1, K0, \ { \ - dst[_m].s[_n] = fma((lhs[_m].s[_k]), (rhs[_n].s[_k]), dst[_m].s[_n]); \ + dst[_m].s[_n] = fma((DST_DATA_TYPE)(lhs[_m].s[_k]), (DST_DATA_TYPE)(rhs[_n].s[_k]), dst[_m].s[_n]); \ }) \ }) \ }) \ |