Adding GpuAdd to dynamic fusion operators
- Provide support for Add operator
- Auto initialize the destination tensor before testing fusion in conv2d
and elementwise binary ops.
Resolves: COMPMID-5518
Signed-off-by: Ramy Elgammal <ramy.elgammal@arm.com>
Change-Id: Ibd815020f02b57f88eea7c2921bdcf98605d99c5
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/8617
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateElementwiseBinary.cpp b/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateElementwiseBinary.cpp
new file mode 100644
index 0000000..996bf15
--- /dev/null
+++ b/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateElementwiseBinary.cpp
@@ -0,0 +1,315 @@
+/*
+ * Copyright (c) 2022 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 "ClTemplateElementwiseBinary.h"
+
+#include "src/dynamic_fusion/sketch/gpu/GpuKernelComponentGroup.h"
+#include "src/dynamic_fusion/sketch/gpu/components/cl/ClComponentElementwiseBinary.h"
+
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "src/core/helpers/WindowHelpers.h"
+
+#include "support/StringSupport.h"
+
+namespace arm_compute
+{
+namespace experimental
+{
+namespace dynamic_fusion
+{
+constexpr unsigned int vector_size_byte_opencl = 16;
+
+ClTemplateElementwiseBinary::ClTemplateElementwiseBinary(ComponentId id,
+ const ArgumentPack<ITensorInfo> &tensors,
+ const Attributes &attributes)
+ : IGpuTemplateComponentWriter{ id, tensors },
+ _lhs{},
+ _rhs{},
+ _dst{},
+ _attributes{ attributes }
+{
+ _lhs = this->tensors().get_const_tensor(TensorType::ACL_SRC_0);
+ _rhs = this->tensors().get_const_tensor(TensorType::ACL_SRC_1);
+ _dst = this->tensors().get_const_tensor(TensorType::ACL_DST_0);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(_lhs, _rhs, _dst);
+}
+
+std::string ClTemplateElementwiseBinary::get_name() const
+{
+ return "elementwise_binary";
+}
+
+std::string ClTemplateElementwiseBinary::get_component_code(const ComponentGroup &comp_group) const
+{
+ ARM_COMPUTE_UNUSED(comp_group);
+ std::string code;
+ const bool is_broadcast = _lhs->tensor_shape() != _rhs->tensor_shape();
+ const bool is_root = (comp_group.get_root_component()->id() == this->id());
+
+ if(is_root)
+ {
+ code =
+R"_(
+ //------------------ START KERNEL {{meta_kernel_id}} ELTWISE_OP ---------------------
+)_"
+ // IN_0(LHS) {{lhs}}
+ // IN_1(RHS) {{rhs}}
+ // OUT(dst, accum) {{dst}}
+ // dst = lhs + rhs (mix-precision, broadcast, boundary aware)
+R"_(
+ TILE({{DATA_TYPE}}, M0, N0, {{dst}});
+ TILE(uint, M0, 1, g_dst_indirect_y);
+ {
+ TILE({{DATA_TYPE}}, M0, N0, lhs_tile);
+ TILE({{DATA_TYPE}}, M0, N0, rhs_tile);
+)_"
+ // Assuming un-collapsed window
+R"_(
+ {{lhs}}_offset_first_element_in_bytes += g_ind_2 * {{lhs}}_stride_z;
+ {{rhs}}_offset_first_element_in_bytes += g_ind_2 * {{rhs}}_stride_z;
+
+ T_LOAD({{DATA_TYPE}}, M0, N0, BUFFER, {{lhs}}, g_ind_0, g_ind_1, 1, {{lhs}}_stride_y, lhs_tile);
+ T_LOAD({{DATA_TYPE}}, {{rhs_m0}}, {{rhs_n0}}, BUFFER, {{rhs}}, {{rhs_start_ind_0}}, {{rhs_start_ind_1}}, 1, {{rhs}}_stride_y, rhs_tile);
+)_";
+ if(is_broadcast)
+ {
+ code +=
+R"_(
+ T_ELTWISE_BROADCAST_{{ELTWISE_OP}}_X({{DATA_TYPE}}, M0, N0, lhs_tile, rhs_tile, {{dst}});
+)_";
+ }
+ else
+ {
+ code +=
+R"_(
+ T_ELTWISE_{{ELTWISE_OP}}({{DATA_TYPE}}, M0, N0, lhs_tile, rhs_tile, {{dst}});
