Integrate Dynamic Fusion patches
* Add public interfaces:
* OperatorGraph: Describe a workload that could contain fused kernels
* IWorkload: Generic interface for workloads built from OperatorGraph
* ClWorkload: OpenCL workloads built from OperatorGraph
* ClCompositeOperator: Runtime async operator to execute a ClWorkload
* DependencyGraph (will likely be deprecated in later iterations)
* Add example
* cl_fused_conv2d_elementwise_add.cpp to explain how to use the new
interfaces
* Add internal translation layer
* Refactor ClKernelBuildingAPI
* Remove non-tile based gemm native kernel component
* Minor interface changes
* Add integration tests
Resolves COMPMID-5161
Signed-off-by: SiCong Li <sicong.li@arm.com>
Change-Id: Ib987ed79289ab0bcbd3130d54f5793408d9f1240
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7510
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/tests/SConscript b/tests/SConscript
index 62fa4fc..87907f4 100644
--- a/tests/SConscript
+++ b/tests/SConscript
@@ -281,6 +281,20 @@
#-Wl,--allow-shlib-undefined: Ignore dependencies of dependencies
prog = test_env.Program(example, [ test_env.Object(source=file, target=example), graph_utils, graph_params]+ files_benchmark_examples, LIBS = test_env["LIBS"] + ["arm_compute_graph"], LINKFLAGS=test_env["LINKFLAGS"]+['-Wl,--allow-shlib-undefined'])
arm_compute_benchmark_examples += [ prog ]
+
+ # Dynamic fusion examples
+ if env['opencl']:
+ if env['experimental_dynamic_fusion']:
+ for file in Glob("%s/dynamic_fusion/*.cpp" % examples_folder):
+ example = "benchmark_" + os.path.basename(os.path.splitext(str(file))[0])
+ if env['os'] in ['android', 'macos', 'bare_metal'] or env['standalone']:
+ prog = test_env.Program(example, [ test_env.Object(source=file, target=example), graph_utils, graph_params]+ files_benchmark_examples, LIBS = test_env["LIBS"], LINKFLAGS=test_env["LINKFLAGS"]+[load_whole_archive, arm_compute_lib, noload_whole_archive] + bm_link_flags + extra_link_flags)
+ arm_compute_benchmark_examples += [ prog ]
+ else:
+ #-Wl,--allow-shlib-undefined: Ignore dependencies of dependencies
+ prog = test_env.Program(example, [ test_env.Object(source=file, target=example), graph_utils, graph_params]+ files_benchmark_examples, LIBS = test_env["LIBS"] + ["arm_compute_graph"], LINKFLAGS=test_env["LINKFLAGS"]+['-Wl,--allow-shlib-undefined'])
+ arm_compute_benchmark_examples += [ prog ]
+
arm_compute_benchmark_examples = install_bin(arm_compute_benchmark_examples)
Depends(arm_compute_benchmark_examples, arm_compute_test_framework)
Depends(arm_compute_benchmark_examples, arm_compute_lib)
diff --git a/tests/validation/CL/UNIT/dynamic_fusion/ClCompositeKernel.cpp b/tests/validation/CL/UNIT/dynamic_fusion/ClCompositeKernel.cpp
index 9e1b4d8..a6b09cc 100644
--- a/tests/validation/CL/UNIT/dynamic_fusion/ClCompositeKernel.cpp
+++ b/tests/validation/CL/UNIT/dynamic_fusion/ClCompositeKernel.cpp
@@ -21,9 +21,12 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#if defined(ENABLE_EXPERIMENTAL_DYNAMIC_FUSION)
+#ifndef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION
+#error "This experimental feature must be enabled with -DENABLE_EXPERIMENTAL_DYNAMIC_FUSION"
+#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
#include "src/gpu/cl/kernels/experimental/dynamic_fusion/ClCompositeKernel.h"
+#include "src/core/experimental/dynamic_fusion/ClKernelBuildingAPI.h"
#include "src/core/utils/helpers/float_ops.h"
#include "src/gpu/cl/kernels/ClElementwiseKernel.h"
@@ -42,9 +45,12 @@
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
+#include "tests/validation/CL/UNIT/dynamic_fusion/Utils.h"
+
#include <chrono>
using namespace arm_compute::experimental::dynamic_fusion;
+using namespace arm_compute::test::validation::utils;
namespace arm_compute
{
@@ -52,149 +58,12 @@
{
namespace validation
{
-namespace
-{
-/** Macros which measures the wall clock time, and records it into a map measurement_map with name clock_name */
-#define TICK(clock_name) \
- auto clock_name##_tick = std::chrono::high_resolution_clock::now();
-#define TOCK(clock_name, measurement_map) \
- auto clock_name##_tock = std::chrono::high_resolution_clock::now(); \
- measurement_map["\"" #clock_name "\""] = duration_cast<microseconds>(clock_name##_tock - clock_name##_tick);
-#define TOCK_AVG(clock_name, measurement_map, num_iterations) \
- auto clock_name##_tock = std::chrono::high_resolution_clock::now(); \
- measurement_map["\"" #clock_name "\""] = duration_cast<microseconds>((clock_name##_tock - clock_name##_tick) / (num_iterations));
-
-template <typename T, typename U>
-void fill(U &&tensor, int seed)
-{
- static_assert(std::is_floating_point<T>::value || std::is_same<T, half>::value, "Only floating point data types supported.");
- using DistributionType = typename std::conditional<std::is_same<T, half>::value, arm_compute::utils::uniform_real_distribution_16bit<T>, std::uniform_real_distribution<T>>::type;
-
- DistributionType distribution{ T(-1.0f), T(1.0f) };
- library->fill(tensor, distribution, seed);
-
- // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
- DistributionType distribution_inf{ T(std::numeric_limits<float>::infinity()), T(std::numeric_limits<float>::infinity()) };
- library->fill_borders_with_garbage(tensor, distribution_inf, seed);
-}
-} // namespace
-
TEST_SUITE(CL)
TEST_SUITE(UNIT)
TEST_SUITE(DYNAMIC_FUSION)
TEST_SUITE(ClCompositeKernel)
TEST_SUITE(Validate)
-TEST_CASE(MoveNet_SubGraph_1_Gemm, framework::DatasetMode::ALL)
-{
- /* Computation:
- * out = add(addend, gemm_native(lhs, rhs, bias)) (non-broadcast)
- */
- const auto data_type = DataType::F32;
- const auto m = 5U;
- const auto n = 4U;
- const auto k = 3U;
- const auto t_lhs_shape = TensorShape(k, m);
- const auto t_rhs_shape = TensorShape(n, k);
- const auto t_dst_shape = TensorShape(n, m);
- auto t_lhs_info = TensorInfo(t_lhs_shape, 1, data_type);
- auto t_rhs_info = TensorInfo(t_rhs_shape, 1, data_type);
- auto t_bias_info = TensorInfo(TensorShape(), 1, DataType::F32);
- auto t_dst_info = TensorInfo(t_dst_shape, 1, data_type);
-
- const ClTensorDescriptor t_lhs_desc{ &t_lhs_info };
- const ClTensorDescriptor t_rhs_desc{ &t_rhs_info };
- const ClTensorDescriptor t_bias_desc{ &t_bias_info };
- const ClTensorDescriptor t_addend_desc{ &t_dst_info };
- const ClTensorDescriptor t_dst_desc{ &t_dst_info };
-
- ClKernelBlueprint bp;
- ArgumentID tid_lhs;
- ArgumentID tid_rhs;
- ArgumentID tid_l0_bias = g_arg_placeholder;
- ArgumentID tid_l1_addend;
- ArgumentID tid_dst;
- auto st = add_tensor_argument(bp, t_lhs_desc, tid_lhs);
- st = add_tensor_argument(bp, t_rhs_desc, tid_rhs);
- st = add_tensor_argument(bp, t_addend_desc, tid_l1_addend);
