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-rw-r--r--src/cpu/kernels/softmax/generic/neon/impl.h210
1 files changed, 106 insertions, 104 deletions
diff --git a/src/cpu/kernels/softmax/generic/neon/impl.h b/src/cpu/kernels/softmax/generic/neon/impl.h
index 4d9b789297..60380cd233 100644
--- a/src/cpu/kernels/softmax/generic/neon/impl.h
+++ b/src/cpu/kernels/softmax/generic/neon/impl.h
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef SRC_CORE_NEON_KERNELS_SOFTMAX_IMPL_H
-#define SRC_CORE_NEON_KERNELS_SOFTMAX_IMPL_H
+#ifndef ACL_SRC_CPU_KERNELS_SOFTMAX_GENERIC_NEON_IMPL_H
+#define ACL_SRC_CPU_KERNELS_SOFTMAX_GENERIC_NEON_IMPL_H
#include "arm_compute/core/Helpers.h"
@@ -33,105 +33,100 @@ namespace arm_compute
{
namespace cpu
{
-template <typename T>
-void neon_logits_1d_max(const ITensor *in, ITensor *out, const Window &window)
-{
- /** SIMD vector tag type. */
- using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
-
- constexpr int window_step_x = 16 / sizeof(T);
- const auto window_start_x = static_cast<int>(window.x().start());
- const auto window_end_x = static_cast<int>(window.x().end());
-
- Window win{window};
- win.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator input(in, win);
- Iterator output(out, win);
-
- const int sum_stages = log2(window_step_x / 2);
- execute_window_loop(
- win,
- [&](const Coordinates &)
- {
- // Get pointers
- const auto in_ptr = reinterpret_cast<const T *>(input.ptr());
- const auto out_ptr = reinterpret_cast<T *>(output.ptr());
-
- // Init max value
- auto vec_max = wrapper::vdup_n(support::cpp11::lowest<T>(), ExactTagType{});
- int x = window_start_x;
-
- for (; x <= (window_end_x - window_step_x); x += window_step_x)
- {
- const auto current_value = wrapper::vloadq(in_ptr + x);
- vec_max = wrapper::vmax(vec_max, current_value);
- }
- auto carry_max = wrapper::vpmax(wrapper::vgethigh(vec_max), wrapper::vgetlow(vec_max));
-
- for (int i = 0; i < sum_stages; ++i)
- {
- carry_max = wrapper::vpmax(carry_max, carry_max);
- }
- T max_val = wrapper::vgetlane(carry_max, 0);
- // Compute left-over elements
- for (; x < window_end_x; ++x)
- {
- max_val = *(in_ptr + x) > max_val ? *(in_ptr + x) : max_val;
- }
+#ifdef __aarch64__
+namespace
+{
+// These helper functions are added because vaddv does not exist for fp16,
+// and, therefore, is not part of the wrapper::vaddv interface.
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+inline float16_t wrapper_vaddv(const float16x8_t &a, int sum_stages)
+{
+ auto sum_res = wrapper::vpadd(wrapper::vgethigh(a), wrapper::vgetlow(a));
+ for (int i = 0; i < sum_stages; ++i)
+ {
+ sum_res = wrapper::vpadd(sum_res, sum_res);
+ }
+ return wrapper::vgetlane(sum_res, 0);
+}
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- *out_ptr = max_val;
- },
- input, output);
+inline float wrapper_vaddv(const float32x4_t &a, int sum_stages)
+{
+ ARM_COMPUTE_UNUSED(sum_stages);
+ return wrapper::vaddv(a);
}
+} // namespace
+#endif // __aarch64__
-template <typename T>
-void neon_softmax_logits_1d_quantized(const ITensor *in,
- const ITensor *max,
- void *const tmp,
- ITensor *out,
- float beta,
- bool is_log,
- const Window &window);
-
-template <typename T>
-void neon_softmax_logits_1d_float(const ITensor *in,
- const ITensor *max,
- void *const tmp,
- ITensor *out,
- const float beta,
- bool is_log,
- const Window &window)
+// The template implementation for float data types is stored in the header file because
+// we need all fp16 instantiated code to live in fp16.cpp files.
