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-rw-r--r--src/gpu/cl/ClKernelLibrary.cpp14
-rw-r--r--src/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp538
-rw-r--r--src/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h88
-rw-r--r--src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp2
-rw-r--r--src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h2
-rw-r--r--src/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.cpp2
-rw-r--r--src/gpu/cl/operators/ClFullyConnected.h2
-rw-r--r--src/gpu/cl/operators/ClGemm.cpp219
-rw-r--r--src/gpu/cl/operators/ClGemm.h19
-rw-r--r--src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp1
10 files changed, 65 insertions, 822 deletions
diff --git a/src/gpu/cl/ClKernelLibrary.cpp b/src/gpu/cl/ClKernelLibrary.cpp
index 4af42262b9..9d524f936e 100644
--- a/src/gpu/cl/ClKernelLibrary.cpp
+++ b/src/gpu/cl/ClKernelLibrary.cpp
@@ -271,16 +271,6 @@ const std::map<std::string, std::string> ClKernelLibrary::_kernel_program_map =
{ "gemm_ma_f32", "common/gemm.cl" },
{ "gemm_mv", "common/gemv.cl" },
{ "gemm_mv_quantized", "common/gemv.cl" },
- { "gemm_mm_interleaved_transposed_f16", "common/gemm_v1.cl" },
- { "gemm_mm_interleaved_transposed_f16_acc32", "common/gemm_v1.cl" },
- { "gemm_mm_interleaved_transposed_f16_bifrost", "common/gemm_v1.cl" },
- { "gemm_mm_interleaved_transposed_f32", "common/gemm_v1.cl" },
- { "gemm_mm_interleaved_transposed_f32_bifrost", "common/gemm_v1.cl" },
- { "gemm_mm_floating_point", "common/gemm_v1.cl" },
- { "gemm_mm_floating_point_f16_bifrost", "common/gemm_v1.cl" },
- { "gemm_mm_floating_point_f16_bifrost_acc32", "common/gemm_v1.cl" },
- { "gemm_mm_floating_point_f32_bifrost", "common/gemm_v1.cl" },
- { "gemm_mm_floating_point_f32_bifrost_1000", "common/gemm_v1.cl" },
{ "gemm_mm_native", "common/gemm.cl" },
{ "gemm_mm_reshaped_lhs_nt_rhs_t", "common/gemm.cl" },
{ "gemm_mm_reshaped_lhs_nt_rhs_t_texture", "common/gemm.cl" },
@@ -591,10 +581,6 @@ const std::map<std::string, std::string> ClKernelLibrary::_program_source_map =
#include "./cl_kernels/common/gemm.clembed"
},
{
- "common/gemm_v1.cl",
-#include "./cl_kernels/common/gemm_v1.clembed"
- },
- {
"common/gemmlowp.cl",
#include "./cl_kernels/common/gemmlowp.clembed"
},
diff --git a/src/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp b/src/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp
deleted file mode 100644
index 4e934f0f33..0000000000
--- a/src/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.cpp
+++ /dev/null
@@ -1,538 +0,0 @@
-/*
- * Copyright (c) 2017-2021 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "src/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h"
-
-#include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLKernelLibrary.h"
-#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/CL/OpenCL.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/TensorInfo.h"
-#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "src/core/AccessWindowStatic.h"
-#include "src/core/CL/CLValidate.h"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/helpers/WindowHelpers.h"
-#include "src/core/utils/helpers/float_ops.h"
-#include "support/Cast.h"
-#include "support/StringSupport.h"
-
-namespace arm_compute
-{
-namespace opencl
-{
-namespace kernels
-{
-namespace
-{
-using ElementsProcessed = Steps;
-
-inline Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float beta,
- bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision)
-{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst);
- ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src0);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((fp_mixed_precision && (src0->data_type() != DataType::F16)), "Mixed precision floating point is supported only for F16 data");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the matrix B must be <= 3");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_interleaved_transposed && reshape_info.reinterpret_input_as_3d(), "The input tensor cannot be reinterpreted as 3D if is_interleaved_transposed is true");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 2 && reshape_info.reinterpret_input_as_3d(), "The src1 tensor cannot have more than 2 dimensions if src0 has to be reinterpreted as 3D");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((reshape_info.reinterpret_input_as_3d() || reshape_info.depth_output_gemm3d() != 0) && (src2 != nullptr)
- && (!reshape_info.broadcast_bias()),
- "Bias addition only supported with broadcast mode in case the input or dst has to be reinterpreted as 3D");
-
- if(!is_interleaved_transposed)
- {
- ARM_COMPUTE_RETURN_ERROR_ON(src0->dimension(0) != src1->dimension(1));
-
- if(src2 != nullptr && !(helpers::float_ops::is_zero(beta)))
- {
- const unsigned int m = reshape_info.reinterpret_input_as_3d() ? src0->dimension(1) * src0->dimension(2) : src0->dimension(1);
- const unsigned int n = src1->dimension(0);
- const unsigned int src2_dim0 = src2->dimension(0);
- const unsigned int src2_dim1 = src2->dimension(1);
-
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1);
- if(reshape_info.broadcast_bias())
