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author | Gian Marco Iodice <gianmarco.iodice@arm.com> | 2019-06-24 14:40:30 +0100 |
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committer | Gian Marco Iodice <gianmarco.iodice@arm.com> | 2019-06-24 15:56:10 +0000 |
commit | 944170e1591ff23c9e6ede2201f0f6aba0f3439b (patch) | |
tree | 64d6b718c01458be04ca1b39c39704b78ce3b5d6 /src | |
parent | 65383e21a5b82071229c6322bf65c47e3719b490 (diff) | |
download | ComputeLibrary-944170e1591ff23c9e6ede2201f0f6aba0f3439b.tar.gz |
COMPMID-2172: Fuse bias addition with CLGEMMMatrixMultiplyNativeKernel
Change-Id: I714b92ec001fc71172719b67fb66d490538b6948
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1399
Reviewed-by: Giuseppe Rossini <giuseppe.rossini@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src')
-rw-r--r-- | src/core/CL/cl_kernels/gemm.cl | 101 | ||||
-rw-r--r-- | src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp | 94 |
2 files changed, 154 insertions, 41 deletions
diff --git a/src/core/CL/cl_kernels/gemm.cl b/src/core/CL/cl_kernels/gemm.cl index 7ada14c774..854d0092d9 100644 --- a/src/core/CL/cl_kernels/gemm.cl +++ b/src/core/CL/cl_kernels/gemm.cl @@ -2122,35 +2122,49 @@ __kernel void gemm_mm_reshaped_lhs_nt_rhs_t(IMAGE_DECLARATION(lhs), * -# DEPTH_GEMM3D: The depth of the output in case it has to be reinterpreted as a 3D tensor * (HEIGHT_GEMM3D * DEPTH_GEMM3D) = columns LHS matrix * - * @param[in] lhs_ptr Pointer to the LHS reshaped matrix. Supported data type: F16/F32 - * @param[in] lhs_stride_x Stride of the LHS reshaped matrix in X dimension (in bytes) - * @param[in] lhs_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] lhs_stride_y Stride of the LHS reshaped matrix in Y dimension (in bytes) - * @param[in] lhs_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the LHS reshaped matrix - * @param[in] rhs_ptr Pointer to the RHS reshaped matrix. Supported data type: same as @p lhs_ptr - * @param[in] rhs_stride_x Stride of the RHS reshaped matrix in X dimension (in bytes) - * @param[in] rhs_step_x src_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] rhs_stride_y Stride of the RHS reshaped matrix in Y dimension (in bytes) - * @param[in] rhs_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the RHS reshaped matrix - * @param[out] dst_ptr Pointer to the destination matrix Supported data type: same as @p lhs_ptr - * @param[in] dst_stride_x Stride of the destination matrix 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 matrix 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 matrix - * @param[in] lhs_stride_z Stride of the LHS reshaped matrix in Z dimension (in bytes) - * @param[in] rhs_stride_z Stride of the RHS reshaped matrix in Z dimension (in bytes) - * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) - * @param[in] lhs_cross_plane_pad (Optional) Bottom paddings for LHS matrix in unit of elements (only if defined REINTERPRET_INPUT_AS_3D) - * @param[in] dst_cross_plane_pad (Optional) Bottom paddings for the output matrix in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D) + * @param[in] lhs_ptr Pointer to the LHS matrix. Supported data type: F16/F32 + * @param[in] lhs_stride_x Stride of the LHS matrix in X dimension (in bytes) + * @param[in] lhs_step_x lhs_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] lhs_stride_y Stride of the LHS matrix in Y dimension (in bytes) + * @param[in] lhs_step_y lhs_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the LHS matrix + * @param[in] rhs_ptr Pointer to the RHS matrix. Supported data type: same as @p lhs_ptr + * @param[in] rhs_stride_x Stride of the RHS matrix in X dimension (in bytes) + * @param[in] rhs_step_x rhs_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] rhs_stride_y Stride of the RHS matrix in Y dimension (in bytes) + * @param[in] rhs_step_y rhs_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the RHS matrix + * @param[in] bias_ptr (Optional)Pointer to the bias reshaped matrix. Supported data type: same as @p lhs_ptr + * @param[in] bias_ptr (Optional) Pointer to the bias matrix. Supported data type: same as @p lhs_ptr + * @param[in] bias_stride_x (Optional) Stride of the bias matrix in X dimension (in bytes) + * @param[in] bias_step_x (Optional) bias_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] bias_stride_y (Optional) Stride of the bias matrix in Y dimension (in