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-rw-r--r--src/cpu/kernels/softmax/generic/neon/impl.cpp596
1 files changed, 596 insertions, 0 deletions
diff --git a/src/cpu/kernels/softmax/generic/neon/impl.cpp b/src/cpu/kernels/softmax/generic/neon/impl.cpp
new file mode 100644
index 0000000000..31baf8a9df
--- /dev/null
+++ b/src/cpu/kernels/softmax/generic/neon/impl.cpp
@@ -0,0 +1,596 @@
+/*
+ * Copyright (c) 2021-2024 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "src/cpu/kernels/softmax/generic/neon/impl.h"
+
+#include "support/SaturateCast.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+template <typename T, bool IS_LOG>
+void neon_softmax_x_quantized(
+ const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window)
+{
+ ARM_COMPUTE_UNUSED(axis);
+
+ 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 input_width = in->info()->valid_region().shape.x();
+
+ 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);
+
+ 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 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;
+
+ 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 vec_max = wrapper::vdup_n(max_val, wrapper::traits::vector_128_tag{});
+
+ /* Init sum to zero */
+ float32x4x4_t vec_sum = {
+ vdupq_n_f32(0.f),
+ vdupq_n_f32(0.f),
+ vdupq_n_f32(0.f),
+ vdupq_n_f32(0.f),
+ };
+
+ /* Loop over row and compute exponentials and sum */
+ 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);
+ float32x4x4_t vec_elements_flt = convert_int_to_float<float32x4x4_t>(vec_elements);
+
+ 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);
+ vec_elements_flt.val[2] = vmulq_f32(vec_elements_flt.val[2], scale_beta_vec);
+ vec_elements_flt.val[3] = vmulq_f32(vec_elements_flt.val[3], scale_beta_vec);
+ vec_sum.val[0] = vaddq_f32(vec_sum.val[0], vexpq_f32(vec_elements_flt.val[0]));
+ vec_sum.val[1] = vaddq_f32(vec_sum.val[1], vexpq_f32(vec_elements_flt.val[1]));
+ vec_sum.val[2] = vaddq_f32(vec_sum.val[2], vexpq_f32(vec_elements_flt.val[2]));
+ vec_sum.val[3] = vaddq_f32(vec_sum.val[3], vexpq_f32(vec_elements_flt.val[3]));
+ }
+ else
+ {
+ vec_elements_flt.val[0] = vexpq_f32(vmulq_f32(vec_elements_flt.val[0], scale_beta_vec));
+ vec_elements_flt.val[1] = vexpq_f32(vmulq_f32(vec_elements_flt.val[1], scale_beta_vec));
+ vec_elements_flt.val[2] = vexpq_f32(vmulq_f32(vec_elements_flt.val[2], scale_beta_vec));
+ vec_elements_flt.val[3] = vexpq_f32(vmulq_f32(vec_elements_flt.val[3], scale_beta_vec));
+ vec_sum.val[0] = vaddq_f32(vec_sum.val[0], vec_elements_flt.val[0]);
+ vec_sum.val[1] = vaddq_f32(vec_sum.val[1], vec_elements_flt.val[1]);
+ vec_sum.val[2] = vaddq_f32(vec_sum.val[2], vec_elements_flt.val[2]);
+ vec_sum.val[3] = vaddq_f32(vec_sum.val[3], vec_elements_flt.val[3]);
+ }
+
+ vst4q_f32(tmp_ptr + x, vec_elements_flt);
+ }
+
+ /* Reduce sum */
+ 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)
+ {
+ element = (max_val - in_ptr[x]) * scale_beta;
+ sum += std::exp(element);
+ }
+ else
+ {