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
- }
- else
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix");
- }
- }
- }
- else
- {
- GEMMRHSMatrixInfo rhs_info;
- GEMMLHSMatrixInfo lhs_info;
- const auto m = static_cast<unsigned int>(reshape_info.m());
- const auto n = static_cast<unsigned int>(reshape_info.n());
- const int k = reshape_info.k();
- const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width();
- const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
- rhs_info.n0 = max_cl_vector_width / src1->element_size();
- rhs_info.k0 = 1;
- rhs_info.h0 = mult_transpose1xW_width;
- rhs_info.interleave = false;
- rhs_info.transpose = false;
- lhs_info.m0 = 4;
- lhs_info.k0 = 4;
- lhs_info.v0 = mult_interleave4x4_height;
- lhs_info.interleave = true;
- lhs_info.transpose = true;
-
- TensorShape tensor_shape0{ src0->tensor_shape() };
- tensor_shape0.set(0, k);
- tensor_shape0.set(1, m);
-
- TensorShape tensor_shape1{ src1->tensor_shape() };
- tensor_shape1.set(0, n);
- tensor_shape1.set(1, k);
-
- const TensorInfo tensor_info0 = src0->clone()->set_tensor_shape(tensor_shape0);
- const TensorInfo tensor_info1 = src1->clone()->set_tensor_shape(tensor_shape1);
-
- const TensorInfo tensor_info_reshaped0 = src0->clone()->set_tensor_shape(misc::shape_calculator::compute_lhs_reshaped_shape(tensor_info0, lhs_info));
- const TensorInfo tensor_info_reshaped1 = src1->clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(tensor_info1, rhs_info));
-
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src0, &tensor_info_reshaped0);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src1, &tensor_info_reshaped1);
-
- if(src2 != nullptr && !(helpers::float_ops::is_zero(beta)))
- {
- const unsigned int src2_dim0 = src2->dimension(0);
- const unsigned int src2_dim1 = src2->dimension(1);
-
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src2, src1);
- if(reshape_info.broadcast_bias())
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim1 != 1 || src2_dim0 != n), "Incorrect dimension of bias matrix which is to be broadcasted");
- }
- else
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((src2_dim0 != n || src2_dim1 != m), "Incorrect dimension of bias matrix");
- }
- }
- }
-
- if(dst->total_size() != 0)
- {
- const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, is_interleaved_transposed, reshape_info));
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, dst);
- }
-
- return Status{};
-}
-
-inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst,
- float beta, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target,
- ElementsProcessed &num_elements_processed)
-{
- ARM_COMPUTE_UNUSED(beta);
- bool window_changed = false;
- Window win{};
- Window win_out{};
-
- const DataType data_type = src0->data_type();
- 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 = reshape_info.reinterpret_input_as_3d();
- bool reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0);
-
- // In case both input and dst have to be reinterpreted as 3D tensors,
- // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
- if(reinterpret_input_as_3d == reinterpret_output_as_3d)
- {
- reinterpret_input_as_3d = false;
- reinterpret_output_as_3d = false;
- }
-
- // dst tensor auto inizialitation if not yet initialized
- auto_init_if_empty(*dst, src0->clone()->set_tensor_shape(misc::shape_calculator::compute_mm_shape(*src0, *src1, is_interleaved_transposed, reshape_info)));
-
- TensorInfo tmp_info(*dst);
-
- if(reinterpret_output_as_3d)
- {
- // Since the dst tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
- // the window needs to be constructed on the 2D collapsed version of the tensor
- TensorShape tmp_shape(dst->tensor_shape());
- tmp_shape.collapse(2U, 1U);
- tmp_info.set_tensor_shape(tmp_shape);
- }
-
- if(is_interleaved_transposed)
- {
- // reinterpret_input_as_3d is not supported if is_interleaved_transposed is set
- ARM_COMPUTE_ERROR_ON(reshape_info.reinterpret_input_as_3d());
-
- // Configure kernel window
- num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type);
- num_elems_processed_per_iteration_y = 4;
-
- win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
- if(src2 != nullptr)
- {
- const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x;
-
- const int bias_processed_per_iteration_y = reshape_info.broadcast_bias() ? 1 : num_elems_processed_per_iteration_y;
-
- AccessWindowStatic src2_access(src2, 0, 0,
- ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x),
- ceil_to_multiple(src2->dimension(1), bias_processed_per_iteration_y));
-
- window_changed = update_window_and_padding(win, src2_access); // window used by the execute_window_loop
- }
- }
- else // The input tensors have not been reshaped
- {
- // Special case for 1xN, 2xN, 3xN and 4xN src0 tensor. num_elems_processed_per_iteration_x is set up for the default case.
- num_elems_processed_per_iteration_x = max_cl_vector_width / data_size_from_type(data_type);
- num_elems_processed_per_iteration_y = std::min(static_cast<int>(dst->dimension(1)), 4);
-
- // Create kernels according to the architecture, data type and input size.