+template <typename T, bool IS_LOG>
+void neon_softmax_float(const ITensor *in, void *const tmp, ITensor *out, float beta, const Window &window)
{
- const int start_x = in->info()->valid_region().anchor.x();
+ ARM_COMPUTE_UNUSED(tmp);
+
const int input_width = in->info()->valid_region().shape.x();
Iterator in_it(in, window);
- Iterator max_it(max, window);
Iterator out_it(out, window);
/** SIMD vector tag type. */
using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
- constexpr int vec_size = 16 / sizeof(T);
- const int sum_stages = log2(vec_size / 2);
+ constexpr int vec_size = 16 / sizeof(T);
+
+ const int sum_stages = log2(vec_size >> 1);
+
+ const auto beta_vec = wrapper::vdup_n(static_cast<T>(beta), ExactTagType{});
execute_window_loop(
window,
[&](const Coordinates &)
{
/* Get pointers */
- const auto in_ptr = reinterpret_cast<const T *>(in_it.ptr()) + start_x;
- const auto out_ptr = reinterpret_cast<T *>(out_it.ptr()) + start_x;
- const auto tmp_ptr = reinterpret_cast<T *>(tmp);
+ const T *in_ptr = reinterpret_cast<const T *>(in_it.ptr());
+ T *out_ptr = reinterpret_cast<T *>(out_it.ptr());
+
+ T max_val;
+
+ /* Compute Max */
+ {
+ // Init max value
+ auto vec_max = wrapper::vdup_n(support::cpp11::lowest<T>(), ExactTagType{});
+ int x = 0;
+
+ for (; x <= (input_width - vec_size); x += vec_size)
+ {
+ const auto current_value = wrapper::vloadq(in_ptr + x);
+ vec_max = wrapper::vmax(vec_max, current_value);
+ }
+
+#ifdef __aarch64__
+ max_val = wrapper::vmaxv(vec_max);
+#else // __aarch64__
+ auto carry_max = wrapper::vpmax(wrapper::vgethigh(vec_max), wrapper::vgetlow(vec_max));
+
+ for (int i = 0; i < sum_stages; ++i)
+ {
+ carry_max = wrapper::vpmax(carry_max, carry_max);
+ }
+
+ max_val = wrapper::vgetlane(carry_max, 0);
+#endif // __aarch64__
- T sum{};
- T sum_inversed{};
+ // Compute left-over elements
+ for (; x < input_width; ++x)
+ {
+ max_val = std::max(*(in_ptr + x), max_val);
+ }
+ } // compute max
+
+ T sum_transformed{};
/* Compute exponentials and sum */
{
/* Get max value */
- const auto max_val = *reinterpret_cast<const T *>(max_it.ptr());
const auto vec_max = wrapper::vdup_n(max_val, ExactTagType{});
/* Init sum to zero */
@@ -143,35 +138,38 @@ void neon_softmax_logits_1d_float(const ITensor *in,
{
auto vec_elements = wrapper::vloadq(in_ptr + x);
vec_elements = wrapper::vsub(vec_elements, vec_max);
- if (is_log)
+ if (IS_LOG)
{
- vec_elements =
- wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast<T>(beta), ExactTagType{}));
- vec_sum = wrapper::vadd(vec_sum, wrapper::vexpq(vec_elements));
+ vec_elements = wrapper::vmul(vec_elements, beta_vec);
+ vec_sum = wrapper::vadd(vec_sum, wrapper::vexpq(vec_elements));
}
else
{
- vec_elements = wrapper::vexpq(
- wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast<T>(beta), ExactTagType{})));