- st = add_tensor_argument(bp, t_dst_desc, tid_dst);
-
- const auto common_kernel_desc = ClKernelComponentDescriptor{};
- const GemmNativeDescriptor gemm_native_desc{ 1.0, 1.0, m, n, k };
- const GEMMKernelInfo gemm_info{ m, n, k, 0, false, false, false, false, ActivationLayerInfo{}, 1, 1, gemm_native_desc.lhs_info, gemm_native_desc.rhs_info, 0, 0 };
- const EltwiseAddDescriptor eltwise_add_desc{ ConvertPolicy::WRAP };
- const TileDescriptor store_tile_info{ Size2D(gemm_info.rhs_info.n0, gemm_info.lhs_info.m0), Size2D(gemm_info.n, gemm_info.m), ClippingStrategy::TOP_LEFT };
-
- ArgumentID tid_acc;
- st = add_tensor_intermed(bp, tid_acc);
- st = add_kcomp_gemm_native(bp, common_kernel_desc, gemm_native_desc, tid_lhs, tid_rhs, tid_l0_bias, tid_acc);
- st = add_kcomp_eltwise_add(bp, common_kernel_desc, EltwiseAddDescriptor{}, tid_l1_addend, tid_acc, tid_acc);
- st = add_kcomp_store(bp, common_kernel_desc, tid_acc, tid_dst, StoreType::StoreBlockBoundaryAware);
-
- ClKernelCode cl_code;
-
- st = set_tile_info(bp, store_tile_info);
- st = build(cl_code, ClCodeBuilderContext{ GpuInfo{ GPUTarget::G71 } }, bp);
-
- ClExecutionDescriptor exec_desc{};
- st = tune_static(exec_desc, cl_code);
-
- CLScheduler::get().default_reinit();
- ClCompositeKernel kernel;
- kernel.configure(CLKernelLibrary::get().get_compile_context(), cl_code);
-
- // Construct tensors
- CLTensor t_lhs{};
- CLTensor t_rhs{};
- CLTensor t_l1_addend{};
- CLTensor t_dst{};
- // Init tensors
- {
- t_lhs.allocator()->init(t_lhs_info);
- t_rhs.allocator()->init(t_rhs_info);
- t_l1_addend.allocator()->init(t_dst_info);
- t_dst.allocator()->init(t_dst_info);
- }
- // "Pack" tensors
- TensorBinding tensors({ { tid_lhs, &t_lhs },
- { tid_rhs, &t_rhs },
- { tid_l1_addend, &t_l1_addend },
- { tid_dst, &t_dst }
- });
- // Allocate and fill tensors
- {
- t_lhs.allocator()->allocate();
- t_rhs.allocator()->allocate();
- t_l1_addend.allocator()->allocate();
- t_dst.allocator()->allocate();
- fill<float>(CLAccessor(t_lhs), 0);
- fill<float>(CLAccessor(t_rhs), 1);
- fill<float>(CLAccessor(t_l1_addend), 2);
- }
-
- CLScheduler::get().enqueue_op(kernel, tensors, exec_desc, true);
-
- // Create reference
- SimpleTensor<float> ref_t_lhs{ t_lhs_shape, data_type, 1 };
- SimpleTensor<float> ref_t_rhs{ t_rhs_shape, data_type, 1 };
- SimpleTensor<float> ref_t_bias_placeholder{ t_dst_shape, data_type, 1 };
- SimpleTensor<float> ref_t_l1_addend{ t_dst_shape, data_type, 1 };
-
- // Fill reference
- fill<float>(ref_t_lhs, 0);
- fill<float>(ref_t_rhs, 1);
- fill<float>(ref_t_l1_addend, 2);
- const auto ref_t_dst = reference::arithmetic_operation(
- ArithmeticOperation::ADD,
- ref_t_l1_addend,
- reference::gemm(ref_t_lhs, ref_t_rhs, ref_t_bias_placeholder, gemm_native_desc.alpha, 0.f /* To disable bias */),
- data_type,
- eltwise_add_desc.convert_policy);
-
- RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
- validate(CLAccessor(t_dst), ref_t_dst, tolerance_f32);
-}
-
TEST_CASE(MoveNet_SubGraph_1_DirectConv2d, framework::DatasetMode::ALL)
{
/* Computation:
@@ -208,7 +77,7 @@
Status st{};
const auto data_type = DataType::F32;
- const auto conv_info = PadStrideInfo(1U, 1U, 1U, 1U);
+ const auto conv_info = Conv2dDescriptor{ Padding2D{ 1U, 1U, 1U, 1U }, { 1U, 1U } /* stride */ };
const auto width = 7U;
const auto height = 6U;
@@ -216,47 +85,44 @@
const auto OFM = 4U;
const auto kernel_sz = 3U;
- const auto src_shape = TensorShape(IFM, width, height);
- const auto wei_shape = TensorShape(IFM, kernel_sz, kernel_sz, OFM);
- const auto bia_shape = TensorShape(OFM);
- const auto dst_shape = TensorShape(OFM, width, height);
+ const auto src_shape = TensorShape(IFM, width, height);
+ const auto wei_shape = TensorShape(IFM, kernel_sz, kernel_sz, OFM);
+ const auto bia_shape = TensorShape(OFM);
+ const auto addend_shape = TensorShape(1, 1);
+ const auto dst_shape = TensorShape(OFM, width, height);
- auto src_info = TensorInfo(src_shape, 1, data_type, DataLayout::NHWC);
- auto wei_info = TensorInfo(wei_shape, 1, data_type, DataLayout::NHWC);
- auto bia_info = TensorInfo(bia_shape, 1, data_type, DataLayout::NHWC);
- auto dst_info = TensorInfo(dst_shape, 1, data_type, DataLayout::NHWC);
-
- const auto src_desc = ClTensorDescriptor(&src_info);
- const auto wei_desc = ClTensorDescriptor(&wei_info);
- const auto bia_desc = ClTensorDescriptor(&bia_info);
- const auto addend_desc = ClTensorDescriptor(&dst_info);
- const auto dst_desc = ClTensorDescriptor(&dst_info);
+ auto src_info = TensorInfo(src_shape, 1, data_type, DataLayout::NHWC);
+ auto wei_info = TensorInfo(wei_shape, 1, data_type, DataLayout::NHWC);
+ auto bia_info = TensorInfo(bia_shape, 1, data_type, DataLayout::NHWC);
+ auto addend_info = TensorInfo(addend_shape, 1, data_type, DataLayout::NHWC);
+ auto dst_info = TensorInfo(dst_shape, 1, data_type, DataLayout::NHWC);
const auto n0 = std::min(OFM, 4u);
const auto m0 = (OFM > 16) ? ((data_type == DataType::F32) ? 2U : 4U) : 1U;
- const ClKernelComponentDescriptor common_kernel_desc{};
- const DirectConvolutionDescriptor direct_conv2d_desc{ conv_info };
- const EltwiseAddDescriptor eltwise_add_desc{ ConvertPolicy::WRAP };
- const TileDescriptor store_tile_info{ Size2D(n0, m0), Size2D(width, height), ClippingStrategy::TOP_LEFT };
+ const ClDirectConv2dKernelDescriptor direct_conv2d_desc{ conv_info };
+ const ClEltwiseAddKernelDescriptor eltwise_add_desc{};
+ const TileDescriptor store_tile_info{ Size2D(n0, m0), Size2D(width, height), ClippingStrategy::TOP_LEFT };
ArgumentID src_id{ g_arg_placeholder };
ArgumentID wei_id{ g_arg_placeholder };
ArgumentID bia_id{ g_arg_placeholder };
ArgumentID acc_id{ g_arg_placeholder };
+ ArgumentID acc_1_id{ g_arg_placeholder };
ArgumentID addend_id{ g_arg_placeholder };
ArgumentID dst_id{ g_arg_placeholder };
- st = add_tensor_argument(bp, src_desc, src_id);
- st = add_tensor_argument(bp, wei_desc, wei_id);
- st = add_tensor_argument(bp, bia_desc, bia_id);
- st = add_tensor_intermed(bp, acc_id);
- st = add_tensor_argument(bp, addend_desc, addend_id);
- st = add_tensor_argument(bp, dst_desc, dst_id);
+ st = add_tensor(bp, &src_info, src_id);
+ st = add_tensor(bp, &wei_info, wei_id);
+ st = add_tensor(bp, &bia_info, bia_id);
+ st = add_tensor(bp, &dst_info, acc_id);
+ st = add_tensor(bp, &dst_info, acc_1_id);
+ st = add_tensor(bp, &addend_info, addend_id);
+ st = add_tensor(bp, &dst_info, dst_id);
- st = add_kcomp_direct_conv(bp, common_kernel_desc, direct_conv2d_desc, src_id, wei_id, bia_id, acc_id);
- st = add_kcomp_eltwise_add(bp, common_kernel_desc, eltwise_add_desc, addend_id, acc_id, acc_id);
- st = add_kcomp_store(bp, common_kernel_desc, acc_id, dst_id, StoreType::TStoreIndirectWidthSelect);