- GPUTarget arch_target = get_arch_from_target(gpu_target);
- if(arch_target == GPUTarget::BIFROST && data_type == DataType::F32)
- {
- num_elems_processed_per_iteration_x = (src1->dimension(0) <= 1000 && src0->num_dimensions() == 1) ? 2 : 4;
- }
-
- // Configure window
- win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
- win_out = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
- AccessWindowStatic src0_access(src0, 0, 0, src0->dimension(0), src0->dimension(1));
- AccessWindowStatic src1_access(src1, 0, 0, ceil_to_multiple(src1->dimension(0), num_elems_processed_per_iteration_x), src1->dimension(1));
- AccessWindowStatic dst_access(dst, 0, 0,
- dst->dimension(0),
- dst->dimension(1));
-
- if(src2 != nullptr)
- {
- const int bias_processed_per_iteration_x = num_elems_processed_per_iteration_x;
-
- AccessWindowStatic src2_access(src2, 0, 0,
- ceil_to_multiple(src2->dimension(0), bias_processed_per_iteration_x),
- src2->dimension(1));
-
- window_changed = update_window_and_padding(win, src0_access, src1_access, src2_access) || // window used by the execute_window_loop
- update_window_and_padding(win_out, dst_access); // window used to update the padding requirements of dst tensor
- }
- else
- {
- window_changed = update_window_and_padding(win, src0_access, src1_access) || // window used by the execute_window_loop
- update_window_and_padding(win_out, dst_access); // window used to update the padding requirements of dst tensor
- }
- }
-
- // Collapse along the Z direction
- // This collapse needs to be here in order to tune the Z dimension of LWS
- Window collapsed = win;
- const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(dst->num_dimensions()), 2u);
- collapsed = win.collapse(win, dimension_to_collapse);
-
- Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
- return std::make_pair(err, collapsed);
-}
-} // namespace
-
-ClGemmMatrixMultiplyKernel::ClGemmMatrixMultiplyKernel()
-{
- _type = CLKernelType::GEMM;
-}
-
-void ClGemmMatrixMultiplyKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha,
- float beta,
- bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, bool fp_mixed_precision, const ActivationLayerInfo &activation_info)
-{
- ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
-
- // Perform validate step
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src0, src1, src2, dst, beta,
- is_interleaved_transposed, reshape_info, fp_mixed_precision));
-
- auto padding_info = is_interleaved_transposed ? get_padding_info({ src0, src1, dst }) : get_padding_info({ src0, dst });
-
- _reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d();
- _reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 0);
- _add_bias = src2 != nullptr;
-
- // In case both input and dst have to be reinterpreted as 3D tensors,
- // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
- if(_reinterpret_input_as_3d == _reinterpret_output_as_3d)
- {
- _reinterpret_input_as_3d = false;
- _reinterpret_output_as_3d = false;
- }
-
- // Check if we need to slide the matrix B
- const unsigned int num_dimensions_src0 = _reinterpret_input_as_3d ? src0->num_dimensions() - 1 : src0->num_dimensions();
-
- _slide_matrix_b = (src1->num_dimensions() >= num_dimensions_src0);
-
- const DataType data_type = src0->data_type();
-
- // Get target architecture
- GPUTarget gpu_target = get_target();
-
- ElementsProcessed num_elements_processed{};
-
- // Configure kernel window
- auto win_config = validate_and_configure_window(src0, src1, src2, dst, beta, is_interleaved_transposed, reshape_info,
- gpu_target, num_elements_processed);
- ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
- ICLKernel::configure_internal(win_config.second);
-
- // If _reinterpret_input_as_3d = _reinterpret_output_as_3d = true, both will be turned off (false)
- // in which case we will dispatch a batched-GEMM to reduce the complexity of the address calculation within the OpenCL kernel.
- // This means that the actual m used by the kernel is given by dst->dimension(1)
- const unsigned int internal_m = _reinterpret_output_as_3d ? dst->dimension(1) * dst->dimension(2) : dst->dimension(1);
- const unsigned int n = dst->dimension(0);
-
- const unsigned int h_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(1) : src0->dimension(1);
- const unsigned int d_gemm_3d = _reinterpret_output_as_3d ? dst->dimension(2) : src0->dimension(2);
-
- const unsigned int m0 = num_elements_processed.y();
- const unsigned int n0 = num_elements_processed.x();
-
- // Calculate partial (store instead of load) M0 and partial N0 for the partial blocks at the end of a row/column if any. This is to avoid padding.
- const unsigned int partial_store_m0 = internal_m % m0;
- const unsigned int partial_store_n0 = n % n0;
-
- // Create build options
- CLBuildOptions build_opts;
-
- build_opts.add_option_if(!(helpers::float_ops::is_one(alpha)), "-DALPHA=" + float_to_string_with_full_precision(alpha));
- build_opts.add_option_if(src2 != 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(reshape_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(h_gemm_3d));
- build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(d_gemm_3d));
- build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(src1->dimension(2)));
- build_opts.add_option_if(activation_info.enabled(), "-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(activation_info.activation())));
- build_opts.add_option_if(activation_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(activation_info.a()));
- build_opts.add_option_if(activation_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(activation_info.b()));
- build_opts.add_option("-DIN1_DIM_X=" + support::cpp11::to_string(src1->dimension(0)));
-
- const bool is_bifrost = get_arch_from_target(gpu_target) == GPUTarget::BIFROST;
-
- std::string kernel_name;
- if(is_interleaved_transposed)
- {
- const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width();
- const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
-
- build_opts.add_option("-DM=" + support::cpp11::to_string(internal_m));
- build_opts.add_option("-DN=" + support::cpp11::to_string(n));
- build_opts.add_option("-DK=" + support::cpp11::to_string(src1->dimension(0) / (n0 * mult_transpose1xW_width)));
- build_opts.add_option("-DH0=" + support::cpp11::to_string(mult_transpose1xW_width));
- build_opts.add_option("-DV0=" + support::cpp11::to_string(mult_interleave4x4_height));
- build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
- build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
-
- if(is_data_type_float(data_type) && is_bifrost)
- {
- kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type)) + "_bifrost";
- }
- else
- {
- kernel_name = "gemm_mm_interleaved_transposed_" + lower_string(string_from_data_type(data_type));
- if(fp_mixed_precision && data_type == DataType::F16)
- {
- // currently wider accumulator is only supported for fp16 kernels.
- kernel_name += "_acc32";
- }
- }
- }
- else // The input tensors have not been reshaped
- {
- build_opts.add_option("-DN=" + support::cpp11::to_string(n));
- build_opts.add_option("-DK=" + support::cpp11::to_string(src0->dimension(0)));
- build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
- build_opts.add_option("-DM0=" + support::cpp11::to_string(m0));
- build_opts.add_option("-DN0=" + support::cpp11::to_string(n0));
- build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
- build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
-
- // Create kernels according to the architecture, data type and input size.
- if(is_data_type_float(data_type) && is_bifrost)
- {
- kernel_name = "gemm_mm_floating_point";
-
- if(src0->num_dimensions() != 1)
- {
- kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost";
- if(fp_mixed_precision && data_type == DataType::F16)
- {
- // currently wider accumulator is only supported for fp16 kernels.
- kernel_name += "_acc32";
- }
- }
- else if(src1->dimension(0) <= 1000 && data_type == DataType::F32)
- {
- // The first kernel is optimized for the case of 1000 or less dst elements (e.g. FC8 of AlexNet and VGG-16, and
- // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 dst elements (e.g.
- // FC6 and FC7 of AlexNet and VGG-16).