+)_";
+ }
+ code +=
+ // Calculate the destination indirect Y
+R"_(
+ LOOP_UNROLLING(int, i, 0, 1, M0,
+ {
+ g_dst_indirect_y[i].v = (uint)min(g_ind_1 + i, (int)({{dst}}_w * {{dst}}_h) - 1);
+ g_dst_indirect_y[i].v += g_ind_2 * (int)({{dst}}_w * {{dst}}_h);
+ })
+ }
+ //------------------ END KERNEL {{meta_kernel_id}} ELTWISE_OP ---------------------
+)_";
+ }
+
+ else // non-root
+ {
+ code =
+R"_(
+ //------------------ START KERNEL {{meta_kernel_id}} ELTWISE_OP ---------------------
+)_"
+ // IN_0/Out(Accumulator) {{acc}}
+ // IN_1(Operand) {{operand}}
+ // acc = operand + acc (mix-precision, broadcast, boundary aware)
+R"_(
+ {
+ TILE(DATA_TYPE, M0, N0, operand_tile);
+ T_LOAD({{DATA_TYPE}}, {{rhs_m0}}, {{rhs_n0}}, BUFFER, {{operand}}, {{rhs_start_ind_0}}, {{rhs_start_ind_1}}, 1, {{operand}}_stride_y, operand_tile);
+)_";
+
+ if(is_broadcast)
+ {
+ code +=
+R"_(
+ T_ELTWISE_BROADCAST_{{ELTWISE_OP}}_X({{DATA_TYPE}}, M0, N0, {{acc}}, operand_tile, {{acc}});
+)_";
+ }
+ else
+ {
+ code +=
+R"_(
+ T_ELTWISE_{{ELTWISE_OP}}({{DATA_TYPE}}, M0, N0, {{acc}}, operand_tile, {{acc}});
+)_";
+ }
+ code +=
+R"_(
+ }
+ //------------------ END KERNEL {{meta_kernel_id}} ELTWISE_OP ---------------------
+)_";
+ }
+
+ return code;
+}
+
+void ClTemplateElementwiseBinary::declare_variables(GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const
+{
+ vtable.declare_variable(
+ _lhs,
+ GpuKernelArgumentInfo(common_tensor_type),
+ comp_group.is_intermediate_tensor(_lhs),
+ "lhs");
+
+ vtable.declare_variable(
+ _rhs,
+ GpuKernelArgumentInfo(common_tensor_type),
+ comp_group.is_intermediate_tensor(_rhs),
+ "rhs");
+
+ vtable.declare_variable(
+ _dst,
+ GpuKernelArgumentInfo(common_tensor_type),
+ comp_group.is_intermediate_tensor(_dst),
+ "dst");
+}
+
+TagLUT ClTemplateElementwiseBinary::get_tag_lut(const GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const
+{
+ TagLUT lut{};
+ const ITensorInfo *accumulator = _lhs;
+ const ITensorInfo *operand = _rhs;
+
+ // Local build options
+ lut["meta_kernel_id"] = id();
+ lut["DATA_TYPE"] = get_cl_type_from_data_type(_lhs->data_type());
+ // Arguments and global shared variables
+ const bool is_root = (comp_group.get_root_component()->id() == this->id());
+ if(is_root)
+ {
+ lut["lhs"] = vtable.get_variable(_lhs);
+ lut["rhs"] = vtable.get_variable(_rhs);
+ lut["dst"] = vtable.get_variable(_dst);
+ }
+ else
+ {
+ // Determine which tensor is the accumulator
+ if(comp_group.is_intermediate_tensor(_lhs))
+ {
+ accumulator = _lhs;
+ operand = _rhs;
+ }
+ else if(comp_group.is_intermediate_tensor(_rhs))
+ {
+ accumulator = _rhs;
+ operand = _lhs;
+ }
+ else
+ {
+ ARM_COMPUTE_ERROR("Invalid elementwise component linking");
+ }
+ lut["acc"] = vtable.get_variable(accumulator);
+ lut["operand"] = vtable.get_variable(operand);
+ }
+ switch(_attributes.operation())
+ {
+ case Attributes::ElementwiseOp::ADD:
+ lut["ELTWISE_OP"] = "ADD";
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Arithmetic Operation not supported");
+ }
+ ARM_COMPUTE_ERROR_ON_MSG(detail::have_different_dimensions(accumulator->tensor_shape(), _dst->tensor_shape(), 0), "Only the operand can be broadcast to match the accumulator's shape");
+ const bool is_broadcast = (operand->tensor_shape() != _dst->tensor_shape());
