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Diffstat (limited to 'src/cpu/kernels/softmax/generic/neon/impl.cpp')
-rw-r--r--src/cpu/kernels/softmax/generic/neon/impl.cpp152
1 files changed, 96 insertions, 56 deletions
diff --git a/src/cpu/kernels/softmax/generic/neon/impl.cpp b/src/cpu/kernels/softmax/generic/neon/impl.cpp
index 5d6e6a4f80..487f6ae051 100644
--- a/src/cpu/kernels/softmax/generic/neon/impl.cpp
+++ b/src/cpu/kernels/softmax/generic/neon/impl.cpp
@@ -29,43 +29,76 @@ namespace arm_compute
{
namespace cpu
{
-template void neon_logits_1d_max<qasymm8_signed_t>(const ITensor *in, ITensor *out, const Window &window);
-template void neon_logits_1d_max<qasymm8_t>(const ITensor *in, ITensor *out, const Window &window);
-
-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, bool IS_LOG>
+void neon_softmax_quantized(const ITensor *in, void *const tmp, ITensor *out, float beta, const Window &window)
{
static_assert(std::is_same<T, qasymm8_t>::value || std::is_same<T, qasymm8_signed_t>::value,
"quantized type should be either qasymm8_t or qasymm8_signed_t.");
- 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 = vdupq_n_f32(scale_beta);
+ const float scale_beta = -beta * in->info()->quantization_info().uniform().scale;
+ const float32x4_t scale_beta_vec = vdupq_n_f32(scale_beta);
+
+ Iterator in_it(in, window);
+ Iterator out_it(out, window);
- Iterator in_it(in, window);
- Iterator max_it(max, window);
- Iterator out_it(out, window);
constexpr int vec_size = 16;
+#ifndef __aarch64__
+ const int sum_stages = log2(vec_size >> 1);
+#endif // __aarch64__
+
+ using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
+
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<float *>(tmp);
+ const T *in_ptr = reinterpret_cast<const T *>(in_it.ptr());
+ T *out_ptr = reinterpret_cast<T *>(out_it.ptr());
+ float *tmp_ptr = reinterpret_cast<float *>(tmp);
+
+ T max_val;
+
+ /* Compute Max */
+ {
+ // Init max value
+ auto vec_max = wrapper::vdup_n(support::cpp11::lowest<T>(), ExactTagType{});
+ int x = 0;
- float sum{};
- float sum_inversed{};
+ 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__
+
+ // Compute left-over elements
+ for (; x < input_width; ++x)
+ {
+ max_val = std::max(*(in_ptr + x), max_val);
+ }
+ } // Compute Max
+
+ float 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, wrapper::traits::vector_128_tag{});
/* Init sum to zero */
@@ -80,11 +113,11 @@ void neon_softmax_logits_1d_quantized(
int x = 0;
for (; x <= (input_width - vec_size); x += vec_size)
{
- auto vec_elements = wrapper::vloadq(in_ptr + x);
- vec_elements = wrapper::vqsub(vec_max, vec_elements);
- auto vec_elements_flt = convert_int_to_float<float32x4x4_t>(vec_elements);
+ auto vec_elements = wrapper::vloadq(in_ptr + x);
+ vec_elements = wrapper::vqsub(vec_max, vec_elements);
+ float32x4x4_t vec_elements_flt = convert_int_to_float<float32x4x4_t>(vec_elements);
- if (is_log)
+ if (IS_LOG)
{
vec_elements_flt.val[0] = vmulq_f32(vec_elements_flt.val[0], scale_beta_vec);
vec_elements_flt.val[1] = vmulq_f32(vec_elements_flt.val[1], scale_beta_vec);
@@ -111,17 +144,24 @@ void neon_softmax_logits_1d_quantized(
}
/* Reduce sum */
- const auto sum_16_byte =
+ const float32x4_t sum_16_byte =
vaddq_f32(vaddq_f32(vec_sum.val[0], vec_sum.val[1]), vaddq_f32(vec_sum.val[2], vec_sum.val[3]));
+
+ float sum;
+
+#ifdef __aarch64__
+ sum = wrapper::vaddv(sum_16_byte);
+#else // __aarch64__
auto sum_res = vpadd_f32(vget_high_f32(sum_16_byte), vget_low_f32(sum_16_byte));
sum_res = vpadd_f32(sum_res, sum_res);