+ st = add_kcomp_direct_conv2d(bp, direct_conv2d_desc, src_id, wei_id, bia_id, acc_id);
+ st = add_kcomp_eltwise_add(bp, eltwise_add_desc, addend_id, acc_id, acc_1_id);
+ st = add_kcomp_store(bp, StoreType::TStoreIndirectWidthSelect, acc_1_id, dst_id);
exec_desc.skip_sliding_window = true;
@@ -282,12 +148,11 @@
dst.allocator()->init(dst_info);
// "Pack" tensors
- TensorBinding tensors({ { src_id, &src },
+ ITensorPack tensors{ { src_id, &src },
{ wei_id, &wei },
{ bia_id, &bia },
{ addend_id, &addend },
- { dst_id, &dst }
- });
+ { dst_id, &dst } };
// Allocate and fill tensors
src.allocator()->allocate();
@@ -296,10 +161,10 @@
addend.allocator()->allocate();
dst.allocator()->allocate();
- fill<float>(CLAccessor(src), 0);
- fill<float>(CLAccessor(wei), 1);
- fill<float>(CLAccessor(bia), 2);
- fill<float>(CLAccessor(addend), 3);
+ fill<float>(CLAccessor(src), 0, library.get());
+ fill<float>(CLAccessor(wei), 1, library.get());
+ fill<float>(CLAccessor(bia), 2, library.get());
+ fill<float>(CLAccessor(addend), 3, library.get());
CLScheduler::get().enqueue_op(kernel, tensors, exec_desc, true);
@@ -310,10 +175,10 @@
SimpleTensor<float> ref_addend_nhwc{ dst_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC };
// Fill reference
- fill<float>(ref_src_nhwc, 0);
- fill<float>(ref_wei_nhwc, 1);
- fill<float>(ref_bia_nhwc, 2);
- fill<float>(ref_addend_nhwc, 3);
+ fill<float>(ref_src_nhwc, 0, library.get());
+ fill<float>(ref_wei_nhwc, 1, library.get());
+ fill<float>(ref_bia_nhwc, 2, library.get());
+ fill<float>(ref_addend_nhwc, 3, library.get());
auto ref_src = reference::permute(ref_src_nhwc, PermutationVector(1U, 2U, 0U));
auto ref_wei = reference::permute(ref_wei_nhwc, PermutationVector(1U, 2U, 0U));
@@ -326,301 +191,25 @@
const auto ref_dst = reference::arithmetic_operation(
ArithmeticOperation::ADD,
ref_addend,
- reference::convolution_layer<float>(ref_src, ref_wei, ref_bia, dst_shape_nchw, conv_info),
- data_type,
- eltwise_add_desc.convert_policy);
+ reference::convolution_layer<float>(ref_src, ref_wei, ref_bia, dst_shape_nchw,
+ PadStrideInfo
+ {
+ static_cast<unsigned int>(conv_info.stride.x()),
+ static_cast<unsigned int>(conv_info.stride.y()),
+ static_cast<unsigned int>(conv_info.pad.left),
+ static_cast<unsigned int>(conv_info.pad.top) }),
+ data_type,
+ ConvertPolicy::SATURATE);
RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
validate(CLAccessor(dst), ref_dst, tolerance_f32);
}
TEST_SUITE_END() // Validate
-
-TEST_SUITE(Benchmark)
-TEST_CASE(MoveNet_SubGraph_1_Gemm, framework::DatasetMode::ALL)
-{
- using std::chrono::duration_cast;
- using std::chrono::microseconds;
- const int num_iterations = 200;
- std::map<std::string, std::chrono::microseconds> measurements;
- /* Computation:
- * out = add(addend, gemm_native(lhs, rhs, bias))
- */
- const auto data_type = DataType::F32;
- const auto m = 12U * 12U;
- const auto n = 64U;
- const auto k = 384U;
- const auto t_lhs_shape = TensorShape(k, m);
- const auto t_rhs_shape = TensorShape(n, k);
- const auto t_dst_shape = TensorShape(n, m);
- auto t_lhs_info = TensorInfo(t_lhs_shape, 1, data_type);
- auto t_rhs_info = TensorInfo(t_rhs_shape, 1, data_type);
- auto t_bias_info = TensorInfo(TensorShape(), 1, data_type);
- auto t_l0_dst_info = TensorInfo(t_dst_shape, 1, data_type); // Intermediate tensor for cond3
- auto t_l1_rhs_info = TensorInfo(t_dst_shape, 1, data_type);
- auto t_dst_info = TensorInfo(t_dst_shape, 1, data_type);
-
- const auto common_kernel_desc = ClKernelComponentDescriptor{};
- const GemmNativeDescriptor gemm_native_desc{ 1.0, 0.0, m, n, k };
- const GEMMKernelInfo gemm_info{ m, n, k, 0, false, false, false, false, ActivationLayerInfo{}, 1, 1, gemm_native_desc.lhs_info, gemm_native_desc.rhs_info, 0, 0 };
- const EltwiseAddDescriptor eltwise_add_desc{ ConvertPolicy::WRAP };
- const TileDescriptor store_tile_info{ Size2D(gemm_info.rhs_info.n0, gemm_info.lhs_info.m0), Size2D(gemm_info.n, gemm_info.m), ClippingStrategy::TOP_LEFT };
-
- // Create reference
- SimpleTensor<float> ref_t_lhs{ t_lhs_shape, data_type, 1 };
- SimpleTensor<float> ref_t_rhs{ t_rhs_shape, data_type, 1 };
- SimpleTensor<float> ref_t_bias_placeholder{ t_dst_shape, data_type, 1 };
- SimpleTensor<float> ref_t_l1_addend{ t_dst_shape, data_type, 1 };
-
- // Fill reference
- fill<float>(ref_t_lhs, 0);
- fill<float>(ref_t_rhs, 1);
- fill<float>(ref_t_l1_addend, 2);
- const auto ref_t_dst = reference::arithmetic_operation(
- ArithmeticOperation::ADD,
- ref_t_l1_addend,
- reference::gemm(ref_t_lhs, ref_t_rhs, ref_t_bias_placeholder, gemm_native_desc.alpha, 0.f /* To disable bias */),
- data_type,
- eltwise_add_desc.convert_policy);
-
- CLScheduler::get().default_reinit();
-
- /* Condition 0: Dynamic Fused Kernel */
- CLTensor cond0_t_dst{};
- {
- TICK(cond0_0_startup_time);
-
- ClKernelBlueprint bp;
- ArgumentID tid_lhs;
- ArgumentID tid_rhs;
- ArgumentID tid_l0_bias = g_arg_placeholder;
- ArgumentID tid_l1_addend;
- ArgumentID tid_dst;
-
- const ClTensorDescriptor t_lhs_desc{ &t_lhs_info };
- const ClTensorDescriptor t_rhs_desc{ &t_rhs_info };
- const ClTensorDescriptor t_bias_desc{ &t_bias_info };
- const ClTensorDescriptor t_addend_desc{ &t_dst_info };
- const ClTensorDescriptor t_dst_desc{ &t_dst_info };
-
- ClKernelCode cl_code;
- TICK(cond0_build_time)
- auto st = add_tensor_argument(bp, t_lhs_desc, tid_lhs);
- st = add_tensor_argument(bp, t_rhs_desc, tid_rhs);
- st = add_tensor_argument(bp, t_addend_desc, tid_l1_addend);
- st = add_tensor_argument(bp, t_dst_desc, tid_dst);
-
- ArgumentID tid_acc;
- st = add_tensor_intermed(bp, tid_acc);
- st = add_kcomp_gemm_native(bp, common_kernel_desc, gemm_native_desc, tid_lhs, tid_rhs, tid_l0_bias, tid_acc);
-
- st = add_kcomp_eltwise_add(bp, common_kernel_desc, EltwiseAddDescriptor{}, tid_l1_addend, tid_acc, tid_acc);
-
- st = add_kcomp_store(bp, common_kernel_desc, tid_acc, tid_dst, StoreType::StoreBlockBoundaryAware);
-
- st = set_tile_info(bp, store_tile_info);
- st = build(cl_code, ClCodeBuilderContext{ GpuInfo{ GPUTarget::G71 } }, bp);
- TOCK(cond0_build_time, measurements)
-
- TICK(cond0_tune_time)
- ClExecutionDescriptor exec_desc{};
- st = tune_static(exec_desc, cl_code);
- TOCK(cond0_tune_time, measurements)
-
- TICK(cond0_configure_time)
- ClCompositeKernel kernel;
- kernel.configure(CLKernelLibrary::get().get_compile_context(), cl_code);
- TOCK(cond0_configure_time, measurements)
-
- // Construct tensors
- CLTensor t_lhs{};
- CLTensor t_rhs{};
- CLTensor t_l1_addend{};
-
- // Init tensors
- {
- t_lhs.allocator()->init(t_lhs_info);
- t_rhs.allocator()->init(t_rhs_info);
- t_l1_addend.allocator()->init(t_dst_info);
