From b54ba2848515bf0aee0619c760518481f58c7525 Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Tue, 14 Jan 2020 15:31:55 +0000 Subject: COMPMID-2847: Fuse output stage in GEMMLowpMatrixMultiplyReshapedOnlyRHS Change-Id: Icd60eb368a34295434e8c141885b4666973a92a1 Signed-off-by: Michele Di Giorgio Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2732 Tested-by: Arm Jenkins Reviewed-by: Georgios Pinitas Reviewed-by: Gian Marco Iodice Comments-Addressed: Arm Jenkins --- ...GEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp | 311 ++++++++++++++++++--- 1 file changed, 277 insertions(+), 34 deletions(-) (limited to 'src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp') diff --git a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp index 3fa2fad8fd..c4ed691f2e 100644 --- a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp +++ b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019 ARM Limited. + * Copyright (c) 2019-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -50,21 +50,27 @@ namespace { using ElementsProcessed = Steps; -Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, - const GEMMReshapeInfo &gemm_info) +Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMKernelInfo &gemm_info, + const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, + const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1); ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3"); + + const GEMMRHSMatrixInfo rhs_info = gemm_info.rhs_info; + const GEMMLHSMatrixInfo lhs_info = gemm_info.lhs_info; + const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage; + ARM_COMPUTE_RETURN_ERROR_ON_MSG((((rhs_info.k0 & (rhs_info.k0 - 1)) && rhs_info.k0 != 3) || (rhs_info.k0 > 16)), "Only 2,3,4,8,16 are supported for k0"); ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 1 || lhs_info.m0 > 8); ARM_COMPUTE_RETURN_ERROR_ON_MSG((((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3) || rhs_info.n0 > 16), "Only 2,3,4,8,16 are supported for n0"); - const int m = gemm_info.m(); - const int n = gemm_info.n(); - const int k = gemm_info.k(); + const int m = gemm_info.m; + const int n = gemm_info.n; + const int k = gemm_info.k; TensorShape tensor_shape1{ input1->tensor_shape() }; tensor_shape1.set(0, n); @@ -74,7 +80,7 @@ Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info)); ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != static_cast(k)); - if(gemm_info.reinterpret_input_as_3d()) + if(gemm_info.reinterpret_input_as_3d) { ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) * input0->dimension(2) != static_cast(m)); } @@ -84,23 +90,118 @@ Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, } ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1); + const TensorShape expected_output_shape = compute_mm_shape(*input0, *input1, gemm_info); if(output->total_size() != 0) { - const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)); + const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(expected_output_shape); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); + if(output_stage.type == GEMMLowpOutputStageType::NONE) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output); + } + } + + if(bias != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON(expected_output_shape[0] != bias->dimension(0)); } + ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN) || (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT), + "Only GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT is supported"); + + // Checks performed if the output stage needs to be fused + if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) + { + // If a_offset == 0, vector_sum_col can be a nullptr + if(gemm_info.a_offset != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != expected_output_shape[0]); + } + + // If b_offset == 0, vector_sum_row can be a nullptr + if(gemm_info.b_offset != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32); + + // Check if mm result is a 3D reinterpretation + const bool reinterpret_as_3d = expected_output_shape.num_dimensions() > 1 && expected_output_shape.y() != vector_sum_row->tensor_shape().x(); + + // Validate input + ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (expected_output_shape[1] * expected_output_shape[2])); + ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != expected_output_shape[1]); + + if(expected_output_shape.num_dimensions() > 1) + { + const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2; + + TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape(); + vector_sum_row_shape.collapse_from(1); + TensorShape collapsed_output_shape(expected_output_shape); + collapsed_output_shape.collapse_from(output_batch_idx); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != collapsed_output_shape[output_batch_idx], + "vector_sum_row must have the same number of batches of output tensor"); + + if(gemm_info.a_offset != 0) + { + TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape(); + vector_sum_col_shape.collapse_from(1); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1], + "vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1"); + } + } + } + + PixelValue min_val{}; + PixelValue max_val{}; + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON(output_stage.output_data_type != output->data_type()); + std::tie(min_val, max_val) = get_min_max(output->data_type()); + ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_max_bound > max_val.get()); + ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound < min_val.get() || output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound); + } + else + { + std::tie(min_val, max_val) = get_min_max(output_stage.output_data_type); + ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_max_bound > max_val.get()); + ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound < min_val.get() || output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound); + } + + if(output_multipliers != nullptr && output_shifts != nullptr) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_multipliers, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_shifts, 1, DataType::S32); + ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1); + if(output_stage.is_quantized_per_channel) + { + ARM_COMPUTE_RETURN_ERROR_ON(expected_output_shape[0] != output_shifts->dimension(0)); + ARM_COMPUTE_RETURN_ERROR_ON(expected_output_shape[0] != output_multipliers->dimension(0)); + } + } + } return Status{}; } -std::pair validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, - const GEMMReshapeInfo &gemm_info, ElementsProcessed &num_elements_processed) +std::pair validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, const GEMMKernelInfo &gemm_info, + ITensorInfo *vector_sum_col, ITensorInfo *vector_sum_row, ITensorInfo *bias, + ITensorInfo *output_multipliers, ITensorInfo *output_shifts, ElementsProcessed &num_elements_processed) { + const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage; + unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0]; unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1]; - bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - bool reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0); + bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d; + bool reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d != 0); Window win{}; Window win_out{}; @@ -114,7 +215,15 @@ std::pair validate_and_configure_window(ITensorInfo *input0, ITe } // Output tensor auto initialization if not yet initialized - auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)).set_data_type(DataType::S32)); + const TensorShape expected_output_shape = compute_mm_shape(*input0, *input1, gemm_info); + if(output_stage.type != GEMMLowpOutputStageType::NONE) + { + auto_init_if_empty(*output, input0->clone()->set_tensor_shape(expected_output_shape).set_data_type(output_stage.output_data_type)); + } + else + { + auto_init_if_empty(*output, input0->clone()->set_tensor_shape(expected_output_shape).set_data_type(DataType::S32)); + } TensorInfo tmp_info(*output); @@ -128,19 +237,19 @@ std::pair validate_and_configure_window(ITensorInfo *input0, ITe } // Configure kernel window - num_elems_processed_per_iteration_x = rhs_info.n0; - num_elems_processed_per_iteration_y = lhs_info.m0; + num_elems_processed_per_iteration_x = gemm_info.rhs_info.n0; + num_elems_processed_per_iteration_y = gemm_info.lhs_info.m0; // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic - const int m = reinterpret_output_as_3d ? gemm_info.m() : input0->dimension(1); + const int m = reinterpret_output_as_3d ? gemm_info.m : input0->dimension(1); const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y; win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); AccessWindowStatic input0_access(input0, 0, 0, - ceil_to_multiple(input0->dimension(0), lhs_info.k0), + ceil_to_multiple(input0->dimension(0), gemm_info.lhs_info.k0), input0->dimension(1) + bottom_pad); AccessWindowStatic input1_access(input1, 0, 0, input1->dimension(0), @@ -152,6 +261,30 @@ std::pair validate_and_configure_window(ITensorInfo *input0, ITe 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(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) + { + if(gemm_info.a_offset != 0) + { + AccessWindowHorizontal vector_sum_col_access(vector_sum_col, 0, num_elems_processed_per_iteration_x); + window_changed = window_changed || update_window_and_padding(win_out, vector_sum_col_access); + } + // No access window needed for vector_sum_row + ARM_COMPUTE_UNUSED(vector_sum_row); + + if(bias != nullptr) + { + AccessWindowHorizontal bias_access(bias, 0, num_elems_processed_per_iteration_x); + window_changed = window_changed || update_window_and_padding(win_out, bias_access); + } + + if(output_multipliers != nullptr && output_multipliers->dimension(0) > 1) + { + AccessWindowHorizontal output_multipliers_access(output_multipliers, 0, num_elems_processed_per_iteration_x); + AccessWindowHorizontal output_shifts_access(output_shifts, 0, num_elems_processed_per_iteration_x); + window_changed = window_changed || update_window_and_padding(win_out, output_multipliers_access, output_shifts_access); + } + } + output_access.set_valid_region(win_out, ValidRegion(Coordinates(), output->tensor_shape())); // Collapse along the Z direction @@ -166,23 +299,56 @@ std::pair validate_and_configure_window(ITensorInfo *input0, ITe } // namespace CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel() - : _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), + _output(nullptr), + _vector_sum_col(nullptr), + _vector_sum_row(nullptr), + _bias(nullptr), + _output_multipliers(nullptr), + _output_shifts(nullptr), + _slide_matrix_b(true), + _reinterpret_input_as_3d(false), + _reinterpret_output_as_3d(false), + _use_dummy_work_items(false), + _is_quantized_per_channel(false), + _fuse_output_stage(false) { } -void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, - const GEMMReshapeInfo &gemm_info) +void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMKernelInfo &gemm_info, + const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, const ICLTensor *bias, + const ICLTensor *output_multipliers, const ICLTensor *output_shifts) { ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); - - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), lhs_info, rhs_info, gemm_info)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), + input1->info(), + output->info(), + gemm_info, + vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, + vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, + bias != nullptr ? bias->info() : nullptr, + output_multipliers != nullptr ? output_multipliers->info() : nullptr, + output_shifts != nullptr ? output_shifts->info() : nullptr)); + + const GEMMRHSMatrixInfo rhs_info = gemm_info.rhs_info; + const GEMMLHSMatrixInfo lhs_info = gemm_info.lhs_info; + const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage; + const int32_t a_offset = gemm_info.a_offset; + const int32_t b_offset = gemm_info.b_offset; _input0 = input0; _input1 = input1; _output = output; - _reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); - _reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0); + _vector_sum_col = vector_sum_col; + _vector_sum_row = vector_sum_row; + _bias = bias; + _output_multipliers = output_multipliers; + _output_shifts = output_shifts; + _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()); + _is_quantized_per_channel = output_stage.is_quantized_per_channel; // 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. @@ -199,7 +365,16 @@ void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::configure(const ICLTensor *i 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(), + output->info(), + gemm_info, + vector_sum_col != nullptr ? vector_sum_col->info() : nullptr, + vector_sum_row != nullptr ? vector_sum_row->info() : nullptr, + bias != nullptr ? bias->info() : nullptr, + output_multipliers != nullptr ? output_multipliers->info() : nullptr, + output_shifts != nullptr ? output_shifts->info() : nullptr, + num_elements_processed); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); @@ -213,8 +388,8 @@ void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::configure(const ICLTensor *i build_opts.add_option_if(rhs_info.interleave, "-DRHS_INTERLEAVE"); build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS"); build_opts.add_option("-DM=" + support::cpp11::to_string(input0->info()->dimension(1))); - build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n())); - build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k())); + build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n)); + build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k)); build_opts.add_option("-DM0=" + support::cpp11::to_string(lhs_info.m0)); build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0)); build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0)); @@ -225,6 +400,35 @@ void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::configure(const ICLTensor *i std::string kernel_name("gemmlowp_mm_reshaped_only_rhs_"); kernel_name += rhs_info.transpose ? "t" : "nt"; + if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) + { + kernel_name += "_fused_output_stage_fixedpoint"; + _fuse_output_stage = true; + // If a_offset == 0, vector_sum_col can be a nullptr + if(a_offset != 0) + { + build_opts.add_option("-DA_OFFSET=" + support::cpp11::to_string(a_offset)); + build_opts.add_option_if(vector_sum_col->info()->tensor_shape().num_dimensions() > 