- kernel_name += "_" + lower_string(string_from_data_type(data_type)) + "_bifrost_1000";
- }
-
- // The work-group size equal to the Bifrost quad size has been proved to be optimal for these kernels
- // via exhaustive autotuning over a range of representative layer configurations.
- set_lws_hint(cl::NDRange(4));
- }
- else // (MIDGARD and F32) or (F16)
- {
- kernel_name = "gemm_mm_floating_point";
- }
- }
- // Create kernel
- _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
-
- // Set config_id for enabling LWS tuning
- _config_id = "gemm_";
- _config_id += (is_interleaved_transposed ? "reshaped_" : "");
- _config_id += (_add_bias ? "add_bias_" : "");
- _config_id += (reshape_info.broadcast_bias() ? "broadcast_bias_" : "");
- _config_id += (fp_mixed_precision ? "fp_mixed_" : "");
- _config_id += (_reinterpret_input_as_3d ? "3di_" : "");
- _config_id += (_reinterpret_output_as_3d ? "3do_" : "");
- _config_id += lower_string(string_from_data_type(src0->data_type()));
- _config_id += "_";
- _config_id += support::cpp11::to_string(dst->dimension(1));
- _config_id += "_";
- _config_id += support::cpp11::to_string(dst->dimension(0));
- _config_id += "_";
- _config_id += support::cpp11::to_string(dst->dimension(2));
- _config_id += "_";
- _config_id += support::cpp11::to_string(dst->dimension(3));
- _config_id += "_";
- _config_id += (is_interleaved_transposed ? support::cpp11::to_string(src1->dimension(0)) : support::cpp11::to_string(src1->dimension(1)));
-
- ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
-}
-
-Status ClGemmMatrixMultiplyKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta,
- bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, bool fp_mixed_precision, const ActivationLayerInfo &activation_info)
-{
- // Note: num_elements_processed will be set in validate_and_configure_window()
- ElementsProcessed num_elements_processed{};
- ARM_COMPUTE_UNUSED(alpha);
- ARM_COMPUTE_UNUSED(activation_info);
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src0, src1, src2, dst, beta, is_interleaved_transposed, reshape_info, fp_mixed_precision));
- ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src0->clone().get(),
- src1->clone().get(),
- (src2 != nullptr) ? src2->clone().get() : nullptr,
- dst->clone().get(),
- beta,
- is_interleaved_transposed,
- reshape_info,
- gpu_target,
- num_elements_processed)
- .first);
-
- return Status{};
-}
-
-void ClGemmMatrixMultiplyKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
-{
- ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
- ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
-
- const auto src0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
- const auto src1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
- const auto src2 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2));
- auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
-
- ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
- ARM_COMPUTE_ERROR_ON(_add_bias && src2 == nullptr);
-
- if(src1->info()->num_dimensions() < 3)
- {
- // The stride_z for matrix B must be zero if we do not slice
- ARM_COMPUTE_ERROR_ON(src1->info()->strides_in_bytes()[3] != 0);
- }
-
- Window slice = window.first_slice_window_3D();
- Window slice_matrix_b = slice;
-
- slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
- slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
-
- const unsigned int num_arguments_bias = _add_bias ? num_arguments_per_2D_tensor() + 1 : 0;
-
- 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 + num_arguments_bias;
- const unsigned int total_cross_plane_pad = src0->info()->padding().top + src0->info()->padding().bottom;
- _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
- }
-
- if(_reinterpret_output_as_3d)
- {
- // Pass bottom paddings to the kernel if the dst has to be reinterpreted as 3D tensor
- const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0) + num_arguments_bias;
- const unsigned int total_cross_plane_pad = dst->info()->padding().top + dst->info()->padding().bottom;
- _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
- }
-
- do
- {
- Window slice_b = slice;
- // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
- // This scenario can happen when the matrix multiplication is used to perform a convolution operation
- if(!_slide_matrix_b)
- {
- slice_b = slice_matrix_b;
- }
-
- unsigned int idx = 0;
- add_2D_tensor_argument(idx, src0, slice);
- add_2D_tensor_argument(idx, src1, slice_b);
- if(_add_bias)
- {
- add_2D_tensor_argument(idx, src2, slice);
- }
- add_2D_tensor_argument(idx, dst, slice);
- _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src0->info()->strides_in_bytes()[2]));
- _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src1->info()->strides_in_bytes()[2]));
- if(_add_bias)
- {
- _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src2->info()->strides_in_bytes()[2]));
- }
- _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[2]));
- enqueue(queue, *this, slice, lws_hint());
- }
- while(window.slide_window_slice_3D(slice));
-}
-} // namespace kernels
-} // namespace opencl
-} // namespace arm_compute
diff --git a/src/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h b/src/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h
deleted file mode 100644
index c16e3279f5..0000000000
--- a/src/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h
+++ /dev/null
@@ -1,88 +0,0 @@
-/*
- * Copyright (c) 2017-2021 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_KERNEL_H
-#define ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_KERNEL_H
-
-#include "src/core/common/Macros.h"
-#include "src/gpu/cl/ClCompileContext.h"
-#include "src/gpu/cl/IClKernel.h"
-
-namespace arm_compute
-{
-namespace opencl
-{
-namespace kernels
-{
-/** OpenCL kernel to multiply two input matrices "A" and "B" and add a martix "C" if provided. All elements of the output matrix will be multiplied by alpha. In case matrix C is passed, it will be added to the previous result.
- * For the matrix C, the broadcast addition is supported if the flag "broadcast_bias" is set in the GEMMReshapeInfo object
- *
- * @note If the input tensors @p src0 and @p src1 have been reshaped respectively with @ref ClGemmReshapeLhsMatrixKernel" and @ref ClGemmReshapeRhsMatrixKernel,
- * the flag @p is_interleaved_transposed must be set to true
- *
- * @attention @p src1 tensor must have at least 2 dimensions (matrix)
- */
-class ClGemmMatrixMultiplyKernel : public IClKernel
-{
-public:
- ClGemmMatrixMultiplyKernel();
- ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClGemmMatrixMultiplyKernel);
- /** Initialise the kernel's input, output and alpha
- *
- * @param[in] compile_context The compile context to be used.