+ element = std::exp((max_val - in_ptr[x]) * scale_beta);
+ sum += element;
+ }
+
+ tmp_ptr[x] = element;
+ }
+
+ if (!IS_LOG)
+ {
+ sum_transformed = 256.f / sum;
+ }
+ else
+ {
+ 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)
+ {
+ 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)
+ {
+ const float32x4x4_t sub = {
+ 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], 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)
+ {
+ const auto offset_vec = wrapper::vdup_n(128.f, wrapper::traits::vector_128_tag{});
+ mul.val[0] = wrapper::vsub(mul.val[0], offset_vec);
+ mul.val[1] = wrapper::vsub(mul.val[1], offset_vec);
+ mul.val[2] = wrapper::vsub(mul.val[2], offset_vec);
+ mul.val[3] = wrapper::vsub(mul.val[3], offset_vec);
+ }
+
+ normalized_value = convert_float_to_int<float32x4x4_t, int_vec_type>(mul);
+ }
+ wrapper::vstore(out_ptr + x, normalized_value);
+ }
+ /* Run remaining elements */
+ for (; x < input_width; ++x)
+ {
+ if (IS_LOG)
+ {
+ 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_transformed) -
+ (is_qasymm8_signed ? 128.f : 0));
+ }
+ }
+ } // Normalize exponentials
+ },
+ in_it, out_it);
+}
+
+template <typename T, bool IS_LOG>
+void neon_softmax_non_x_quantized(
+ const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, 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 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);
+
+ /** SIMD vector tag type. */
+ using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;
+
+ constexpr int vec_size = 16;
+ const ITensorInfo *in_info = in->info();
+ const ITensorInfo *out_info = out->info();
+ const int x_width = in_info->valid_region().shape.x();
+ const int in_axis_stride = in_info->strides_in_bytes()[axis];
+ const int out_axis_stride = out_info->strides_in_bytes()[axis];
+ const int tmp_axis_stride = in_axis_stride;
+ const int axis_width = in_info->dimension(axis);
+ const int end_actual = std::min(window[0].end(), x_width);
+
+ execute_window_loop(
+ window,
+ [&](const Coordinates &winCoords)
+ {
+ const bool vector_exceeds_bounds = ((winCoords[0] + vec_size) > end_actual);
+
+ int num_remaining = (end_actual - winCoords[0]);
+ int num_remaining_full = num_remaining / 4;
+ int num_remaining_partial = num_remaining % 4;
+
+ /* Get pointers */
+ const uint8_t *in_ptr = in_it.ptr();
+ uint8_t *out_ptr = out_it.ptr();
+ uint8_t *tmp_ptr = reinterpret_cast<uint8_t *>(tmp);
+
+ auto vec_max = wrapper::vdup_n(support::cpp11::lowest<T>(), ExactTagType{});
+
+ /* Compute Max */
+ {
+ if (!vector_exceeds_bounds)
+ {
+ int i = 0;
+ for (; i < axis_width; ++i)
+ {
+ const auto current_value =
+ wrapper::vloadq((i * in_axis_stride) + reinterpret_cast<const T *>(in_ptr));
+ vec_max = wrapper::vmax(vec_max, current_value);
+ }
+ }
+ else
+ {
+ int i = 0;
+ for (; i < axis_width; ++i)
+ {
+ const T *const base_ptr_in = ((i * in_axis_stride) + reinterpret_cast<const T *>(in_ptr));
+ int j = 0;
+ for (; j < num_remaining; ++j)
+ {
+ const T current_value = *(base_ptr_in + j);
