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-rw-r--r--src/cpu/kernels/softmax/generic/sve2/impl.h176
1 files changed, 2 insertions, 174 deletions
diff --git a/src/cpu/kernels/softmax/generic/sve2/impl.h b/src/cpu/kernels/softmax/generic/sve2/impl.h
index 16dde2b115..abbcc15181 100644
--- a/src/cpu/kernels/softmax/generic/sve2/impl.h
+++ b/src/cpu/kernels/softmax/generic/sve2/impl.h
@@ -24,9 +24,7 @@
#ifndef SRC_CORE_SVE2_KERNELS_SOFTMAX_IMPL_H
#define SRC_CORE_SVE2_KERNELS_SOFTMAX_IMPL_H
-#if defined(ARM_COMPUTE_ENABLE_SVE2)
-#include "arm_compute/core/Types.h"
-#include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
+#include "arm_compute/core/Helpers.h"
namespace arm_compute
{
@@ -34,177 +32,7 @@ namespace cpu
{
template <typename ScalarType>
void sve2_softmax_logits_1d_quantized(const ITensor *in, const ITensor *max, void *const tmp,
- ITensor *out, float beta, bool is_log, const Window &window)
-{
- const int start_x = in->info()->valid_region().anchor.x();
- const int input_width = in->info()->valid_region().shape.x();
-
- const float scale_beta = -beta * in->info()->quantization_info().uniform().scale;
- const auto scale_beta_vec = svdup_n_f32(scale_beta);
-
- Iterator in_it(in, window);
- Iterator max_it(max, window);
- Iterator out_it(out, window);
- const auto all_true_pg = wrapper::svptrue<ScalarType>();
- using SVEType = typename wrapper::traits::sve_vector<ScalarType>::type;
-
- const int inc_1 = static_cast<int>(svcntw());
- const int inc_2 = static_cast<int>(2 * svcntw());
- const int inc_3 = static_cast<int>(3 * svcntw());
-
- execute_window_loop(window, [&](const Coordinates &)
- {
- /* Get pointers */
- const auto in_ptr = reinterpret_cast<const ScalarType *>(in_it.ptr()) + start_x;
- const auto out_ptr = reinterpret_cast<ScalarType *>(out_it.ptr()) + start_x;
- const auto tmp_ptr = reinterpret_cast<float *>(tmp);
-
- float sum{};
-
- /* Compute exponentials and sum */
- {
- /* Get max value */
- const auto max_val = *reinterpret_cast<const ScalarType *>(max_it.ptr());
- const auto vec_max = wrapper::svdup_n(max_val);
-
- /* Init sum to zero */
- auto vec_sum_0 = svdup_n_f32(0.f);
- auto vec_sum_1 = svdup_n_f32(0.f);
- auto vec_sum_2 = svdup_n_f32(0.f);
- auto vec_sum_3 = svdup_n_f32(0.f);
-
- /* Loop over row and compute exponentials and sum */
- int x = 0;
- svbool_t pg = wrapper::svwhilelt<ScalarType>(x, input_width);
- svbool_t pg_0 = svunpklo(svunpklo(pg));
- svbool_t pg_1 = svunpkhi(svunpklo(pg));
- svbool_t pg_2 = svunpklo(svunpkhi(pg));
- svbool_t pg_3 = svunpkhi(svunpkhi(pg));
- do
- {
- auto vec_elements = svld1(pg, in_ptr + x);
- vec_elements = svsub_z(pg, vec_max, vec_elements);
-
- auto vec_elements_flt_0 = svcvt_f32_z(pg_0, svunpklo(svunpklo(vec_elements)));
- auto vec_elements_flt_1 = svcvt_f32_z(pg_1, svunpkhi(svunpklo(vec_elements)));
- auto vec_elements_flt_2 = svcvt_f32_z(pg_2, svunpklo(svunpkhi(vec_elements)));
- auto vec_elements_flt_3 = svcvt_f32_z(pg_3, svunpkhi(svunpkhi(vec_elements)));
-
- if(is_log)
- {
- vec_elements_flt_0 = svmul_f32_z(pg_0, vec_elements_flt_0, scale_beta_vec);
- vec_elements_flt_1 = svmul_f32_z(pg_1, vec_elements_flt_1, scale_beta_vec);
- vec_elements_flt_2 = svmul_f32_z(pg_2, vec_elements_flt_2, scale_beta_vec);
- vec_elements_flt_3 = svmul_f32_z(pg_3, vec_elements_flt_3, scale_beta_vec);
- vec_sum_0 = svadd_f32_m(pg_0, vec_sum_0, svexp_f32_z(pg_0, vec_elements_flt_0));
- vec_sum_1 = svadd_f32_m(pg_1, vec_sum_1, svexp_f32_z(pg_1, vec_elements_flt_1));
- vec_sum_2 = svadd_f32_m(pg_2, vec_sum_2, svexp_f32_z(pg_2, vec_elements_flt_2));
- vec_sum_3 = svadd_f32_m(pg_3, vec_sum_3, svexp_f32_z(pg_3, vec_elements_flt_3));
- }
- else
- {