- cond0_t_dst.allocator()->init(t_dst_info);
- }
- // Allocate tensors
- {
- t_lhs.allocator()->allocate();
- t_rhs.allocator()->allocate();
- t_l1_addend.allocator()->allocate();
- cond0_t_dst.allocator()->allocate();
- fill<float>(CLAccessor(t_lhs), 0);
- fill<float>(CLAccessor(t_rhs), 1);
- fill<float>(CLAccessor(t_l1_addend), 2);
- }
-
- // "Pack" tensors
- TensorBinding tensors({ { tid_lhs, &t_lhs }, { tid_rhs, &t_rhs }, { tid_l1_addend, &t_l1_addend }, { tid_dst, &cond0_t_dst } });
-
- CLScheduler::get().enqueue_op(kernel, tensors, exec_desc, true);
- CLScheduler::get().sync();
- TOCK(cond0_0_startup_time, measurements)
-
- TICK(cond0_1_latency)
- for(int i = 0; i < num_iterations; ++i)
- {
- CLScheduler::get().enqueue_op(kernel, tensors, exec_desc, true);
- }
- CLScheduler::get().sync();
- TOCK_AVG(cond0_1_latency, measurements, num_iterations)
- }
- /* Condition 1: Dynamic Unfused Kernel */
- /* Condition 2: Static Fused Kernel (current) */
- CLTensor cond2_t_dst{};
- {
- TICK(cond2_0_startup_time);
- arm_compute::opencl::kernels::ClGemmMatrixMultiplyNativeKernel l0_gemm_mm;
-
- TICK(cond2_configure_time);
- experimental::PostOpList<ITensorInfo *> post_ops;
- post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>(&t_dst_info, 1, eltwise_add_desc.convert_policy);
- GEMMKernelInfo gemm_info{ m, n, k, 0, false, false, false, false, ActivationLayerInfo{}, 1, 1, gemm_native_desc.lhs_info, gemm_native_desc.rhs_info, 0, 0, post_ops };
- l0_gemm_mm.configure(CLKernelLibrary::get().get_compile_context(), &t_lhs_info, &t_rhs_info, nullptr, &t_dst_info, gemm_native_desc.alpha, gemm_native_desc.beta, gemm_native_desc.lhs_info,
- gemm_native_desc.rhs_info, gemm_info);
- TOCK(cond2_configure_time, measurements);
-
- // Construct tensors
- CLTensor t_lhs{};
- CLTensor t_rhs{};
- CLTensor t_l1_addend{};
-
- // Init tensors
- {
- t_lhs.allocator()->init(t_lhs_info);
- t_rhs.allocator()->init(t_rhs_info);
- t_l1_addend.allocator()->init(t_dst_info);
- cond2_t_dst.allocator()->init(t_dst_info);
- }
- // Allocate tensors
- {
- t_lhs.allocator()->allocate();
- t_rhs.allocator()->allocate();
- t_l1_addend.allocator()->allocate();
- cond2_t_dst.allocator()->allocate();
- fill<float>(CLAccessor(t_lhs), 0);
- fill<float>(CLAccessor(t_rhs), 1);
- fill<float>(CLAccessor(t_l1_addend), 2);
- }
-
- // "Pack" tensors
- ITensorPack tensors
- {
- { ACL_SRC_0, &t_lhs },
- { ACL_SRC_1, &t_rhs },
- { EXPERIMENTAL_ACL_POST_OP_ARG_FIRST, &t_l1_addend },
- { ACL_DST, &cond2_t_dst },
- };
- CLScheduler::get().enqueue_op(l0_gemm_mm, tensors, true);
- CLScheduler::get().sync();
- TOCK(cond2_0_startup_time, measurements);
-
- TICK(cond2_1_latency);
- for(int i = 0; i < num_iterations; ++i)
- {
- CLScheduler::get().enqueue_op(l0_gemm_mm, tensors, true);
- }
- CLScheduler::get().sync();
- TOCK_AVG(cond2_1_latency, measurements, num_iterations);
- }
- /* Condition 3: Static Unfused Kernel (current) */
- CLTensor cond3_t_dst{};
- {
- TICK(cond3_0_startup_time);
- arm_compute::opencl::kernels::ClGemmMatrixMultiplyNativeKernel l0_gemm_mm;
- arm_compute::opencl::kernels::ClSaturatedArithmeticKernel l1_add;
-
- TICK(cond3_configure_time);
- GEMMKernelInfo gemm_info{ m, n, k, 0, false, false, false, false, ActivationLayerInfo{}, 1, 1, gemm_native_desc.lhs_info, gemm_native_desc.rhs_info, 0, 0 };
- l0_gemm_mm.configure(CLKernelLibrary::get().get_compile_context(), &t_lhs_info, &t_rhs_info, nullptr, &t_l0_dst_info, gemm_native_desc.alpha, gemm_native_desc.beta, gemm_native_desc.lhs_info,
- gemm_native_desc.rhs_info, gemm_info);
- l1_add.configure(CLKernelLibrary::get().get_compile_context(), ArithmeticOperation::ADD, &t_l0_dst_info, &t_l1_rhs_info, &t_dst_info, eltwise_add_desc.convert_policy);
- TOCK(cond3_configure_time, measurements);
-
- // Construct tensors
- CLTensor t_lhs{};
- CLTensor t_rhs{};
- CLTensor t_l0_dst{};
- CLTensor t_l1_addend{};
-
- // Init tensors
- {
- t_lhs.allocator()->init(t_lhs_info);
- t_rhs.allocator()->init(t_rhs_info);
- t_l0_dst.allocator()->init(t_l0_dst_info);
- t_l1_addend.allocator()->init(t_dst_info);
- cond3_t_dst.allocator()->init(t_dst_info);
- }
- // Allocate tensors
- {
- t_lhs.allocator()->allocate();
- t_rhs.allocator()->allocate();
- t_l0_dst.allocator()->allocate();
- t_l1_addend.allocator()->allocate();
- cond3_t_dst.allocator()->allocate();
- fill<float>(CLAccessor(t_lhs), 0);
- fill<float>(CLAccessor(t_rhs), 1);
- fill<float>(CLAccessor(t_l1_addend), 2);
- }
-
- // "Pack" tensors
- ITensorPack tensors_l0
- {
- { ACL_SRC_0, &t_lhs },
- { ACL_SRC_1, &t_rhs },
- { ACL_DST, &t_l0_dst },
- };
- ITensorPack tensors_l1
- {
- { ACL_SRC_0, &t_l0_dst },
- { ACL_SRC_1, &t_l1_addend },
- { ACL_DST, &cond3_t_dst },
- };
- CLScheduler::get().enqueue_op(l0_gemm_mm, tensors_l0, true);
- CLScheduler::get().enqueue_op(l1_add, tensors_l1, true);
- CLScheduler::get().sync();
- TOCK(cond3_0_startup_time, measurements);
-
- TICK(cond3_1_latency);
- for(int i = 0; i < num_iterations; ++i)
- {
- CLScheduler::get().enqueue_op(l0_gemm_mm, tensors_l0, true);
- CLScheduler::get().enqueue_op(l1_add, tensors_l1, true);
- }
- CLScheduler::get().sync();
- TOCK_AVG(cond3_1_latency, measurements, num_iterations);
- }
-
- RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
- std::cout << "cond0 validation: " << std::endl;
- validate(CLAccessor(cond0_t_dst), ref_t_dst, tolerance_f32);
- std::cout << "cond2 validation: " << std::endl;
- validate(CLAccessor(cond2_t_dst), ref_t_dst, tolerance_f32);
- std::cout << "cond3 validation: " << std::endl;
- validate(CLAccessor(cond3_t_dst), ref_t_dst, tolerance_f32);
-
- /* Report */
- std::cout << "Performance comparison (gemm native + add)" << std::endl;
- std::cout << "cond0: dynamic fusion module" << std::endl;
- std::cout << "cond2: static fused with post ops" << std::endl;
- std::cout << "cond3: static unfused" << std::endl;
- for(auto m : measurements)
- {
- std::cout << m.first << ": " << m.second.count() << "us" << std::endl;
- }
-}
-TEST_SUITE_END() // Benchmark
TEST_SUITE_END() // ClCompositeKernel
TEST_SUITE_END() // DYNAMIC_FUSION
TEST_SUITE_END() // UNIT
TEST_SUITE_END() // CL
} // namespace validation
} // namespace test
-} // namespace arm_compute
-
-#endif // defined(ENABLE_EXPERIMENTAL_DYNAMIC_FUSION)
\ No newline at end of file
+} // namespace arm_compute
\ No newline at end of file
diff --git a/tests/validation/CL/UNIT/dynamic_fusion/DependencyGraph.cpp b/tests/validation/CL/UNIT/dynamic_fusion/DependencyGraph.cpp
new file mode 100644
index 0000000..6962f0e
--- /dev/null
+++ b/tests/validation/CL/UNIT/dynamic_fusion/DependencyGraph.cpp
@@ -0,0 +1,267 @@
+/*
+ * 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.