bytes) + * @param[in] bias_step_y (Optional) bias_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias matrix + * @param[out] dst_ptr Pointer to the destination matrix Supported data type: same as @p lhs_ptr + * @param[in] dst_stride_x Stride of the destination matrix 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 matrix 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 matrix + * @param[in] lhs_stride_z Stride of the LHS matrix in Z dimension (in bytes) + * @param[in] rhs_stride_z Stride of the RHS matrix in Z dimension (in bytes) + * @param[in] bias_stride_z (Optional) Stride of the bias matrix in Z dimension (in bytes) + * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) + * @param[in] lhs_cross_plane_pad (Optional) Bottom paddings for LHS matrix in unit of elements (only if defined REINTERPRET_INPUT_AS_3D) + * @param[in] dst_cross_plane_pad (Optional) Bottom paddings for the output matrix in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D) */ __kernel void gemm_mm_native(IMAGE_DECLARATION(lhs), IMAGE_DECLARATION(rhs), +#if defined(BETA) + IMAGE_DECLARATION(bias), +#endif // defined(BETA) IMAGE_DECLARATION(dst), uint lhs_stride_z, uint rhs_stride_z, +#if defined(BETA) + uint bias_stride_z, +#endif //defined(BETA) uint dst_stride_z #if defined(REINTERPRET_INPUT_AS_3D) , @@ -2192,8 +2206,8 @@ __kernel void gemm_mm_native(IMAGE_DECLARATION(lhs), rhs_offset += z * rhs_stride_z; #endif // defined(MATRIX_B_DEPTH) - REPEAT_VAR_INIT_TO_CONST(8, uint, zlhs, 0); //uint zlhs0=0,zlhs1=0,zlhs2=0,... zlhs7=0; - REPEAT_VAR_INIT_TO_CONST(16, uint, zrhs, 0); + REPEAT_VAR_INIT_TO_CONST(M0, uint, zlhs, 0); + REPEAT_VAR_INIT_TO_CONST(16, uint, zero, 0); #if defined(REINTERPRET_INPUT_AS_3D) // The plane (zlhs) is calculated dividing M (y * M0) by HEIGHT_GEMM3D @@ -2211,7 +2225,7 @@ __kernel void gemm_mm_native(IMAGE_DECLARATION(lhs), #endif // defined(REINTERPRET_INPUT_AS_3D) // Initialize the accumulators - REPEAT_VAR_INIT_TO_CONST(M0, VEC_DATA_TYPE(DATA_TYPE, N0), c, 0); //VEC_DATA_TYPE(DATA_TYPE, N0) c0=0,c1=0,c2=0,... c(N0-1)=0; + REPEAT_VAR_INIT_TO_CONST(M0, VEC_DATA_TYPE(DATA_TYPE, N0), c, 0); //VEC_DATA_TYPE(DATA_TYPE, N0) c0=0,c1=0,c2=0,... c(M0-1)=0; int i = 0; for(; i <= (K - K0); i += K0) @@ -2229,7 +2243,7 @@ __kernel void gemm_mm_native(IMAGE_DECLARATION(lhs), LOAD_BLOCK(M0, K0, DATA_TYPE, a, lhs_ptr, lhs_offset, lhs_stride_y, zlhs); // Load values from RHS matrix - LOAD_BLOCK(K0, N0, DATA_TYPE, b, rhs_ptr, rhs_offset, rhs_stride_y, zrhs); + LOAD_BLOCK(K0, N0, DATA_TYPE, b, rhs_ptr, rhs_offset, rhs_stride_y, zero); RHS_VFMA_M0xN0(0, a, b0, c); RHS_VFMA_M0xN0(1, a, b1, c); @@ -2305,7 +2319,7 @@ __kernel void gemm_mm_native(IMAGE_DECLARATION(lhs), __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + (x * (uint)N0 * sizeof(DATA_TYPE)) + (y * (uint)M0 * dst_stride_y); - REPEAT_VAR_INIT_TO_CONST(8, uint, zout, 0); //uint zout0=0,zout1=0,zout2=0,... zout7=0; + REPEAT_VAR_INIT_TO_CONST(M0, uint, zout, 0); #if defined(REINTERPRET_OUTPUT_AS_3D) // The plane (zout) is calculated dividing M (y * M0) by HEIGHT_GEMM3D @@ -2323,11 +2337,40 @@ __kernel void gemm_mm_native(IMAGE_DECLARATION(lhs), #endif // defined(REINTERPRET_OUTPUT_AS_3D) // Multiply by the weight of matrix-matrix product and store the result - // Multiply by the weight of matrix-matrix product and store the result #if defined(ALPHA) SCALE_BLOCK(M0, DATA_TYPE, c, ALPHA); #endif // defined(ALPHA) + // Add beta*bias +#if defined(BETA) +#if defined(BROADCAST_BIAS) + __global uchar *bias_addr = bias_ptr + bias_offset_first_element_in_bytes + (get_global_id(0) * (uint)N0 * sizeof(DATA_TYPE)); + + LOAD_BLOCK(1, N0, DATA_TYPE, bias, bias_addr, 0, bias_stride_y, zero); + +#ifndef UNIT_BETA + SCALE_BLOCK(1, DATA_TYPE, bias, BETA); +#endif // UNIT_BIAS + + // c = c + bias[broadcasted] + ADD_BLOCK_BROADCAST(M0, c, bias0); + +#else // defined(BROADCAST_BIAS) + __global uchar *bias_addr = bias_ptr + bias_offset_first_element_in_bytes + (get_global_id(0) * (uint)N0 * sizeof(DATA_TYPE)) + (get_global_id(1) * (uint)M0 * bias_stride_y) + get_global_id( + 2) * bias_stride_z; + + LOAD_BLOCK(M0, N0, DATA_TYPE, bias, bias_addr, 0, bias_stride_y, zero); + +#ifndef UNIT_BETA + SCALE_BLOCK(M0, DATA_TYPE, bias, BETA); +#endif // UNIT_BIAS + + // c = c + bias + ADD_BLOCK(M0, c, bias); + +#endif // defined(BROADCAST_BIAS) +#endif // defined(BETA) + // Store