COMPMID-1021: CPUInfo refactoring.
Removed CPUTarget in favor of the CPUModel type.
CPUInfo now holds a vector of N CPUs.
CPUInfo autoinitialise upon construction with 1 GENERIC CPU.
CPPScheduler fills CPUInfo's vector upon construction (runtime).
IScheduler has a single CPUInfo obj and ThreadInfo always gets a pointer to it (avoid copying the vector)
Change-Id: I30f293258c959c87f6bac5eac8b963beb6a4d365
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/124626
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/arm_compute/runtime/NEON/AssemblyHelper.h b/arm_compute/runtime/NEON/AssemblyHelper.h
index e2d27cf..40f2858 100644
--- a/arm_compute/runtime/NEON/AssemblyHelper.h
+++ b/arm_compute/runtime/NEON/AssemblyHelper.h
@@ -127,70 +127,32 @@
/** 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 @@
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