- vec_elements_flt_0 = svexp_f32_z(pg_0, svmul_f32_z(pg_0, vec_elements_flt_0, scale_beta_vec));
- vec_elements_flt_1 = svexp_f32_z(pg_1, svmul_f32_z(pg_1, vec_elements_flt_1, scale_beta_vec));
- vec_elements_flt_2 = svexp_f32_z(pg_2, svmul_f32_z(pg_2, vec_elements_flt_2, scale_beta_vec));
- vec_elements_flt_3 = svexp_f32_z(pg_3, svmul_f32_z(pg_3, vec_elements_flt_3, scale_beta_vec));
- vec_sum_0 = svadd_f32_m(pg_0, vec_sum_0, vec_elements_flt_0);
- vec_sum_1 = svadd_f32_m(pg_1, vec_sum_1, vec_elements_flt_1);
- vec_sum_2 = svadd_f32_m(pg_2, vec_sum_2, vec_elements_flt_2);
- vec_sum_3 = svadd_f32_m(pg_3, vec_sum_3, vec_elements_flt_3);
- }
-
- svst1_f32(pg_0, tmp_ptr + x, vec_elements_flt_0);
- svst1_f32(pg_1, tmp_ptr + x + inc_1, vec_elements_flt_1);
- svst1_f32(pg_2, tmp_ptr + x + inc_2, vec_elements_flt_2);
- svst1_f32(pg_3, tmp_ptr + x + inc_3, vec_elements_flt_3);
-
- x += wrapper::svcnt<ScalarType>();
- pg = wrapper::svwhilelt<ScalarType>(x, input_width);
- pg_0 = svunpklo(svunpklo(pg));
- pg_1 = svunpkhi(svunpklo(pg));
- pg_2 = svunpklo(svunpkhi(pg));
- pg_3 = svunpkhi(svunpkhi(pg));
- }
- while(svptest_any(all_true_pg, pg));
-
- /* Reduce sum */
- const auto vec_sum = svadd_f32_z(all_true_pg, svadd_f32_z(all_true_pg, vec_sum_0, vec_sum_1), svadd_f32_z(all_true_pg, vec_sum_2, vec_sum_3));
- sum = svaddv_f32(all_true_pg, vec_sum);
-
- /* Run remaining elements */
- x = 0;
- if(is_log)
- {
- sum = std::log(sum);
- }
- else
- {
- sum = 256.f / sum;
- }
- }
-
- /* Normalize exponentials */
- {
- constexpr bool is_qasymm8_signed = std::is_same<ScalarType, qasymm8_signed_t>::value;
- /* Loop over row and compute softmax */
- int x = 0;
- svbool_t pg = wrapper::svwhilelt<ScalarType>(x, input_width);
- svbool_t pg_0 = svunpklo(svunpklo(pg));
- svbool_t pg_1 = svunpkhi(svunpklo(pg));
- svbool_t pg_2 = svunpklo(svunpkhi(pg));
- svbool_t pg_3 = svunpkhi(svunpkhi(pg));
- do
- {
- auto vec_in_0 = svld1_f32(pg_0, tmp_ptr + x);
- auto vec_in_1 = svld1_f32(pg_1, tmp_ptr + x + inc_1);
- auto vec_in_2 = svld1_f32(pg_2, tmp_ptr + x + inc_2);
- auto vec_in_3 = svld1_f32(pg_3, tmp_ptr + x + inc_3);
-
- svfloat32_t res_0{};
- svfloat32_t res_1{};
- svfloat32_t res_2{};
- svfloat32_t res_3{};
-
- if(is_log)
- {
- res_0 = svsub_f32_z(pg_0, vec_in_0, svdup_n_f32(sum));
- res_1 = svsub_f32_z(pg_1, vec_in_1, svdup_n_f32(sum));
- res_2 = svsub_f32_z(pg_2, vec_in_2, svdup_n_f32(sum));
- res_3 = svsub_f32_z(pg_3, vec_in_3, svdup_n_f32(sum));
- }
- else
- {
- res_0 = svmul_f32_z(pg_0, vec_in_0, svdup_n_f32(sum));
- res_1 = svmul_f32_z(pg_1, vec_in_1, svdup_n_f32(sum));
- res_2 = svmul_f32_z(pg_2, vec_in_2, svdup_n_f32(sum));
- res_3 = svmul_f32_z(pg_3, vec_in_3, svdup_n_f32(sum));
-
- if(is_qasymm8_signed)
- {
- const auto offset_vec = svdup_n_f32(128.f);
- res_0 = svsub_z(pg_0, vec_in_0, offset_vec);
- res_1 = svsub_z(pg_1, vec_in_1, offset_vec);
- res_2 = svsub_z(pg_2, vec_in_2, offset_vec);
- res_3 = svsub_z(pg_3, vec_in_3, offset_vec);
- }
- }
-
- // Store value
- const auto out = convert_float_to_int<SVEType>(res_0, res_1, res_2, res_3);
- svst1(pg, out_ptr + x, out);
- x += wrapper::svcnt<ScalarType>();
- pg = wrapper::svwhilelt<ScalarType>(x, input_width);
- pg_0 = svunpklo(svunpklo(pg));
- pg_1 = svunpkhi(svunpklo(pg));
- pg_2 = svunpklo(svunpkhi(pg));
- pg_3 = svunpkhi(svunpkhi(pg));
- }
- while(svptest_any(all_true_pg, pg));
- }
- },
- in_it, max_it, out_it);
-}
+ ITensor *out, float beta, bool is_log, const Window &window);
} // namespace cpu
} // namespace arm_compute
-#endif /* defined(ARM_COMPUTE_ENABLE_SVE2) */
#endif /* SRC_CORE_SVE2_KERNELS_SOFTMAX_IMPL_H */
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-26 23:31:18 +0000