output block STORE_BLOCK(M0, N0, DATA_TYPE, c, dst_addr, dst_stride_y, zout); diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp index a3de6e0853..0b9359e610 100644 --- a/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp +++ b/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp @@ -51,7 +51,8 @@ namespace { using ElementsProcessed = Steps; -Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, +Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, const GEMMLHSMatrixInfo &lhs_info, + const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info) { ARM_COMPUTE_UNUSED(alpha); @@ -85,6 +86,22 @@ Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != static_cast<unsigned int>(m)); } + if(input2 != nullptr && !(helpers::float_ops::is_zero(beta))) + { + const int input2_dim0 = static_cast<int>(input2->dimension(0)); + const int input2_dim1 = static_cast<int>(input2->dimension(1)); + + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input2, input1); + if(gemm_info.broadcast_bias()) + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim1 != 1 || input2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted"); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG((input2_dim0 != n || input2_dim1 != m), "Incorrect dimension of bias matrix"); + } + } + if(output->total_size() != 0) { const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)); @@ -95,7 +112,8 @@ Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, return Status{}; } -std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, +std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, + const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info, ElementsProcessed &num_elements_processed) { unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0]; @@ -150,8 +168,24 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration_x), output->dimension(1) + bottom_pad); - window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop - update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor + if(input2 != nullptr) + { + const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x; + + const int bias_processed_per_iteration_y = gemm_info.broadcast_bias() ? 1 : num_elems_processed_per_iteration_y; + + AccessWindowStatic input2_access(input2, 0, 0, + ceil_to_multiple(input2->dimension(0), bias_processed_per_iteration_x), + ceil_to_multiple(input2->dimension(1), bias_processed_per_iteration_y)); + + window_changed = update_window_and_padding(win, input0_access, input1_access, input2_access) || // window used by the execute_window_loop + update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor + } + else + { + window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop + update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor + } output_access.set_valid_region(win_out, ValidRegion(Coordinates(), output->tensor_shape())); @@ -167,23 +201,28 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITe } // namespace CLGEMMMatrixMultiplyNativeKernel::CLGEMMMatrixMultiplyNativeKernel() - : _input0(nullptr), _input1(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false), _use_dummy_work_items(false) + : _input0(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false), _use_dummy_work_items(false), + _add_bias(false), _broadcast_bias(false) { } -void CLGEMMMatrixMultiplyNativeKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, float alpha, const GEMMLHSMatrixInfo &lhs_info, +void CLGEMMMatrixMultiplyNativeKernel::configure(const ICLTensor *input0, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, float alpha, float beta, + const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), alpha, lhs_info, rhs_info, gemm_info)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), (input2 != nullptr ? input2->info() : nullptr), output->info(), alpha, beta, lhs_info, rhs_info, gemm_info)); _input0 = input0; _input1 = input1; + _input2 = helpers::float_ops::is_zero(beta) ? nullptr : input2; _output = output; _reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); _reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0); _use_dummy_work_items = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device()); + _add_bias = _input2 != nullptr; + _broadcast_bias = gemm_info.broadcast_bias(); // In case both input and output have to be reinterpreted as 3D tensors, // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. @@ -200,7 +239,7 @@ void CLGEMMMatrixMultiplyNativeKernel::configure(const ICLTensor *input0, const ElementsProcessed num_elements_processed{}; // Configure kernel window - auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed); + auto win_config = validate_and_configure_window(input0->info(), input1->info(), input2 != nullptr ? input2->info() : nullptr, output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); @@ -208,6 +247,9 @@ void CLGEMMMatrixMultiplyNativeKernel::configure(const ICLTensor *input0, const CLBuildOptions build_opts; build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input0->info()->data_type())); build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha)); + build_opts.add_option_if(_input2 != nullptr, "-DBETA=" + float_to_string_with_full_precision(beta)); + build_opts.add_option_if(helpers::float_ops::is_one(beta), "-DUNIT_BETA"); + build_opts.add_option_if(gemm_info.broadcast_bias(), "-DBROADCAST_BIAS"); build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D"); build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D"); build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1))); @@ -229,6 +271,8 @@ void CLGEMMMatrixMultiplyNativeKernel::configure(const ICLTensor *input0, const // Set config_id for enabling LWS tuning _config_id = kernel_name; _config_id += "_"; + _config_id += (_add_bias ? "add_bias_" : ""); + _config_id += (_broadcast_bias ? "broadcast_bias_" : ""); _config_id += (_reinterpret_input_as_3d ? "3di_" : ""); _config_id += (_reinterpret_output_as_3d ? "3do_" : ""); _config_id += lower_string(string_from_data_type(input0->info()->data_type())); @@ -248,13 +292,15 @@ void CLGEMMMatrixMultiplyNativeKernel::configure(const ICLTensor *input0, const _config_id += support::cpp11::to_string(rhs_info.k0); } -Status CLGEMMMatrixMultiplyNativeKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, const GEMMLHSMatrixInfo &lhs_info, +Status CLGEMMMatrixMultiplyNativeKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float alpha, float beta, + const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info) { ElementsProcessed num_elements_processed{}; - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, alpha, lhs_info, rhs_info, gemm_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, input2, output, alpha, beta, lhs_info, rhs_info, gemm_info)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), input1->clone().get(), + input2 != nullptr ? input2->clone().get() : nullptr, output->clone().get(), lhs_info, rhs_info, @@ -285,7 +331,15 @@ void CLGEMMMatrixMultiplyNativeKernel::run(const Window &window, cl::CommandQueu if(_reinterpret_input_as_3d) { // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor - const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3; + unsigned int idx0; + if(_add_bias) + { + idx0 = 4 * num_arguments_per_2D_tensor() + 4; + } + else + { + idx0 = 3 * num_arguments_per_2D_tensor() + 3; + } const unsigned int total_cross_plane_pad = _input0->info()->padding().top + _input0->info()->padding().bottom; _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad)); } @@ -293,7 +347,15 @@ void CLGEMMMatrixMultiplyNativeKernel::run(const Window &window, cl::CommandQueu if(_reinterpret_output_as_3d) { // Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor - const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0); + unsigned int idx0; + if(_add_bias) + { + idx0 = 4 * num_arguments_per_2D_tensor() + 4 + (_reinterpret_input_as_3d ? 1 : 0); + } + else + { + idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0); + } const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom; _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad)); } @@ -311,9 +373,17 @@ void CLGEMMMatrixMultiplyNativeKernel::run(const Window &window, cl::CommandQueu unsigned int idx = 0; add_2D_tensor_argument(idx, _input0, slice); add_2D_tensor_argument(idx, _input1, slice_b); + if(_add_bias) + { + add_2D_tensor_argument(idx, _input2, slice); + } add_2D_tensor_argument(idx, _output, slice); _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2])); _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2])); + if(_add_bias) + { + _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input2->info()->strides_in_bytes()[2])); + } _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2])); enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items); } |