+
+ // Set broadcast parameters
+ // PRE: All tensors are broadcast-compatible
+ if(is_broadcast)
+ {
+ // Note that n0 maps to input tensor dimension 0, m0 maps to input dimensions 1 and 2 because of our collapse strategy
+ if(operand->dimension(0) == 1U && operand->dimension(1) == 1U && operand->dimension(2) == 1U) // Broadcast in X, Y, Z: collapsed rhs win [M0xN0] = [1x1]
+ {
+ lut["rhs_m0"] = "1";
+ lut["rhs_n0"] = "1";
+ lut["rhs_start_ind_1"] = "0";
+ lut["rhs_start_ind_0"] = "0";
+ }
+ else if(operand->dimension(1) == 1U && operand->dimension(2) == 1U) // Broadcast in Y and Z: collapsed rhs win [M0xN0] = [1xN]
+ {
+ lut["rhs_m0"] = "1";
+ lut["rhs_n0"] = "N0";
+ lut["rhs_start_ind_1"] = "0";
+ lut["rhs_start_ind_0"] = "g_ind_0";
+ }
+ else
+ {
+ ARM_COMPUTE_ERROR("Only support rhs broadcasting in all X, Y, Z dimensions, or just in Y and Z dimensions");
+ }
+ }
+ else
+ {
+ lut["rhs_m0"] = "M0";
+ lut["rhs_n0"] = "N0";
+ lut["rhs_start_ind_1"] = "g_ind_1";
+ lut["rhs_start_ind_0"] = "g_ind_0";
+ }
+ return lut;
+}
+
+CLBuildOptions ClTemplateElementwiseBinary::get_build_options(const ComponentGroup &comp_group) const
+{
+ CLBuildOptions build_opts{};
+ /// NOTE: For now tile sizes (n0, m0) are set by the execution window. This may change in the future
+ const auto root_window = comp_group.get_root_component()->template_writer()->get_window();
+ const unsigned int n0 = root_window.x().step();
+ const unsigned int m0 = root_window.y().step();
+ const unsigned int partial_store_n0 = _dst->dimension(0) % n0;
+
+ build_opts.add_option("-DM0=" + support::cpp11::to_string(m0));
+ build_opts.add_option("-DN0=" + support::cpp11::to_string(n0));
+ build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(_lhs->data_type()));
+ build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0));
+
+ return build_opts;
+}
+
+std::string ClTemplateElementwiseBinary::get_config_id() const
+{
+ std::string config_id{};
+ config_id += lower_string(string_from_data_type(_dst->data_type()));
+ config_id += "_";
+ config_id += support::cpp11::to_string(_dst->dimension(0));
+ config_id += "_";
+ config_id += support::cpp11::to_string(_dst->dimension(1));
+ config_id += "_";
+ config_id += lower_string(string_from_data_layout(_dst->data_layout()));
+
+ return config_id;
+}
+
+std::set<std::string> ClTemplateElementwiseBinary::get_headers_list() const
+{
+ return std::set<std::string>{ "helpers.h", "tile_helpers.h" };
+}
+
+Window ClTemplateElementwiseBinary::get_window() const
+{
+ ARM_COMPUTE_ERROR_ON_MSG(_dst->tensor_shape().total_size() == 0U, "Destination tensor is not initialized");
+
+ TensorShape output_shape = _dst->tensor_shape();
+ // Collapse Dim 1 (W) and Dim 2 (H) together, leave Dim 0 (C) and upper dimensions unchanged
+ // This is in line with the collapsing convention used by operators like Conv2d
+ output_shape.collapse(2U, 1U);
+ const unsigned int num_elems_processed_per_iteration = adjust_vec_size(vector_size_byte_opencl / _dst->element_size(), _dst->dimension(0));
+ Window win = calculate_max_window(output_shape, Steps(num_elems_processed_per_iteration));
+
+ return win;
+}
+
+} // namespace dynamic_fusion
+} // namespace experimental
+} // namespace arm_compute