+ */
+#ifndef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION
+#error "This experimental feature must be enabled with -DENABLE_EXPERIMENTAL_DYNAMIC_FUSION"
+#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
+#include "arm_compute/core/experimental/DependencyGraph.h"
+
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Macros.h"
+
+using namespace arm_compute::experimental::dynamic_fusion;
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+TEST_SUITE(CL)
+
+TEST_SUITE(UNIT)
+TEST_SUITE(DYNAMIC_FUSION)
+TEST_SUITE(DependencyGraph)
+
+TEST_CASE(Correct_Graph_Creation_Should_Pass, framework::DatasetMode::ALL)
+{
+ DependencyGraph graph{};
+ const auto t0 = graph.add_tensor();
+ const auto t1 = graph.add_tensor();
+ const auto t2 = graph.add_tensor();
+ const auto t3 = graph.add_tensor();
+ const auto t4 = graph.add_tensor();
+
+ const auto o0 = graph.add_operator({ t0, t1 }, { t2 }).second;
+ const auto o1 = graph.add_operator({ t3, t2 }, { t4 }).second;
+
+ ARM_COMPUTE_EXPECT_EQUAL(graph.number_of_ops(), 2U, framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT_EQUAL(graph.number_of_tensors(), 5U, framework::LogLevel::ERRORS);
+
+ const DependencyGraph ref_graph
+ {
+ {
+ // src_tensors
+ { o0, { t0, t1 } },
+ { o1, { t3, t2 } },
+ },
+ {
+ // dst_tensors
+ { o0, { t2 } },
+ { o1, { t4 } },
+ },
+ {
+ // src_ops
+ { t0, {} },
+ { t1, {} },
+ { t2, { o0 } },
+ { t3, {} },
+ { t4, { o1 } },
+ },
+ {
+ // dst_ops
+ { t0, { o0 } },
+ { t1, { o0 } },
+ { t2, { o1 } },
+ { t3, { o1 } },
+ { t4, {} },
+ }
+
+ };
+ ARM_COMPUTE_EXPECT(graph == ref_graph, framework::LogLevel::ERRORS);
+}
+
+TEST_CASE(Correct_Merge_Points_Should_Enable_Graph_Expansion, framework::DatasetMode::ALL)
+{
+ // Merge points are a simple way to collapse "graph of graphs" into a single graph
+ // Suppose we have a top-level graph g0
+ DependencyGraph g0{};
+ const auto g0_t0 = g0.add_tensor();
+ const auto g0_t1 = g0.add_tensor();
+ const auto g0_t2 = g0.add_tensor();
+ const auto g0_t3 = g0.add_tensor();
+ const auto g0_t4 = g0.add_tensor();
+ g0.add_operator({ g0_t0, g0_t1 }, { g0_t2 }); // g0_o0
+ g0.add_operator({ g0_t3, g0_t2 }, { g0_t4 }); // g0_o1
+
+ // Then g0 expands into g1, with additional nodes added in-between "merge point tensors"
+ // Note that the expansion logic may be local to each operator node
+ DependencyGraph g1{};
+ // g0_o0 expands into g1_o0, g1_o1, g1_o2
+ const auto g1_t0 = g1.add_tensor(g0_t0);
+ const auto g1_t1 = g1.add_tensor(g0_t1);
+ const auto g1_t2 = g1.add_tensor();
+ const auto g1_t3 = g1.add_tensor();
+ const auto g1_t4 = g1.add_tensor(g0_t2);
+ const auto g1_o0 = g1.add_operator({ g1_t0 }, { g1_t2 }).second;
+ const auto g1_o1 = g1.add_operator({ g1_t1 }, { g1_t3 }).second;
+ const auto g1_o2 = g1.add_operator({ g1_t2, g1_t3 }, { g1_t4 }).second;
+
+ // g0_o1 expands into g1_o3
+ const auto g1_t5 = g1.add_tensor(g0_t3);
+ const auto g1_t6 = g1.add_tensor(g0_t2);
+ const auto g1_t7 = g1.add_tensor(g0_t4);
+ ARM_COMPUTE_EXPECT_EQUAL(g1_t4, g1_t6, framework::LogLevel::ERRORS); // both associate with the same merge point g0_t2, thus they should point to the same tensor in g1
+ const auto g1_o3 = g1.add_operator({ g1_t5, g1_t6 }, { g1_t7 }).second;
+
+ const DependencyGraph ref_graph
+ {
+ {
+ // src_tensors
+ { g1_o0, { g1_t0 } },
+ { g1_o1, { g1_t1 } },
+ { g1_o2, { g1_t2, g1_t3 } },
+ { g1_o3, { g1_t5, g1_t4 } },
+ },
+ {
+ // dst_tensors
+ { g1_o0, { g1_t2 } },
+ { g1_o1, { g1_t3 } },
+ { g1_o2, { g1_t4 } },
+ { g1_o3, { g1_t7 } },
+ },
+ {
+ // src_ops
+ { g1_t0, {} },
+ { g1_t1, {} },
+ { g1_t2, { g1_o0 } },
+ { g1_t3, { g1_o1 } },
+ { g1_t4, { g1_o2 } },
+ { g1_t5, {} },
+ { g1_t7, { g1_o3 } },
+ },
+ {
+ // dst_ops
+ { g1_t0, { g1_o0 } },
+ { g1_t1, { g1_o1 } },
+ { g1_t2, { g1_o2 } },
+ { g1_t3, { g1_o2 } },
+ { g1_t4, { g1_o3 } },
+ { g1_t5, { g1_o3 } },
+ { g1_t7, {} },
+ },
+ {
+ // merge points
+ { g0_t0, g1_t0 },
+ { g0_t1, g1_t1 },
+ { g0_t2, g1_t4 },
+ { g0_t3, g1_t5 },
+ { g0_t4, g1_t7 },
+ }
+ };
+ ARM_COMPUTE_EXPECT(g1 == ref_graph, framework::LogLevel::ERRORS);
+}
+
+TEST_CASE(Path_Existence_Check_0, framework::DatasetMode::ALL)
+{
+ DependencyGraph graph{};
+ const auto t0 = graph.add_tensor();
+ const auto t1 = graph.add_tensor();
+ const auto t2 = graph.add_tensor();
+ const auto t3 = graph.add_tensor();
+ const auto t4 = graph.add_tensor();
+ const auto t5 = graph.add_tensor();
+ const auto t6 = graph.add_tensor();
+ const auto t7 = graph.add_tensor();
+ const auto o0 = graph.add_operator({ t1 }, { t3, t4 }).second;
+ const auto o1 = graph.add_operator({ t3 }, { t5 }).second;
+ const auto o2 = graph.add_operator({ t5, t6 }, { t7 }).second;
+ const auto o3 = graph.add_operator({ t4 }, { t6 }).second;
+ const auto o4 = graph.add_operator({ t0, t5 }, { t2 }).second;
+
+ ARM_COMPUTE_UNUSED(o1, o3);
+
+ ARM_COMPUTE_EXPECT((graph.path_exists_from_tensor_to_op(t3, o2)), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT((graph.path_exists_from_tensor_to_op(t1, o4)), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!(graph.path_exists_from_tensor_to_op(t2, o4)), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!(graph.path_exists_from_tensor_to_op(t0, o2)), framework::LogLevel::ERRORS);
+
+ ARM_COMPUTE_EXPECT((graph.path_exists_from_op_to_op(o0, o2)), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!(graph.path_exists_from_op_to_op(o2, o0)), framework::LogLevel::ERRORS);
+
+ ARM_COMPUTE_EXPECT(!(graph.path_exists_from_op_to_op(o2, o4)), framework::LogLevel::ERRORS);
+}
+
+TEST_CASE(Correct_Topological_Sort_Should_Pass, framework::DatasetMode::ALL)
+{
+ DependencyGraph graph{};
+ const auto t0 = graph.add_tensor();
+ const auto t1 = graph.add_tensor();
+ const auto t2 = graph.add_tensor();
+ const auto t3 = graph.add_tensor();
+ const auto t4 = graph.add_tensor();
+ const auto t5 = graph.add_tensor();
+ const auto t6 = graph.add_tensor();
+ const auto t7 = graph.add_tensor();
+ const auto o0 = graph.add_operator({ t1 }, { t3, t4 }).second;
+ const auto o1 = graph.add_operator({ t3 }, { t5 }).second;
+ const auto o2 = graph.add_operator({ t5, t6 }, { t7 }).second;
+ const auto o3 = graph.add_operator({ t4 }, { t6 }).second;
+ const auto o4 = graph.add_operator({ t0, t5 }, { t2 }).second;
+
+ const auto res = graph.topological_sort();
+ ARM_COMPUTE_EXPECT(bool(res.first), framework::LogLevel::ERRORS);
+ std::vector<DependencyGraph::OpPack> ref_sorted_op_packs
+ {
+ { o0, { t1 }, { t3, t4 } },
+ { o1, { t3 }, { t5 } },
+ { o3, { t4 }, { t6 } },
+ { o4, { t0, t5 }, { t2 } },
+ { o2, { t5, t6 }, { t7 } },
+
+ };
+ ARM_COMPUTE_EXPECT((res.second == ref_sorted_op_packs), framework::LogLevel::ERRORS);
+}
+
+TEST_CASE(Cycles_Should_Fail, framework::DatasetMode::ALL)
+{
+ DependencyGraph graph{};
+ const auto t0 = graph.add_tensor();
+ const auto t1 = graph.add_tensor();
+ const auto t2 = graph.add_tensor();
+ const auto t3 = graph.add_tensor();
+
+ graph.add_operator({ t0, t1 }, { t2 });
+ graph.add_operator({ t2 }, { t1, t3 }); // Ideally error should occur here
+
+ const auto res = graph.topological_sort();
+ ARM_COMPUTE_EXPECT(!bool(res.first), framework::LogLevel::ERRORS);
+}
+TEST_CASE(Loops_Should_Fail, framework::DatasetMode::ALL)
+{
+ DependencyGraph graph{};
+ const auto t0 = graph.add_tensor();
+ const auto t1 = graph.add_tensor();
+ const auto t2 = graph.add_tensor();
+
+ ARM_COMPUTE_EXPECT_THROW(graph.add_operator({ t0, t2 }, { t1, t2 }).first, framework::LogLevel::ERRORS);
+ ARM_COMPUTE_UNUSED(t0, t1, t2);
+}
+TEST_SUITE_END() // DependencyGraph
+TEST_SUITE_END() // DYNAMIC_FUSION
+TEST_SUITE_END() // UNIT
+
+TEST_SUITE_END() // CL
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
\ No newline at end of file
diff --git a/tests/validation/CL/UNIT/dynamic_fusion/Integration_OperatorFuseMovenetSubGraph1.cpp b/tests/validation/CL/UNIT/dynamic_fusion/Integration_OperatorFuseMovenetSubGraph1.cpp
new file mode 100644
index 0000000..1b04b0c
--- /dev/null
+++ b/tests/validation/CL/UNIT/dynamic_fusion/Integration_OperatorFuseMovenetSubGraph1.cpp
@@ -0,0 +1,403 @@
+/*
+ * 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.