- * @param[in] src0 Input tensor containing the Matrix A. Data types supported: F16/F32
- * @param[in] src1 Input tensor containing the Matrix B. Data type supported: same as @p src0
- * @param[in] src2 Input tensor containing the Matrix C (bias). Can be nullptr. Data type supported: same as @p src0
- * @param[out] dst Output tensor to store the result of matrix multiplication. Data type supported: same as @p src0
- * @param[in] alpha Weight of the matrix product
- * @param[in] beta (Optional) Weight of vector C. Default value is 0. Only beta = 1 is currently supported.
- * @param[in] is_interleaved_transposed (Optional) True if input0 and input1 have been reshaped respectively using @ref ClGemmReshapeLhsMatrixKernel and @ref ClGemmReshapeRhsMatrixKernel
- * @param[in] reshape_info (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped
- * @param[in] fp_mixed_precision (Optional) Use wider accumulators (32 bit instead of 16 for FP16) to improve accuracy
- * @param[in] activation_info (Optional) Activation to apply after the matrix multiplication
- *
- */
- void configure(const ClCompileContext &compile_context, ITensorInfo *src0, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float alpha, float beta = 0.f,
- bool is_interleaved_transposed = true, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo(), bool fp_mixed_precision = false, const ActivationLayerInfo &activation_info = ActivationLayerInfo());
- /** Static function to check if given info will lead to a valid configuration
- *
- * Similar to @ref ClGemmMatrixMultiplyKernel::configure()
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float alpha, float beta,
- bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target, bool fp_mixed_precision = false, const ActivationLayerInfo &activation_info = ActivationLayerInfo());
-
- // Inherited methods overridden:
- void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
-
-public:
- bool _slide_matrix_b{ true };
- bool _reinterpret_input_as_3d{ false };
- bool _reinterpret_output_as_3d{ false };
- bool _add_bias{ false };
-};
-} // namespace kernels
-} // namespace opencl
-} // namespace arm_compute
-#endif /* ARM_COMPUTE_CL_GEMM_MATRIXMULTIPLY_KERNEL_H */
diff --git a/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp b/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp
index 448d35353b..6c872fd48c 100644
--- a/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp
+++ b/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.cpp
@@ -55,7 +55,7 @@ Status validate_arguments(const ITensorInfo *src0, const ITensorInfo *src1, cons
{
ARM_COMPUTE_UNUSED(alpha);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src0, 1, DataType::F32, DataType::F16);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src0, src1);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(src0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(src1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
diff --git a/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h b/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h
index 26dec918cd..89837cc515 100644
--- a/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h
+++ b/src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h
@@ -44,7 +44,7 @@ public:
/** Initialise the kernel's input and dst.
*
* @param[in] compile_context The compile context to be used.
- * @param[in] src0 Input tensor for the LHS matrix. Data type supported: F32. The number of dimensions for the LHS matrix must be less or equal than 4.
+ * @param[in] src0 Input tensor for the LHS matrix. Data type supported: F32/F16. The number of dimensions for the LHS matrix must be less or equal than 4.
* @param[in] src1 Input tensor for the RHS matrix. Data type supported: same as @p src0. The number of dimensions for the RHS matrix must be less or equal than 3.
* @param[in] src2 Input tensor containing the bias matrix. Data type supported: same as @p src0.
* @param[out] dst dst tensor info. Data type supported: same as @p src0
diff --git a/src/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.cpp b/src/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.cpp
index b9eac2412e..d74c7fac9b 100644
--- a/src/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.cpp
+++ b/src/gpu/cl/kernels/gemm/native/ClGemmDefaultConfigNativeBifrost.cpp
@@ -101,7 +101,7 @@ std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> ClGemmDefaultConfigNativeBifrost
}
else
{
- return configure_lhs_rhs_info(m, n, 5, 4, 2, 1, 1, false, false, false, false);
+ return configure_lhs_rhs_info(m, n, 4, 4, 4, 1, 1, false, false, false, false);
}
}
diff --git a/src/gpu/cl/operators/ClFullyConnected.h b/src/gpu/cl/operators/ClFullyConnected.h
index dc5f9e5c9b..b5ac70c93b 100644
--- a/src/gpu/cl/operators/ClFullyConnected.h
+++ b/src/gpu/cl/operators/ClFullyConnected.h
@@ -46,7 +46,7 @@ class ClTranspose;
*
* -# @ref opencl::kernels::ClIm2ColKernel (called when the input comes from a convolutional layer)
* -# @ref CLTranspose (if @p are_weights_reshaped is set to false and transpose_weights is set to true ) (called once)
- * -# @ref opencl::kernels::ClGemmMatrixMultiplyKernel or @ref CLGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
+ * -# @ref opencl::ClGemm or @ref CLGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
*
* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
*/
diff --git a/src/gpu/cl/operators/ClGemm.cpp b/src/gpu/cl/operators/ClGemm.cpp
index 4cd5237b11..d2d0f8f91d 100644
--- a/src/gpu/cl/operators/ClGemm.cpp
+++ b/src/gpu/cl/operators/ClGemm.cpp
@@ -64,27 +64,14 @@ namespace
{
inline bool validate_gemm_kernel(CLGEMMKernelType kernel_type)