+ vec_max[j] = std::max(vec_max[j], current_value);
+ }
+ }
+ }
+ } // Compute Max
+
+ float32x4x4_t vec_sum_transformed = {
+ vdupq_n_f32(0.f),
+ vdupq_n_f32(0.f),
+ vdupq_n_f32(0.f),
+ vdupq_n_f32(0.f),
+ };
+
+ /* Compute exponentials and sum */
+ {
+ /* Init sum to zero */
+ float32x4x4_t vec_sum = vec_sum_transformed;
+
+ auto vec_elements = wrapper::vdup_n(static_cast<T>(0), ExactTagType{});
+
+ float32x4x4_t vec_elements_flt;
+
+ if (!vector_exceeds_bounds)
+ {
+ int i = 0;
+ for (; i < axis_width; ++i)
+ {
+ vec_elements = wrapper::vloadq((i * in_axis_stride) + reinterpret_cast<const T *>(in_ptr));
+ vec_elements = wrapper::vqsub(vec_max, vec_elements);
+ vec_elements_flt = convert_int_to_float<float32x4x4_t>(vec_elements);
+
+ 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);
+ vec_elements_flt.val[2] = vmulq_f32(vec_elements_flt.val[2], scale_beta_vec);
+ vec_elements_flt.val[3] = vmulq_f32(vec_elements_flt.val[3], scale_beta_vec);
+ vec_sum.val[0] = vaddq_f32(vec_sum.val[0], vexpq_f32(vec_elements_flt.val[0]));
+ vec_sum.val[1] = vaddq_f32(vec_sum.val[1], vexpq_f32(vec_elements_flt.val[1]));
+ vec_sum.val[2] = vaddq_f32(vec_sum.val[2], vexpq_f32(vec_elements_flt.val[2]));
+ vec_sum.val[3] = vaddq_f32(vec_sum.val[3], vexpq_f32(vec_elements_flt.val[3]));
+ }
+ else
+ {
+ vec_elements_flt.val[0] = vexpq_f32(vmulq_f32(vec_elements_flt.val[0], scale_beta_vec));
+ vec_elements_flt.val[1] = vexpq_f32(vmulq_f32(vec_elements_flt.val[1], scale_beta_vec));
+ vec_elements_flt.val[2] = vexpq_f32(vmulq_f32(vec_elements_flt.val[2], scale_beta_vec));
+ vec_elements_flt.val[3] = vexpq_f32(vmulq_f32(vec_elements_flt.val[3], scale_beta_vec));
+ vec_sum.val[0] = vaddq_f32(vec_sum.val[0], vec_elements_flt.val[0]);
+ vec_sum.val[1] = vaddq_f32(vec_sum.val[1], vec_elements_flt.val[1]);
+ vec_sum.val[2] = vaddq_f32(vec_sum.val[2], vec_elements_flt.val[2]);
+ vec_sum.val[3] = vaddq_f32(vec_sum.val[3], vec_elements_flt.val[3]);
+ }
+ vst4q_f32((i * tmp_axis_stride) + reinterpret_cast<float *>(tmp_ptr), vec_elements_flt);
+ }
+
+ auto vec_256 = wrapper::vdup_n(static_cast<float32_t>(256.f), ExactTagType{});
+ if (!IS_LOG)
+ {
+ vec_sum_transformed.val[0] = wrapper::vdiv(vec_256, vec_sum.val[0]);
+ vec_sum_transformed.val[1] = wrapper::vdiv(vec_256, vec_sum.val[1]);
+ vec_sum_transformed.val[2] = wrapper::vdiv(vec_256, vec_sum.val[2]);
+ vec_sum_transformed.val[3] = wrapper::vdiv(vec_256, vec_sum.val[3]);
+ }
+ else
+ {
+ vec_sum_transformed.val[0] = wrapper::vlog(vec_sum.val[0]);
+ vec_sum_transformed.val[1] = wrapper::vlog(vec_sum.val[1]);
+ vec_sum_transformed.val[2] = wrapper::vlog(vec_sum.val[2]);
+ vec_sum_transformed.val[3] = wrapper::vlog(vec_sum.val[3]);
+ }
+ }
+ else
+ {
+ int i = 0;
+ for (; i < axis_width; ++i)
+ {
+ const T *const base_ptr_in = (i * in_axis_stride) + reinterpret_cast<const T *>(in_ptr);
+ auto vec_elements = wrapper::vdup_n(static_cast<T>(0), ExactTagType{});
+ //vec_els is functionally redundant but is needed as a workaround for a toolchain bug.