+ */
+
+#ifndef ENABLE_EXPERIMENTAL_DYNAMIC_FUSION
+#error "This experimental feature must be enabled with -DENABLE_EXPERIMENTAL_DYNAMIC_FUSION"
+#endif /* ENABLE_EXPERIMENTAL_DYNAMIC_FUSION */
+#include "arm_compute/core/TensorInfo.h"
+
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/experimental/ClWorkload.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+#include "arm_compute/runtime/experimental/ClCompositeOperator.h"
+#include "src/core/experimental/dynamic_fusion/WorkloadImpl/ClKernelDescriptors.h"
+#include "src/gpu/cl/operators/ClAdd.h"
+#include "src/gpu/cl/operators/ClConv2d.h"
+#include "tests/CL/CLAccessor.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Macros.h"
+#include "tests/validation/CL/UNIT/dynamic_fusion/Utils.h"
+#include "tests/validation/Validation.h"
+
+#include "tests/validation/reference/ConvolutionLayer.h"
+#include "tests/validation/reference/ElementwiseOperations.h"
+#include "tests/validation/reference/Permute.h"
+
+#ifdef ARM_COMPUTE_ASSERTS_ENABLED
+#include "tests/SimpleTensorPrinter.h"
+#endif /* ARM_COMPUTE_ASSERTS_ENABLED */
+
+using namespace arm_compute::experimental::dynamic_fusion;
+using namespace arm_compute::test::validation::utils;
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+TEST_SUITE(CL)
+TEST_SUITE(INTEGRATION)
+TEST_SUITE(DYNAMIC_FUSION)
+TEST_CASE(Operator_Fuse_Movenet_SubGraph_1_F32, framework::DatasetMode::ALL)
+{
+ // Please refer to: https://confluence.arm.com/pages/viewpage.action?pageId=886243697
+ /* Computation:
+ * out = add_desc(addend, conv2d1x1(direct_conv)(input, weights, bias))
+ */
+ const auto data_type = DataType::F32;
+ const auto data_layout = DataLayout::NHWC;
+ const auto t_input_shape = TensorShape(384, 12, 12);
+ // const auto t_weight_shape = TensorShape(384, 1, 1, 64);
+ // const auto t_dst_shape = TensorShape(64, 12, 12);
+ const auto t_weight_shape = TensorShape(384, 1, 1, 16);
+ const auto t_dst_shape = TensorShape(16, 12, 12);
+ auto t_input_info = TensorInfo(t_input_shape, 1, data_type, data_layout);
+ auto t_weight_info = TensorInfo(t_weight_shape, 1, data_type, data_layout);
+ auto t_l1_addend_info = TensorInfo(t_dst_shape, 1, data_type, data_layout);
+ auto t_acc_info = TensorInfo(); // Intermediate tensor for cond3
+ auto t_dst_info = TensorInfo();
+
+ Conv2dDescriptor conv2d_desc{};
+ AddDescriptor add_desc{};
+
+ // Create reference
+ SimpleTensor<float> ref_t_input{ t_input_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC };
+ SimpleTensor<float> ref_t_weight{ t_weight_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC };
+ SimpleTensor<float> ref_t_bias_placeholder{ t_dst_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC };
+ SimpleTensor<float> ref_t_l1_addend{ t_dst_shape, data_type, 1, QuantizationInfo(), DataLayout::NHWC };
+
+ // Fill reference
+ fill<float>(ref_t_input, 0, library.get());
+ fill<float>(ref_t_weight, 1, library.get());
+ fill<float>(ref_t_l1_addend, 2, library.get());
+
+ auto ref_t_input_nchw = reference::permute(ref_t_input, PermutationVector(1U, 2U, 0U));
+ auto ref_t_weight_nchw = reference::permute(ref_t_weight, PermutationVector(1U, 2U, 0U));
+ auto ref_t_bias_placeholder_nchw = reference::permute(ref_t_bias_placeholder, PermutationVector(1U, 2U, 0U));
+ auto ref_t_l1_addend_nchw = reference::permute(ref_t_l1_addend, PermutationVector(1U, 2U, 0U));
+ auto t_dst_shape_nchw = t_dst_shape;
+ permute(t_dst_shape_nchw, PermutationVector(1U, 2U, 0U));
+
+ PadStrideInfo legacy_pad_stride(conv2d_desc.stride.x(), conv2d_desc.stride.y(), conv2d_desc.pad.left, conv2d_desc.pad.right, conv2d_desc.pad.top, conv2d_desc.pad.bottom, DimensionRoundingType{});
+ auto ref_t_dst_nchw = reference::arithmetic_operation(
+ ArithmeticOperation::ADD,
+ ref_t_l1_addend_nchw,
+ reference::convolution_layer(ref_t_input_nchw, ref_t_weight_nchw, ref_t_bias_placeholder_nchw, t_dst_shape_nchw, legacy_pad_stride, conv2d_desc.dilation),
+ data_type,
+ ConvertPolicy{});
+ const auto ref_t_dst = reference::permute(ref_t_dst_nchw, PermutationVector(2U, 0U, 1U));
+
+ CLScheduler::get().default_reinit();
+ const auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
+ OperatorGraph op_graph;
+
+ const auto op_t_input = add_tensor(op_graph, t_input_info);
+ const auto op_t_weight = add_tensor(op_graph, t_weight_info);
+ const auto op_t_l1_addend = add_tensor(op_graph, t_l1_addend_info);
+ const auto op_t_acc = add_tensor(op_graph, t_acc_info); // temp accumulator; TensorInfo to be inferred
+ const auto op_t_dst = add_tensor(op_graph, t_dst_info);
+
+ auto conv2d = add_op_conv2d(op_graph, conv2d_desc, op_t_input, op_t_weight, op_t_acc);
+ force_conv2d_method(op_graph, conv2d, ConvolutionMethod::DIRECT);
+ add_op_elementwise_add(op_graph, add_desc, op_t_acc, op_t_l1_addend, op_t_dst);
+
+ const ClWorkloadContext workload_ctx{ GpuInfo{ CLScheduler::get().target() } };
+ ClWorkload workload;
+ build(workload, op_graph, workload_ctx);
+
+ ClCompositeOperator op;
+ op.configure(cl_compile_ctx, workload);
+
+ // Construct tensors
+ CLTensor t_input{};
+ CLTensor t_weight{};
+ CLTensor t_l1_addend{};
+ CLTensor t_dst{};
+
+ // Init tensors
+ t_input.allocator()->init(t_input_info);
+ t_weight.allocator()->init(t_weight_info);
+ t_l1_addend.allocator()->init(t_dst_info);
+ t_dst.allocator()->init(t_dst_info);
+
+ // Allocate and fill tensors
+ t_input.allocator()->allocate();
+ t_weight.allocator()->allocate();
+ t_l1_addend.allocator()->allocate();
+ t_dst.allocator()->allocate();
+ fill<float>(CLAccessor(t_input), 0, library.get());
+ fill<float>(CLAccessor(t_weight), 1, library.get());
+ fill<float>(CLAccessor(t_l1_addend), 2, library.get());
+ // "Pack" tensors
+ OpTensorBinding bp_tensors({ { op_t_input, &t_input },
+ { op_t_weight, &t_weight },
+ { op_t_l1_addend, &t_l1_addend },
+ { op_t_dst, &t_dst }
+ });
+
+ // Populate prepare and run pack-maps (including allocating aux tensors)
+ ClAuxTensorData aux_tensor_data{};
+ TensorPackMap prepare_pack_map{};
+ TensorPackMap run_pack_map{};
+ bind_tensors(aux_tensor_data, prepare_pack_map, run_pack_map, workload, bp_tensors);
+
+ op.prepare(prepare_pack_map);
+ op.run(run_pack_map);
+ RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
+ validate(CLAccessor(t_dst), ref_t_dst_nchw, tolerance_f32);
+}
+TEST_SUITE(Unsupported)
+TEST_CASE(DataType_QASYMM8, framework::DatasetMode::ALL)
+{
+ const auto data_type = DataType::QASYMM8;
