diff options
Diffstat (limited to 'arm_compute/runtime/NEON/AssemblyHelper.h')
| -rw-r--r-- | arm_compute/runtime/NEON/AssemblyHelper.h | 80 |
1 files changed, 25 insertions, 55 deletions
diff --git a/arm_compute/runtime/NEON/AssemblyHelper.h b/arm_compute/runtime/NEON/AssemblyHelper.h index e2d27cf941..40f28587c2 100644 --- a/arm_compute/runtime/NEON/AssemblyHelper.h +++ b/arm_compute/runtime/NEON/AssemblyHelper.h @@ -127,70 +127,32 @@ inline void allocate_workspace(size_t workspace_size, Tensor &workspace, MemoryG /** Create a wrapper kernel. * - * @param[in] a Input tensor A. - * @param[in] b Input tensor B. - * @param[in] c (Optional) Input tensor C. - * @param[out] d Output tensor. - * @param[in] alpha Alpha value. - * @param[in] beta Beta value. - * - * @return the wrapper kernel. - */ -template <typename T> -std::unique_ptr<NEGEMMAssemblyWrapper<T>> create_wrapper_kernel(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *d, float alpha, float beta) -{ - // rework this function, why are we checking data type and other things here ? should we create another function can_run_optimised_kernel() ? -#if defined(__arm__) - if(NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && a->info()->data_type() == DataType::F32 && (c == nullptr || beta == 0.f)) - { - return support::cpp14::make_unique<NEGEMMAssemblyWrapper<T>>(); - } -#elif defined(__aarch64__) - if(NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && a->info()->data_type() == DataType::F32 && (c == nullptr || beta == 0.f)) - { - return support::cpp14::make_unique<NEGEMMAssemblyWrapper<T>>(); - } - else if(a->info()->data_type() == DataType::F16 && (c == nullptr || beta == 0.f)) - { -#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC - return support::cpp14::make_unique<NEGEMMAssemblyWrapper<T>>(); -#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - ARM_COMPUTE_ERROR("Recompile the library with arch=arm64-v8.2-a to enable support for FP16."); -#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ - } -#endif /* defined(__arm__) || defined(__aarch64__) */ - return nullptr; -} - -/** Setup assembly kernel. - * * @param[in] a Input tensor A. * @param[in] b Input tensor B. - * @param[in] c (Optional) Input tensor C. - * @param[in] d Output tensor. + * @param[out] d Output tensor. * @param[in] alpha Alpha value. * @param[in] beta Beta value. * @param[out] workspace Workspace tensor * @param[in] memory_group Tensor memory group. * @param[out] asm_glue Assembly glue kernel. * - * @return True if the assembly kernel is setup correctly. + * @return the wrapper kernel. */ template <typename T> -inline bool setup_assembly_kernel(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *d, float alpha, float beta, +inline bool setup_assembly_kernel(const ITensor *a, const ITensor *b, ITensor *d, float alpha, float beta, Tensor &workspace, MemoryGroup &memory_group, T &asm_glue) { - const ::CPUInfo *ci = get_CPUInfo(); - const int M = d->info()->tensor_shape().y(); - const int N = d->info()->tensor_shape().x(); - const int K = a->info()->tensor_shape().x(); - unsigned int num_threads = NEScheduler::get().num_threads(); + const CPUInfo &ci = NEScheduler::get().cpu_info(); + const int M = d->info()->tensor_shape().y(); + const int N = d->info()->tensor_shape().x(); + const int K = a->info()->tensor_shape().x(); + unsigned int num_threads = NEScheduler::get().num_threads(); // unique_ptr to a Gemm object - std::unique_ptr<typename T::AssemblyGemm> asm_gemm(arm_gemm::gemm<typename T::TypeOperator, typename T::TypeResult>(*ci, M, N, K, false, false, alpha, beta, num_threads, - false)); - + std::unique_ptr<typename T::AssemblyGemm> + asm_gemm(arm_gemm::gemm<typename T::TypeOperator, typename T::TypeResult>(ci, M, N, K, false, false, alpha, beta, num_threads, false)); // arm_compute wrapper for the Gemm object (see above) - std::unique_ptr<NEGEMMAssemblyWrapper<typename T::AssemblyGemm>> acl_gemm_wrapper = create_wrapper_kernel<typename T::AssemblyGemm>(a, b, c, d, alpha, beta); + std::unique_ptr<NEGEMMAssemblyWrapper<typename T::AssemblyGemm>> + acl_gemm_wrapper = support::cpp14::make_unique<NEGEMMAssemblyWrapper<typename T::AssemblyGemm>>(); if(acl_gemm_wrapper != nullptr && asm_gemm != nullptr) { acl_gemm_wrapper->configure(asm_gemm.get()); @@ -198,15 +160,23 @@ inline bool setup_assembly_kernel(const ITensor *a, const ITensor *b, const ITen if(workspace_size) { // Allocate workspace - allocate_workspace(workspace_size, workspace, memory_group, 4096, num_threads); + const unsigned int alignment = 4096; + allocate_workspace(workspace_size, workspace, memory_group, alignment, num_threads); + ARM_COMPUTE_ERROR_ON_NULLPTR(workspace.buffer()); asm_gemm->set_working_space(reinterpret_cast<typename T::TypeResult *>(workspace.buffer())); } - const unsigned int window_size = asm_gemm->get_window_size(); - if(window_size < num_threads) + + //if we disable this code below in brackets then ConvLayer deadlocks when threads > 1 and + //the shapes are In=1x1x1024 Weights=1x1x1024x1001 Biases=1001 Out=1x1x1001 { - num_threads = window_size; - asm_gemm->set_nthreads(num_threads); + const unsigned int window_size = asm_gemm->get_window_size(); + if(window_size < num_threads) + { + num_threads = window_size; + asm_gemm->set_nthreads(num_threads); + } } + asm_glue._gemm_kernel_asm = std::move(asm_gemm); asm_glue._optimised_kernel = std::move(acl_gemm_wrapper); // We need to setup the ptrs in the run() method |