{
- switch(kernel_type)
- {
- case CLGEMMKernelType::NATIVE_V1:
- case CLGEMMKernelType::RESHAPED_ONLY_RHS:
- case CLGEMMKernelType::RESHAPED_V1:
- case CLGEMMKernelType::RESHAPED:
- {
- return true;
- }
- default:
- {
- return false;
- }
- }
+ return kernel_type == CLGEMMKernelType::NATIVE? false : true;
}
//Automatically select between mlgo (prioritized) and default heuristics for gemm kernel type
inline CLGEMMKernelType auto_select_gemm_kernel(auto_heuristics::CommonQuery query, bool reshape_b_only_on_first_run, bool constant_weights)
{
if(!constant_weights)
{
- return CLGEMMKernelType::NATIVE_V1;
+ return CLGEMMKernelType::NATIVE;
}
auto gemm_kernel = auto_heuristics::select_mlgo_gemm_kernel(query, reshape_b_only_on_first_run);
@@ -198,97 +185,54 @@ inline std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_r
} // namespace
ClGemm::ClGemm()
- : _mm_kernel(std::make_unique<ClGemmMatrixMultiplyKernel>()),
- _reshape_lhs_kernel(std::make_unique<ClGemmReshapeLhsMatrixKernel>()),
+ : _reshape_lhs_kernel(std::make_unique<ClGemmReshapeLhsMatrixKernel>()),
_reshape_rhs_kernel(std::make_unique<ClGemmReshapeRhsMatrixKernel>()),
+ _mm_native_kernel(std::make_unique<ClGemmMatrixMultiplyNativeKernel>()),
_mm_reshaped_kernel(std::make_unique<ClGemmMatrixMultiplyReshapedKernel>()),
_mm_reshaped_only_rhs_kernel(std::make_unique<ClGemmMatrixMultiplyReshapedOnlyRhsKernel>()),
_mm_reshaped_only_rhs_fallback_kernel(std::make_unique<ClGemmMatrixMultiplyReshapedOnlyRhsKernel>()),
_tmp_a(),
_tmp_b(),
_reshape_b_only_on_first_run(false),
- _gemm_kernel_type(CLGEMMKernelType::NATIVE_V1),
+ _gemm_kernel_type(CLGEMMKernelType::NATIVE),
_is_prepared(false),
_aux_mem(AuxTensorIdx::Count)
{
}
-void ClGemm::configure_native_v1(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta,
- const GEMMInfo &gemm_info)
+void ClGemm::configure_native(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta,
+ const GEMMInfo &gemm_info)
{
- const unsigned int m = gemm_info.reinterpret_input_as_3d() ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
- const unsigned int n = b->dimension(0);
- const unsigned int k = a->dimension(0);
- const GPUTarget gpu_target = CLScheduler::get().target();
-
- // Set the target for the kernels
- _mm_kernel->set_target(gpu_target);
-
- GEMMReshapeInfo reshape_info(m, n, k, 1, 1, gemm_info.depth_output_gemm3d(), gemm_info.reinterpret_input_as_3d(), gemm_info.broadcast_bias());
-
- // Configure and tune matrix multiply kernel
- _mm_kernel->configure(compile_context, a, b, c, output, alpha, beta, false, reshape_info, gemm_info.fp_mixed_precision(), gemm_info.activation_info());
-
- // Tune kernel statically
- CLScheduler::get().tune_kernel_static(*_mm_kernel);
-}
+ DataType data_type = a->data_type();
+ bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
+ const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
+ const unsigned int n = b->dimension(0);
+ const unsigned int k = a->dimension(0);
+ const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
+ const int depth_output_gemm3d = gemm_info.depth_output_gemm3d();
+ const GPUTarget gpu_target = CLScheduler::get().target();
+ bool broadcast_bias = gemm_info.broadcast_bias();
-void ClGemm::configure_reshaped_v1(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta,
- const GEMMInfo &gemm_info)
-{
- bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
- const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
- const unsigned int n = b->dimension(0);
- const unsigned int k = a->dimension(0);
- const int depth_output_gemm3d = gemm_info.depth_output_gemm3d();
- const GPUTarget gpu_target = CLScheduler::get().target();
- int mult_transpose1xW_width = 1;
- int mult_interleave4x4_height = 1;
+ GEMMKernelInfo kernel_info;
+ kernel_info.m = m;
+ kernel_info.n = n;
+ kernel_info.k = k;
+ kernel_info.depth_output_gemm3d = depth_output_gemm3d;
+ kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
+ kernel_info.broadcast_bias = broadcast_bias;
+ kernel_info.activation_info = gemm_info.activation_info();
// Set the target for the kernels
- _reshape_lhs_kernel->set_target(gpu_target);
- _mm_kernel->set_target(gpu_target);
-
- if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST)
- {
- mult_transpose1xW_width = 4;
- mult_interleave4x4_height = 2;
- }
-
- GEMMRHSMatrixInfo rhs_info;
- rhs_info.n0 = 16 / b->element_size();
- rhs_info.k0 = 1;
- rhs_info.h0 = mult_transpose1xW_width;
- rhs_info.interleave = false;
- rhs_info.transpose = false;
-
- GEMMLHSMatrixInfo lhs_info;
- lhs_info.m0 = 4;
- lhs_info.k0 = 4;
- lhs_info.v0 = mult_interleave4x4_height;
- lhs_info.interleave = true;
- lhs_info.transpose = true;
+ _mm_native_kernel->set_target(gpu_target);
- GEMMReshapeInfo reshape_info(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, false, gemm_info.broadcast_bias());
-
- // Configure interleave kernel
- _reshape_lhs_kernel->configure(compile_context, a, &_tmp_a, lhs_info, reinterpret_input_as_3d);
-
- // Configure transpose kernel
- _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info);
+ auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size });
// Configure and tune matrix multiply kernel
- _mm_kernel->configure(compile_context, &_tmp_a, &_tmp_b, c, output, alpha, beta, true, reshape_info, gemm_info.fp_mixed_precision(), gemm_info.activation_info());
-
- CLScheduler::get().tune_kernel_static(*_mm_kernel);
-
- // Request memory for LHS and RHS reshape matrix
- _aux_mem[LhsReshape] = MemoryInfo(offset_int_vec(LhsReshape), MemoryLifetime::Temporary, _tmp_a.total_size());
- _aux_mem[RhsReshape] = MemoryInfo(offset_int_vec(RhsReshape), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size());