+ std::vector<T> vec_els(16);
+
+ for (int k = 0; k < num_remaining_full; ++k)
+ {
+ for (int j = 0; j < 4; ++j)
+ {
+ vec_els[k * 4 + j] = *(base_ptr_in + (4 * k + j));
+ }
+ }
+ for (int j = 0; j < num_remaining_partial; ++j)
+ {
+ vec_els[num_remaining_full * 4 + j] = *(base_ptr_in + (4 * num_remaining_full + j));
+ }
+ for (int q = 0; q < 16; q++)
+ {
+ vec_elements[q] = vec_els[q];
+ }
+ vec_elements = wrapper::vqsub(vec_max, vec_elements);
+ float32x4x4_t vec_elements_flt = convert_int_to_float<float32x4x4_t>(vec_elements);
+
+ 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);
+ vec_elements_flt.val[2] = vmulq_f32(vec_elements_flt.val[2], scale_beta_vec);
+ vec_elements_flt.val[3] = vmulq_f32(vec_elements_flt.val[3], scale_beta_vec);
+ vec_sum.val[0] = vaddq_f32(vec_sum.val[0], vexpq_f32(vec_elements_flt.val[0]));
+ vec_sum.val[1] = vaddq_f32(vec_sum.val[1], vexpq_f32(vec_elements_flt.val[1]));
+ vec_sum.val[2] = vaddq_f32(vec_sum.val[2], vexpq_f32(vec_elements_flt.val[2]));
+ vec_sum.val[3] = vaddq_f32(vec_sum.val[3], vexpq_f32(vec_elements_flt.val[3]));
+ }
+ else
+ {
+ vec_elements_flt.val[0] = vexpq_f32(vmulq_f32(vec_elements_flt.val[0], scale_beta_vec));
+ vec_elements_flt.val[1] = vexpq_f32(vmulq_f32(vec_elements_flt.val[1], scale_beta_vec));
+ vec_elements_flt.val[2] = vexpq_f32(vmulq_f32(vec_elements_flt.val[2], scale_beta_vec));
+ vec_elements_flt.val[3] = vexpq_f32(vmulq_f32(vec_elements_flt.val[3], scale_beta_vec));
+ vec_sum.val[0] = vaddq_f32(vec_sum.val[0], vec_elements_flt.val[0]);
+ vec_sum.val[1] = vaddq_f32(vec_sum.val[1], vec_elements_flt.val[1]);
+ vec_sum.val[2] = vaddq_f32(vec_sum.val[2], vec_elements_flt.val[2]);
+ vec_sum.val[3] = vaddq_f32(vec_sum.val[3], vec_elements_flt.val[3]);
+ }
+
+ float *const base_ptr_tmp = (i * tmp_axis_stride) + reinterpret_cast<float *>(tmp_ptr);
+ for (int k = 0; k < num_remaining_full; ++k)
+ {
+ for (int j = 0; j < 4; ++j)
+ {
+ *(base_ptr_tmp + (4 * k + j)) = vec_elements_flt.val[k][j];
+ }
+ }
+
+ for (int j = 0; j < num_remaining_partial; ++j)
+ {
+ *(base_ptr_tmp + (4 * num_remaining_full + j)) =
+ vec_elements_flt.val[num_remaining_full][j];
+ }
+ }
+
+ auto vec_256 = wrapper::vdup_n(static_cast<float32_t>(256), ExactTagType{});
+ if (!IS_LOG)
+ {
+ vec_sum_transformed.val[0] = wrapper::vdiv(vec_256, vec_sum.val[0]);
+ vec_sum_transformed.val[1] = wrapper::vdiv(vec_256, vec_sum.val[1]);
+ vec_sum_transformed.val[2] = wrapper::vdiv(vec_256, vec_sum.val[2]);
+ vec_sum_transformed.val[3] = wrapper::vdiv(vec_256, vec_sum.val[3]);
+ }
+ else
+ {
+ vec_sum_transformed.val[0] = wrapper::vlog(vec_sum.val[0]);
+ vec_sum_transformed.val[1] = wrapper::vlog(vec_sum.val[1]);
+ vec_sum_transformed.val[2] = wrapper::vlog(vec_sum.val[2]);
+ vec_sum_transformed.val[3] = wrapper::vlog(vec_sum.val[3]);
+ }
+ }
+ } // Compute exponentials and sum
+
+ /* Normalize exponentials */
+ {
+ constexpr bool is_qasymm8_signed = std::is_same<T, qasymm8_signed_t>::value;
+ if (!vector_exceeds_bounds)
+ {
+ int i = 0;
+ for (; i < axis_width; ++i)
+ {
+ using int_vec_type = wrapper::traits::neon_vector_t<T, 16>;
+ float32x4x4_t vec_in = vld4q_f32((i * tmp_axis_stride) + reinterpret_cast<float *>(tmp_ptr));
+
+ int_vec_type normalized_value{};
+
+ if (IS_LOG)
+ {
+ const float32x4x4_t sub = {
+ vsubq_f32(vec_in.val[0], vec_sum_transformed.val[0]),
+ vsubq_f32(vec_in.val[1], vec_sum_transformed.val[1]),
+ vsubq_f32(vec_in.val[2], vec_sum_transformed.val[2]),