sum = wrapper::vgetlane(sum_res, 0);
+#endif // __aarch64__
/* Run remaining elements */
for (; x < input_width; ++x)
{
float element{};
- if (is_log)
+ if (IS_LOG)
{
element = (max_val - in_ptr[x]) * scale_beta;
sum += std::exp(element);
@@ -135,19 +175,22 @@ void neon_softmax_logits_1d_quantized(
tmp_ptr[x] = element;
}
- if (!is_log)
+ if (!IS_LOG)
{
- sum_inversed = 256.f / sum;
+ sum_transformed = 256.f / sum;
}
else
{
- sum = std::log(sum);
+ sum_transformed = std::log(sum);
}
- }
+ } // Compute exponentials and sum
/* Normalize exponentials */
{
constexpr bool is_qasymm8_signed = std::is_same<T, qasymm8_signed_t>::value;
+
+ const float32x4_t sum_vec = vdupq_n_f32(sum_transformed);
+
/* Loop over row and compute softmax */
int x = 0;
for (; x <= (input_width - vec_size); x += vec_size)
@@ -155,23 +198,23 @@ void neon_softmax_logits_1d_quantized(
using int_vec_type = wrapper::traits::neon_vector_t<T, 16>;
float32x4x4_t vec_in = vld4q_f32(tmp_ptr + x);
int_vec_type normalized_value{};
- if (is_log)
+ if (IS_LOG)
{
const float32x4x4_t sub = {
- vsubq_f32(vec_in.val[0], vdupq_n_f32(sum)),
- vsubq_f32(vec_in.val[1], vdupq_n_f32(sum)),
- vsubq_f32(vec_in.val[2], vdupq_n_f32(sum)),
- vsubq_f32(vec_in.val[3], vdupq_n_f32(sum)),
+ vsubq_f32(vec_in.val[0], sum_vec),
+ vsubq_f32(vec_in.val[1], sum_vec),
+ vsubq_f32(vec_in.val[2], sum_vec),
+ vsubq_f32(vec_in.val[3], sum_vec),
};
normalized_value = convert_float_to_int<float32x4x4_t, int_vec_type>(sub);
}
else
{
float32x4x4_t mul = {
- vmulq_f32(vec_in.val[0], vdupq_n_f32(sum_inversed)),
- vmulq_f32(vec_in.val[1], vdupq_n_f32(sum_inversed)),
- vmulq_f32(vec_in.val[2], vdupq_n_f32(sum_inversed)),
- vmulq_f32(vec_in.val[3], vdupq_n_f32(sum_inversed)),
+ vmulq_f32(vec_in.val[0], sum_vec),
+ vmulq_f32(vec_in.val[1], sum_vec),
+ vmulq_f32(vec_in.val[2], sum_vec),
+ vmulq_f32(vec_in.val[3], sum_vec),
};
if (is_qasymm8_signed)
@@ -190,34 +233,31 @@ void neon_softmax_logits_1d_quantized(
/* Run remaining elements */
for (; x < input_width; ++x)
{
- if (is_log)
+ if (IS_LOG)
{
- out_ptr[x] = utils::cast::saturate_cast<T>(tmp_ptr[x] - sum);
+ out_ptr[x] = utils::cast::saturate_cast<T>(tmp_ptr[x] - sum_transformed);
}
else
{
- out_ptr[x] = utils::cast::saturate_cast<T>((tmp_ptr[x] * sum_inversed) -
+ out_ptr[x] = utils::cast::saturate_cast<T>((tmp_ptr[x] * sum_transformed) -
(is_qasymm8_signed ? 128.f : 0));
}
}
- }
+ } // Normalize exponentials
},
- in_it, max_it, out_it);
+ in_it, out_it);
}
-template void neon_softmax_logits_1d_quantized<qasymm8_signed_t>(const ITensor *in,
- const ITensor *max,
- void *const tmp,
- ITensor *out,
- float beta,
- bool is_log,
- const Window &window);
-template void neon_softmax_logits_1d_quantized<qasymm8_t>(const ITensor *in,
- const ITensor *max,
- void *const tmp,
- ITensor *out,
- float beta,
- bool is_log,
- const Window &window);
+template void neon_softmax_quantized<qasymm8_signed_t, true>(
+ const ITensor *in, void *const tmp, ITensor *out, float beta, const Window &window);
+
+template void neon_softmax_quantized<qasymm8_signed_t, false>(
+ const ITensor *in, void *const tmp, ITensor *out, float beta, const Window &window);
+
+template void neon_softmax_quantized<qasymm8_t, true>(
+ const ITensor *in, void *const tmp, ITensor *out, float beta, const Window &window);
+
+template void neon_softmax_quantized<qasymm8_t, false>(
+ const ITensor *in, void *const tmp, ITensor *out, float beta, const Window &window);
} // namespace cpu
} // namespace arm_compute
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-23 18:09:05 +0000