+ const auto data_layout = DataLayout::NHWC;
+ const auto t_input_shape = TensorShape(384, 12, 12);
+ const auto t_weight_shape = TensorShape(384, 1, 1, 64);
+ const auto t_dst_shape = TensorShape(64, 12, 12);
+ auto t_input_info = TensorInfo(t_input_shape, 1, data_type, data_layout);
+ auto t_weight_info = TensorInfo(t_weight_shape, 1, data_type, data_layout);
+ auto t_l1_addend_info = TensorInfo(t_dst_shape, 1, data_type, data_layout);
+ auto t_acc_info = TensorInfo(t_dst_shape, 1, data_type, data_layout);
+ auto t_dst_info = TensorInfo(t_dst_shape, 1, data_type, data_layout);
+
+ Conv2dDescriptor conv2d_desc{};
+ AddDescriptor add_desc{};
+
+ OperatorGraph op_graph;
+
+ const auto op_t_input = add_tensor(op_graph, t_input_info);
+ const auto op_t_weight = add_tensor(op_graph, t_weight_info);
+ const auto op_t_l1_addend = add_tensor(op_graph, t_l1_addend_info);
+ const auto op_t_acc = add_tensor(op_graph, t_acc_info); // temp accumulator; TensorInfo to be inferred
+ const auto op_t_dst = add_tensor(op_graph, t_dst_info);
+
+ auto conv2d = add_op_conv2d(op_graph, conv2d_desc, op_t_input, op_t_weight, op_t_acc);
+ add_op_elementwise_add(op_graph, add_desc, op_t_acc, op_t_l1_addend, op_t_dst);
+ force_conv2d_method(op_graph, conv2d, ConvolutionMethod::DIRECT);
+
+ const ClWorkloadContext workload_ctx{ GpuInfo{ CLScheduler::get().target() } };
+ ClWorkload workload;
+ const auto success = build(workload, op_graph, workload_ctx);
+
+ ARM_COMPUTE_EXPECT(!bool(success), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!bool(ClCompositeOperator::validate(workload)), framework::LogLevel::ERRORS);
+}
+TEST_CASE(DataLayout_NCHW, framework::DatasetMode::ALL)
+{
+ const auto data_type = DataType::F32;
+ const auto data_layout = DataLayout::NCHW;
+ const auto t_input_shape = TensorShape(384, 12, 12);
+ const auto t_weight_shape = TensorShape(384, 1, 1, 64);
+ const auto t_dst_shape = TensorShape(64, 12, 12);
+ auto t_input_info = TensorInfo(t_input_shape, 1, data_type, data_layout);
+ auto t_weight_info = TensorInfo(t_weight_shape, 1, data_type, data_layout);
+ auto t_dst_info = TensorInfo(t_dst_shape, 1, data_type, data_layout);
+
+ Conv2dDescriptor conv2d_desc{};
+
+ OperatorGraph op_graph;
+
+ const auto op_t_input = add_tensor(op_graph, t_input_info);
+ const auto op_t_weight = add_tensor(op_graph, t_weight_info);
+ const auto op_t_dst = add_tensor(op_graph, t_dst_info);
+
+ auto conv2d = add_op_conv2d(op_graph, conv2d_desc, op_t_input, op_t_weight, op_t_dst);
+ force_conv2d_method(op_graph, conv2d, ConvolutionMethod::DIRECT);
+ const ClWorkloadContext workload_ctx{ GpuInfo{ CLScheduler::get().target() } };
+ ClWorkload workload;
+ const auto success = build(workload, op_graph, workload_ctx);
+
+ ARM_COMPUTE_EXPECT(!bool(success), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!bool(ClCompositeOperator::validate(workload)), framework::LogLevel::ERRORS);
+}
+TEST_SUITE_END() // Unsupported
+
+TEST_SUITE(Invalid)
+TEST_CASE(Multiple_Complex_Ops_0, framework::DatasetMode::ALL)
+{
+ /* Computation:
+ * out = conv2d(conv2d(l0_input, l0_weight), l1_weight)
+ */
+ const auto data_type = DataType::F32;
+ const auto data_layout = DataLayout::NHWC;
+ const auto t_l0_input_shape = TensorShape(1024, 56, 56);
+ const auto t_l0_weight_shape = TensorShape(512, 1024, 1, 1);
+ const auto t_l1_weight_shape = TensorShape(512, 256, 1, 1);
+
+ auto t_l0_input_info = TensorInfo(t_l0_input_shape, 1, data_type, data_layout);
+ auto t_l0_weight_info = TensorInfo(t_l0_weight_shape, 1, data_type, data_layout);
+ auto t_l1_weight_info = TensorInfo(t_l1_weight_shape, 1, data_type, data_layout);
+ auto t_l0_dst_info = TensorInfo();
+ auto t_dst_info = TensorInfo();
+
+ OperatorGraph op_graph;
+ const auto conv2d_desc = Conv2dDescriptor{};
+
+ const auto op_t_l0_input = add_tensor(op_graph, t_l0_input_info);
+ const auto op_t_l0_weight = add_tensor(op_graph, t_l0_weight_info);
+ const auto op_t_l1_weight = add_tensor(op_graph, t_l1_weight_info);
+ const auto op_t_l0_dst = add_tensor(op_graph, t_l0_dst_info); // temp accumulator; TensorInfo to be inferred
+ const auto op_t_dst = add_tensor(op_graph, t_dst_info);
+
+ add_op_conv2d(op_graph, conv2d_desc, op_t_l0_input, op_t_l0_weight, op_t_l0_dst);
+ add_op_conv2d(op_graph, conv2d_desc, op_t_l0_dst, op_t_l1_weight, op_t_dst);
+
+ const ClWorkloadContext workload_ctx{ GpuInfo{ CLScheduler::get().target() } };
+ ClWorkload workload;
+ const auto success = build(workload, op_graph, workload_ctx);
+
+ ARM_COMPUTE_EXPECT(!bool(success), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!bool(ClCompositeOperator::validate(workload)), framework::LogLevel::ERRORS);
+}
+TEST_CASE(Enlarging_Execution_Space, framework::DatasetMode::ALL)
+{
+ /* Computation:
+ * out = add(l2_lhs, add(add(l0_lhs, l0_rhs), l1_rhs))
+ */
+ const auto data_type = DataType::F32;
+ const auto data_layout = DataLayout::NHWC;
+ const auto t_l0_lhs_shape = TensorShape(1, 256, 3);
+ const auto t_l0_rhs_shape = TensorShape(1, 256, 3);
+ const auto t_l1_rhs_shape = TensorShape(1, 1, 3);
+ const auto t_l2_lhs_shape = TensorShape(1024, 1, 3);
+
+ auto t_l0_lhs_info = TensorInfo(t_l0_lhs_shape, 1, data_type, data_layout);
+ auto t_l0_rhs_info = TensorInfo(t_l0_rhs_shape, 1, data_type, data_layout);
+ auto t_l1_rhs_info = TensorInfo(t_l1_rhs_shape, 1, data_type, data_layout);
+ auto t_l2_lhs_info = TensorInfo(t_l2_lhs_shape, 1, data_type, data_layout);
+ auto t_l0_dst_info = TensorInfo();
+ auto t_l1_dst_info = TensorInfo();
+ auto t_dst_info = TensorInfo();
+
+ OperatorGraph op_graph;
+ const auto add_desc = AddDescriptor{};
+
+ const auto op_t_l0_lhs = add_tensor(op_graph, t_l0_lhs_info);
+ const auto op_t_l0_rhs = add_tensor(op_graph, t_l0_rhs_info);
+ const auto op_t_l1_rhs = add_tensor(op_graph, t_l1_rhs_info);
+ const auto op_t_l2_lhs = add_tensor(op_graph, t_l2_lhs_info);
+ const auto op_t_l0_dst = add_tensor(op_graph, t_l0_dst_info); // temp accumulator; TensorInfo to be inferred
+ const auto op_t_l1_dst = add_tensor(op_graph, t_l1_dst_info); // temp accumulator; TensorInfo to be inferred
+ const auto op_t_dst = add_tensor(op_graph, t_dst_info);
+
+ add_op_elementwise_add(op_graph, add_desc, op_t_l0_lhs, op_t_l0_rhs, op_t_l0_dst);
+ add_op_elementwise_add(op_graph, add_desc, op_t_l0_dst, op_t_l1_rhs, op_t_l1_dst);
+ add_op_elementwise_add(op_graph, add_desc, op_t_l1_dst, op_t_l2_lhs, op_t_dst);
+