1, "-DSUM_COL_HAS_BATCHES"); + } + // If b_offset == 0, vector_sum_row can be a nullptr + build_opts.add_option_if(b_offset != 0, "-DB_OFFSET=" + support::cpp11::to_string(b_offset)); + build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(a_offset * b_offset * input0->info()->dimension(0))); + build_opts.add_option_if(bias != nullptr, "-DADD_BIAS"); + build_opts.add_option("-DRESULT_OFFSET=" + support::cpp11::to_string(output_stage.gemmlowp_offset)); + build_opts.add_option("-DRESULT_MULTIPLIER=" + support::cpp11::to_string(output_stage.gemmlowp_multipliers[0])); + build_opts.add_option("-DRESULT_SHIFT=" + support::cpp11::to_string(output_stage.gemmlowp_shifts[0])); + build_opts.add_option_if(_is_quantized_per_channel, "-DPER_CHANNEL_QUANTIZATION"); + + const int min = output_stage.gemmlowp_min_bound; + const int max = output_stage.gemmlowp_max_bound; + + PixelValue min_val{}; + PixelValue max_val{}; + std::tie(min_val, max_val) = get_min_max(output->info()->data_type()); + build_opts.add_option_if((min != min_val.get()) && (min != max), "-DMIN_BOUND=" + support::cpp11::to_string(min)); + build_opts.add_option_if((max != max_val.get()) && (min != max), "-DMAX_BOUND=" + support::cpp11::to_string(max)); + } + // Create kernel _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); @@ -239,7 +443,7 @@ void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::configure(const ICLTensor *i _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(0)); _config_id += "_"; - _config_id += support::cpp11::to_string(gemm_info.k()); + _config_id += support::cpp11::to_string(gemm_info.k); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(2)); _config_id += "_"; @@ -254,17 +458,21 @@ void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::configure(const ICLTensor *i _config_id += support::cpp11::to_string(rhs_info.interleave); } -Status CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, - const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info) +Status CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMKernelInfo &gemm_info, + const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, + const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts) { ElementsProcessed num_elements_processed{}; - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, lhs_info, rhs_info, gemm_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), input1->clone().get(), output->clone().get(), - lhs_info, - rhs_info, gemm_info, + vector_sum_col != nullptr ? vector_sum_col->clone().get() : nullptr, + vector_sum_row != nullptr ? vector_sum_row->clone().get() : nullptr, + bias != nullptr ? bias->clone().get() : nullptr, + output_multipliers != nullptr ? output_multipliers->clone().get() : nullptr, + output_shifts != nullptr ? output_shifts->clone().get() : nullptr, num_elements_processed) .first); @@ -304,6 +512,21 @@ void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::run(const Window &window, cl _kernel.setArg(idx0, static_cast(total_cross_plane_pad)); } + // Set window for vector_sum_col + Window win_vector_sum_col = slice; + win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0)); + win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0)); + + // Set window for vector_sum_row + Window win_vector_sum_row = slice; + win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0)); + win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0)); + win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0)); + + Window biases_slice = slice; + biases_slice.set(Window::DimY, Window::Dimension(0, 1, 1)); + biases_slice.set(Window::DimZ, Window::Dimension(0, 1, 1)); + do { Window slice_b = slice; @@ -321,6 +544,26 @@ void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::run(const Window &window, cl _kernel.setArg(idx++, static_cast(_input0->info()->strides_in_bytes()[2])); _kernel.setArg(idx++, static_cast(_input1->info()->strides_in_bytes()[2])); _kernel.setArg(idx++, static_cast(_output->info()->strides_in_bytes()[2])); + if(_reinterpret_input_as_3d) + { + // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor + idx++; + } + + if(_reinterpret_output_as_3d) + { + // Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor + idx++; + } + + if(_fuse_output_stage) + { + add_2D_tensor_argument_if((_vector_sum_col != nullptr), idx, _vector_sum_col, win_vector_sum_col); + add_2D_tensor_argument_if((_vector_sum_row != nullptr), idx, _vector_sum_row, win_vector_sum_row); + add_1D_tensor_argument_if((_bias != nullptr), idx, _bias, biases_slice); + add_1D_tensor_argument_if(_is_quantized_per_channel, idx, _output_multipliers, biases_slice); + add_1D_tensor_argument_if(_is_quantized_per_channel, idx, _output_shifts, biases_slice); + } enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items); } while(window.slide_window_slice_3D(slice)); -- cgit v1.2.1