+ vsubq_f32(vec_in.val[3], vec_sum_transformed.val[3]),
+ };
+ normalized_value = convert_float_to_int<float32x4x4_t, int_vec_type>(sub);
+ }
+ else
+ {
+ float32x4x4_t mul = {
+ vmulq_f32(vec_in.val[0], vec_sum_transformed.val[0]),
+ vmulq_f32(vec_in.val[1], vec_sum_transformed.val[1]),
+ vmulq_f32(vec_in.val[2], vec_sum_transformed.val[2]),
+ vmulq_f32(vec_in.val[3], vec_sum_transformed.val[3]),
+ };
+
+ if (is_qasymm8_signed)
+ {
+ const auto offset_vec = wrapper::vdup_n(128.f, wrapper::traits::vector_128_tag{});
+ mul.val[0] = wrapper::vsub(mul.val[0], offset_vec);
+ mul.val[1] = wrapper::vsub(mul.val[1], offset_vec);
+ mul.val[2] = wrapper::vsub(mul.val[2], offset_vec);
+ mul.val[3] = wrapper::vsub(mul.val[3], offset_vec);
+ }
+
+ normalized_value = convert_float_to_int<float32x4x4_t, int_vec_type>(mul);
+ }
+ wrapper::vstore((i * out_axis_stride) + reinterpret_cast<T *>(out_ptr), normalized_value);
+ }
+ }
+ else
+ {
+ int i = 0;
+ for (; i < axis_width; ++i)
+ {
+ T *const base_ptr_out = (i * out_axis_stride) + reinterpret_cast<T *>(out_ptr);
+ float *const base_ptr_tmp = (i * tmp_axis_stride) + reinterpret_cast<float *>(tmp_ptr);
+ if (IS_LOG)
+ {
+ for (int k = 0; k < num_remaining_full; ++k)
+ {
+ for (int j = 0; j < 4; ++j)
+ {
+ *(base_ptr_out + (4 * k + j)) = utils::cast::saturate_cast<T>(
+ (*(base_ptr_tmp + (4 * k + j)) - vec_sum_transformed.val[k][j]));
+ }
+ }
+ for (int j = 0; j < num_remaining_partial; ++j)
+ {
+ *(base_ptr_out + (4 * num_remaining_full + j)) =
+ utils::cast::saturate_cast<T>(*(base_ptr_tmp + (4 * num_remaining_full + j)) -
+ vec_sum_transformed.val[num_remaining_full][j]);
+ }
+ }
+ else
+ {
+ for (int k = 0; k < num_remaining_full; ++k)
+ {
+ for (int j = 0; j < 4; ++j)
+ {
+ *(base_ptr_out + (4 * k + j)) = utils::cast::saturate_cast<T>(
+ *(base_ptr_tmp + (4 * k + j)) * vec_sum_transformed.val[k][j] -
+ (is_qasymm8_signed ? 128.f : 0));
+ }
+ }
+ for (int j = 0; j < num_remaining_partial; ++j)
+ {
+ *(base_ptr_out + (4 * num_remaining_full + j)) =
+ utils::cast::saturate_cast<T>(*(base_ptr_tmp + (4 * num_remaining_full + j)) *
+ vec_sum_transformed.val[num_remaining_full][j] -
+ (is_qasymm8_signed ? 128.f : 0));
+ }
+ }
+ }
+ }
+ } // Normalize exponentials
+ },
+ in_it, out_it);
+}
+
+template void neon_softmax_x_quantized<qasymm8_signed_t, true>(
+ const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
+
+template void neon_softmax_x_quantized<qasymm8_signed_t, false>(
+ const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
+
+template void neon_softmax_x_quantized<qasymm8_t, true>(
+ const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
+
+template void neon_softmax_x_quantized<qasymm8_t, false>(
+ const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
+
+template void neon_softmax_non_x_quantized<qasymm8_signed_t, true>(
+ const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
+
+template void neon_softmax_non_x_quantized<qasymm8_signed_t, false>(
+ const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
+
+template void neon_softmax_non_x_quantized<qasymm8_t, true>(
+ const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
+
+template void neon_softmax_non_x_quantized<qasymm8_t, false>(
+ const ITensor *in, void *const tmp, ITensor *out, float beta, int axis, const Window &window);
+} // namespace cpu
+} // namespace arm_compute
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-03 16:59:20 +0000