+ const ClWorkloadContext workload_ctx{ GpuInfo{ CLScheduler::get().target() } };
+ ClWorkload workload;
+ const auto success = build(workload, op_graph, workload_ctx);
+
+ ARM_COMPUTE_EXPECT(!bool(success), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!bool(ClCompositeOperator::validate(workload)), framework::LogLevel::ERRORS);
+}
+TEST_CASE(Root_Simple_And_Complex, framework::DatasetMode::ALL)
+{
+ /* Computation:
+ * out = add(conv(l0_0_input, l0_0_weight), add(l0_1_lhs, l0_1_rhs))
+ */
+ const auto data_type = DataType::F32;
+ const auto data_layout = DataLayout::NHWC;
+
+ const auto t_l0_0_input_shape = TensorShape(128, 21, 21);
+ const auto t_l0_0_weight_shape = TensorShape(144, 128, 1, 1);
+ const auto t_l0_1_lhs_shape = TensorShape(144, 21, 21);
+ const auto t_l0_1_rhs_shape = TensorShape(1, 1, 21);
+
+ auto t_l0_0_input_info = TensorInfo(t_l0_0_input_shape, 1, data_type, data_layout);
+ auto t_l0_0_weight_info = TensorInfo(t_l0_0_weight_shape, 1, data_type, data_layout);
+ auto t_l0_1_lhs_info = TensorInfo(t_l0_1_lhs_shape, 1, data_type, data_layout);
+ auto t_l0_1_rhs_info = TensorInfo(t_l0_1_rhs_shape, 1, data_type, data_layout);
+ auto t_l0_0_dst_info = TensorInfo();
+ auto t_l0_1_dst_info = TensorInfo();
+ auto t_dst_info = TensorInfo();
+
+ OperatorGraph op_graph;
+ const auto conv2d_desc = Conv2dDescriptor{};
+ const auto add_desc = AddDescriptor{};
+
+ const auto op_t_l0_0_input = add_tensor(op_graph, t_l0_0_input_info);
+ const auto op_t_l0_0_weight = add_tensor(op_graph, t_l0_0_weight_info);
+ const auto op_t_l0_1_lhs = add_tensor(op_graph, t_l0_1_lhs_info);
+ const auto op_t_l0_1_rhs = add_tensor(op_graph, t_l0_1_rhs_info);
+ const auto op_t_l0_0_dst = add_tensor(op_graph, t_l0_0_dst_info); // temp accumulator; TensorInfo to be inferred
+ const auto op_t_l0_1_dst = add_tensor(op_graph, t_l0_1_dst_info); // temp accumulator; TensorInfo to be inferred
+ const auto op_t_dst = add_tensor(op_graph, t_dst_info);
+
+ add_op_conv2d(op_graph, conv2d_desc, op_t_l0_0_input, op_t_l0_0_weight, op_t_l0_0_dst);
+ add_op_elementwise_add(op_graph, add_desc, op_t_l0_1_lhs, op_t_l0_1_rhs, op_t_l0_1_dst);
+ add_op_elementwise_add(op_graph, add_desc, op_t_l0_0_dst, op_t_l0_1_dst, op_t_dst);
+
+ const ClWorkloadContext workload_ctx{ GpuInfo{ CLScheduler::get().target() } };
+ ClWorkload workload;
+ const auto success = build(workload, op_graph, workload_ctx);
+
+ ARM_COMPUTE_EXPECT(!bool(success), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!bool(ClCompositeOperator::validate(workload)), framework::LogLevel::ERRORS);
+}
+TEST_CASE(Loop, framework::DatasetMode::ALL)
+{
+ /* Computation:
+ * tensor state0;
+ * state1 = add(l0_lhs, state0)
+ * state0 = add(l1_lhs, state1)
+ */
+ const auto data_type = DataType::F32;
+ const auto data_layout = DataLayout::NHWC;
+
+ const auto t_shape = TensorShape(13, 21);
+
+ auto t_l0_lhs_info = TensorInfo(t_shape, 1, data_type, data_layout);
+ auto t_l1_lhs_info = TensorInfo(t_shape, 1, data_type, data_layout);
+ auto state0_info = TensorInfo(t_shape, 1, data_type, data_layout);
+ auto state1_info = TensorInfo();
+
+ OperatorGraph op_graph;
+ const auto conv2d_desc = Conv2dDescriptor{};
+ const auto add_desc = AddDescriptor{};
+
+ const auto op_t_l0_lhs = add_tensor(op_graph, t_l0_lhs_info);
+ const auto op_t_l1_lhs = add_tensor(op_graph, t_l1_lhs_info);
+ const auto op_t_state0 = add_tensor(op_graph, state0_info);
+ const auto op_t_state1 = add_tensor(op_graph, state1_info);
+
+ add_op_conv2d(op_graph, conv2d_desc, op_t_l0_lhs, op_t_state0, op_t_state1);
+ add_op_elementwise_add(op_graph, add_desc, op_t_l1_lhs, op_t_state1, op_t_state0);
+
+ const ClWorkloadContext workload_ctx{ GpuInfo{ CLScheduler::get().target() } };
+ ClWorkload workload;
+ const auto success = build(workload, op_graph, workload_ctx);
+
+ ARM_COMPUTE_EXPECT(!bool(success), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!bool(ClCompositeOperator::validate(workload)), framework::LogLevel::ERRORS);
+}
+TEST_SUITE_END() // Invalid
+
+TEST_SUITE_END() // DYNAMIC_FUSION
+TEST_SUITE_END() // INTEGRATION
+TEST_SUITE_END() // CL
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
\ No newline at end of file
diff --git a/tests/validation/CL/UNIT/dynamic_fusion/Utils.h b/tests/validation/CL/UNIT/dynamic_fusion/Utils.h
new file mode 100644
index 0000000..4512305
--- /dev/null
+++ b/tests/validation/CL/UNIT/dynamic_fusion/Utils.h
@@ -0,0 +1,71 @@
+/*
+ * 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.
+ */
+#ifndef TESTS_VALIDATION_CL_DYNAMICFUSION_UTILS
+#define TESTS_VALIDATION_CL_DYNAMICFUSION_UTILS
+
+#include "tests/AssetsLibrary.h"
+#include "utils/Utils.h"
+
+#include <chrono>
+#include <limits>
+#include <type_traits>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace utils
+{
+/** A pair of macros which measures the wall clock time, and records it into a map measurement_map with name clock_name
+ *
+ */
+#define TICK(clock_name) \
+ auto clock_name##_tick = std::chrono::high_resolution_clock::now();
+#define TOCK(clock_name, measurement_map) \
+ auto clock_name##_tock = std::chrono::high_resolution_clock::now(); \
+ measurement_map["\"" #clock_name "\""] = duration_cast<microseconds>(clock_name##_tock - clock_name##_tick);
+#define TOCK_AVG(clock_name, measurement_map, num_iterations) \
+ auto clock_name##_tock = std::chrono::high_resolution_clock::now(); \
+ measurement_map["\"" #clock_name "\""] = duration_cast<microseconds>((clock_name##_tock - clock_name##_tick) / (num_iterations));
+
+template <typename T, typename U>
+void fill(U &&tensor, int seed, AssetsLibrary *library)
+{
+ static_assert(std::is_floating_point<T>::value || std::is_same<T, half>::value, "Only floating point data types supported.");
+ using DistributionType = typename std::conditional<std::is_same<T, half>::value, arm_compute::utils::uniform_real_distribution_16bit<T>, std::uniform_real_distribution<T>>::type;
+
+ DistributionType distribution{ T(-1.0f), T(1.0f) };
+ library->fill(tensor, distribution, seed);
+
+ // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
+ DistributionType distribution_inf{ T(std::numeric_limits<float>::infinity()), T(std::numeric_limits<float>::infinity()) };
+ library->fill_borders_with_garbage(tensor, distribution_inf, seed);
+}
+} // namespace utils
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
+#endif //TESTS_VALIDATION_CL_DYNAMICFUSION_UTILS
\ No newline at end of file