- vec_sum = wrapper::vadd(vec_sum, vec_elements);
+ vec_elements = wrapper::vexpq(wrapper::vmul(vec_elements, beta_vec));
+ vec_sum = wrapper::vadd(vec_sum, vec_elements);
}
- wrapper::vstore(tmp_ptr + x, vec_elements);
+ wrapper::vstore(out_ptr + x, vec_elements);
}
/* Reduce sum */
+ T sum{};
+#ifdef __aarch64__
+ sum = wrapper_vaddv(vec_sum, sum_stages);
+#else // __aarch64__
auto sum_res = wrapper::vpadd(wrapper::vgethigh(vec_sum), wrapper::vgetlow(vec_sum));
for (int i = 0; i < sum_stages; ++i)
{
sum_res = wrapper::vpadd(sum_res, sum_res);
}
sum = wrapper::vgetlane(sum_res, 0);
+#endif // __aarch64__
/* Run remaining elements */
for (; x < input_width; ++x)
{
T element{};
- if (is_log)
+ if (IS_LOG)
{
element = (in_ptr[x] - max_val) * beta;
sum += std::exp(element);
@@ -181,55 +179,59 @@ void neon_softmax_logits_1d_float(const ITensor *in,
element = std::exp((in_ptr[x] - max_val) * beta);
sum += element;
}
- tmp_ptr[x] = element;
+
+ out_ptr[x] = element;
}
- if (!is_log)
+ if (!IS_LOG)
{
- sum_inversed = T(1) / sum;
+ sum_transformed = T(1) / sum;
}
else
{
- sum = static_cast<T>(std::log(sum));
+ sum_transformed = static_cast<T>(std::log(sum));
}
- }
+ } // Compute exponentials and sum
/* Normalize exponentials */
{
+ const auto sum_vec = wrapper::vdup_n(static_cast<T>(sum_transformed), ExactTagType{});
+
/* Loop over row and compute softmax */
int x = 0;
for (; x <= (input_width - vec_size); x += vec_size)
{
- auto vec_in = wrapper::vloadq(tmp_ptr + x);
- auto normalized_value = wrapper::vdup_n(static_cast<T>(0), ExactTagType{});
- if (is_log)
+ const auto vec_in = wrapper::vloadq(out_ptr + x);
+ if (IS_LOG)
{
- normalized_value = wrapper::vsub(vec_in, wrapper::vdup_n(static_cast<T>(sum), ExactTagType{}));
+ wrapper::vstore(out_ptr + x, wrapper::vsub(vec_in, sum_vec));
}
else
{
- normalized_value =
- wrapper::vmul(vec_in, wrapper::vdup_n(static_cast<T>(sum_inversed), ExactTagType{}));
+ wrapper::vstore(out_ptr + x, wrapper::vmul(vec_in, sum_vec));
}
- wrapper::vstore(out_ptr + x, normalized_value);
}
+
/* Run remaining elements */
for (; x < input_width; ++x)
{
- if (is_log)
+ if (IS_LOG)
{
- out_ptr[x] = tmp_ptr[x] - sum;
+ out_ptr[x] = out_ptr[x] - sum_transformed;
}
else
{
- out_ptr[x] = tmp_ptr[x] * sum_inversed;
+ out_ptr[x] = out_ptr[x] * sum_transformed;
}
}
- }
+ } // Normalize exponentials
},
- in_it, max_it, out_it);
+ in_it, out_it);
}
+
+template <typename T, bool IS_LOG>
+void neon_softmax_quantized(const ITensor *in, void *const tmp, ITensor *out, float beta, const Window &window);
} // namespace cpu
} // namespace arm_compute
-#endif /* SRC_CORE_NEON_KERNELS_SOFTMAX_IMPL_H */
+#endif // ACL_SRC_CPU_KERNELS_SOFTMAX_GENERIC_NEON_IMPL_H
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-27 07:29:42 +0000