+ _mm_native_kernel->configure(compile_context, a, b, c, output, alpha, beta, config.lhs_info, config.rhs_info, kernel_info);
}
-void ClGemm::configure_reshaped_v2(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta,
- const GEMMInfo &gemm_info)
+void ClGemm::configure_reshaped(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta,
+ const GEMMInfo &gemm_info)
{
DataType data_type = a->data_type();
bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
@@ -311,7 +255,7 @@ void ClGemm::configure_reshaped_v2(const CLCompileContext &compile_context, ITen
// Set the target for the kernels
_reshape_lhs_kernel->set_target(gpu_target);
- _mm_kernel->set_target(gpu_target);
+ _mm_reshaped_kernel->set_target(gpu_target);
GEMMLHSMatrixInfo lhs_info{};
GEMMRHSMatrixInfo rhs_info{};
@@ -354,7 +298,8 @@ void ClGemm::configure_reshaped_only_rhs(const CLCompileContext &compile_context
kernel_info.activation_info = gemm_info.activation_info();
// Set the target for the kernels
- _mm_kernel->set_target(gpu_target);
+ _mm_reshaped_only_rhs_kernel->set_target(gpu_target);
+ _mm_reshaped_only_rhs_fallback_kernel->set_target(gpu_target);
GEMMLHSMatrixInfo lhs_info{};
GEMMRHSMatrixInfo rhs_info{};
@@ -381,78 +326,35 @@ void ClGemm::configure_reshaped_only_rhs(const CLCompileContext &compile_context
_aux_mem[RhsReshape] = MemoryInfo(offset_int_vec(RhsReshape), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size());
}
-Status ClGemm::validate_native_v1(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
+Status ClGemm::validate_native(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
{
ARM_COMPUTE_UNUSED(alpha);
ARM_COMPUTE_UNUSED(output);
// Get the GPU target
const GPUTarget gpu_target = CLScheduler::get().target();
+ DataType data_type = a->data_type();
bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
const unsigned int n = b->dimension(0);
const unsigned int k = a->dimension(0);
+ const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
const int depth_output_gemm3d = gemm_info.depth_output_gemm3d();
+ const bool broadcast_bias = gemm_info.broadcast_bias();
- const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d, gemm_info.broadcast_bias());
-
- // Validate matrix multiply
- ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyKernel::validate(a, b, c, output, alpha, beta,
- false, reshape_info, gpu_target, gemm_info.fp_mixed_precision(), gemm_info.activation_info()));
-
- return Status{};
-}
-
-Status ClGemm::validate_reshaped_v1(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
-{
- ARM_COMPUTE_UNUSED(alpha);
- ARM_COMPUTE_UNUSED(output);
-
- TensorInfo tmp_a_info{};
- TensorInfo tmp_b_info{};
-
- // Get the GPU target
- const GPUTarget gpu_target = CLScheduler::get().target();
- const unsigned int m = gemm_info.reinterpret_input_as_3d() ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
- const unsigned int n = b->dimension(0);
- const unsigned int k = a->dimension(0);
- int mult_transpose1xW_width = 1;
- int mult_interleave4x4_height = 1;
- const int depth_output_gemm3d = gemm_info.depth_output_gemm3d();
-
- if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST)
- {
- mult_transpose1xW_width = 4;
- mult_interleave4x4_height = 2;
- }
-
- GEMMRHSMatrixInfo rhs_info;
- rhs_info.n0 = 16 / b->element_size();
- rhs_info.k0 = 1;
- rhs_info.h0 = mult_transpose1xW_width;
- rhs_info.interleave = false;
- rhs_info.transpose = false;
-
- GEMMLHSMatrixInfo lhs_info;
- lhs_info.m0 = 4;
- lhs_info.k0 = 4;
- lhs_info.v0 = mult_interleave4x4_height;
- lhs_info.interleave = true;
- lhs_info.transpose = true;
-
- const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, false, gemm_info.broadcast_bias());
-
- // Validate interleave kernel
- auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d())));
- ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeLhsMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d()));
+ GEMMKernelInfo kernel_info;
+ kernel_info.m = m;
+ kernel_info.n = n;
+ kernel_info.k = k;
+ kernel_info.depth_output_gemm3d = depth_output_gemm3d;
+ kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
+ kernel_info.broadcast_bias = broadcast_bias;
+ kernel_info.activation_info = gemm_info.activation_info();
- // Validate transpose kernel
- auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
- ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info));
+ auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size });
// Validate matrix multiply
- ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyKernel::validate(&tmp_a_info, &tmp_b_info, c, output, alpha, beta,
- true, reshape_info, gpu_target, gemm_info.fp_mixed_precision(), gemm_info.activation_info()));
+ ARM_COMPUTE_RETURN_ON_ERROR(ClGemmMatrixMultiplyNativeKernel::validate(a, b, c, output, alpha, beta, config.lhs_info, config.rhs_info, kernel_info));
return Status{};
}
@@ -583,19 +485,14 @@ void ClGemm::configure(const CLCompileContext &compile_context, ITensorInfo *a,
switch(_gemm_kernel_type)
{
- case CLGEMMKernelType::NATIVE_V1:
- {
- configure_native_v1(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
- break;
- }
- case CLGEMMKernelType::RESHAPED_V1:
+ case CLGEMMKernelType::NATIVE:
{
- configure_reshaped_v1(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
+ configure_native(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
break;
}
case CLGEMMKernelType::RESHAPED:
{
- configure_reshaped_v2(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
+ configure_reshaped(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
break;
}
case CLGEMMKernelType::RESHAPED_ONLY_RHS:
@@ -632,14 +529,9 @@ Status ClGemm::validate(const ITensorInfo *a, const ITensorInfo *b, const ITenso
switch(gemm_kernel_type)
{
- case CLGEMMKernelType::NATIVE_V1:
+ case CLGEMMKernelType::NATIVE:
{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_native_v1(a, b, c_to_use, output, alpha, beta, gemm_info));
- break;
- }
- case CLGEMMKernelType::RESHAPED_V1:
- {
- ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_v1(a, b, c_to_use, output, alpha, beta, gemm_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_native(a, b, c_to_use, output, alpha, beta, gemm_info));
break;
}
case CLGEMMKernelType::RESHAPED:
@@ -679,12 +571,11 @@ void ClGemm::run(ITensorPack &tensors)
// Run matrix multiply kernel
switch(_gemm_kernel_type)
{
- case CLGEMMKernelType::NATIVE_V1:
+ case CLGEMMKernelType::NATIVE:
{
- CLScheduler::get().enqueue_op(*_mm_kernel, tensors, true);
+ CLScheduler::get().enqueue_op(*_mm_native_kernel, tensors, true);
break;
}
- case CLGEMMKernelType::RESHAPED_V1:
case CLGEMMKernelType::RESHAPED:
{
// Run interleave kernel
@@ -704,10 +595,6 @@ void ClGemm::run(ITensorPack &tensors)
{
CLScheduler::get().enqueue_op(*_mm_reshaped_kernel, gemm_reshaped_pack, true);
}
- else
- {
- CLScheduler::get().enqueue_op(*_mm_kernel, gemm_reshaped_pack, true);
- }
break;
}
case CLGEMMKernelType::RESHAPED_ONLY_RHS:
diff --git a/src/gpu/cl/operators/ClGemm.h b/src/gpu/cl/operators/ClGemm.h
index 60bb78c371..fd53648b3c 100644
--- a/src/gpu/cl/operators/ClGemm.h
+++ b/src/gpu/cl/operators/ClGemm.h
@@ -31,7 +31,6 @@
#include "src/gpu/cl/ClCompileContext.h"
#include "src/gpu/cl/IClKernel.h"
#include "src/gpu/cl/IClOperator.h"
-#include "src/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h"
#include "src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h"
#include "src/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedKernel.h"
#include "src/gpu/cl/kernels/ClGemmMatrixMultiplyReshapedOnlyRhsKernel.h"
@@ -46,10 +45,10 @@ namespace opencl
{
/** Basic function to execute GEMM on OpenCL. This function calls the following OpenCL kernels:
*
- * -# @ref kernels::ClGemmReshapeLhsMatrixKernel (only if the RESHAPED_V1 is selected by the heuristic model)
- * -# @ref kernels::ClGemmReshapeRhsMatrixKernel (only if either the RESHAPED_V1 or RESHAPED_ONLY_RHS is selected by the select_gemm_kernel method())
- * -# @ref kernels::ClGemmMatrixMultiplyKernel (only if either the NATIVE or RESHAPED_V1 is selected by the select_gemm_kernel method())
- * -# @ref kernels::ClGemmMatrixMultiplyReshapedKernel (only if RESHAPED_V1 is selected by the select_gemm_kernel method())
+ * -# @ref kernels::ClGemmReshapeLhsMatrixKernel (only if the RESHAPED is selected by the heuristic model)
+ * -# @ref kernels::ClGemmReshapeRhsMatrixKernel (only if either the RESHAPED or RESHAPED_ONLY_RHS is selected by the select_gemm_kernel method())
+ * -# @ref kernels::ClGemmMatrixMultiplyNativeKernel (only if NATIVE is selected by the select_gemm_kernel method())
+ * -# @ref kernels::ClGemmMatrixMultiplyReshapedKernel (only if RESHAPED is selected by the select_gemm_kernel method())
* -# @ref kernels::ClGemmMatrixMultiplyReshapedOnlyRhsKernel (only if RESHAPED_ONLY_RHS is selected by the select_gemm_kernel method())
*/
class ClGemm : public IClOperator
@@ -100,13 +99,11 @@ public:
experimental::MemoryRequirements workspace() const override;
private:
- void configure_native_v1(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
- void configure_reshaped_v1(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
- void configure_reshaped_v2(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
+ void configure_native(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
+ void configure_reshaped(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
void configure_reshaped_only_rhs(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
- static Status validate_native_v1(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
- static Status validate_reshaped_v1(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
+ static Status validate_native(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
static Status validate_reshaped(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
static Status validate_reshaped_only_rhs(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info);
@@ -119,9 +116,9 @@ private:
};
private:
- std::unique_ptr<kernels::ClGemmMatrixMultiplyKernel> _mm_kernel;
std::unique_ptr<kernels::ClGemmReshapeLhsMatrixKernel> _reshape_lhs_kernel;
std::unique_ptr<kernels::ClGemmReshapeRhsMatrixKernel> _reshape_rhs_kernel;
+ std::unique_ptr<kernels::ClGemmMatrixMultiplyNativeKernel> _mm_native_kernel;
std::unique_ptr<kernels::ClGemmMatrixMultiplyReshapedKernel> _mm_reshaped_kernel;
std::unique_ptr<kernels::ClGemmMatrixMultiplyReshapedOnlyRhsKernel> _mm_reshaped_only_rhs_kernel;
std::unique_ptr<kernels::ClGemmMatrixMultiplyReshapedOnlyRhsKernel> _mm_reshaped_only_rhs_fallback_kernel;
diff --git a/src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp b/src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp
index 6fd7e52d5d..7a62186677 100644
--- a/src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp
+++ b/src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp
@@ -239,7 +239,6 @@ void ClGemmLowpMatrixMultiplyCore::configure(const CLCompileContext &compile_con
GEMMLHSMatrixInfo lhs_info;
// Arguments used by GEMMReshapeInfo
- // If we pass the matrix A and matrix B reshaped to CLGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to CLGEMMReshapeInfo
// in order to know how the matrices have been reshaped
bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-24 12:32:20 +0000