Multi ISA Technical Debt
* Update json struct meet multi-ISA updates
* Add impl.cpp in kernels where we only have impl.h
Resolves COMPMID-5173
Change-Id: I5da3c4b016a5d0115c4ba46cbfefde7bce518ac1
Signed-off-by: Dana Zlotnik <dana.zlotnik@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7191
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
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 0000000..5654bb5
--- /dev/null
+++ b/src/cpu/kernels/softmax/generic/neon/impl.cpp
@@ -0,0 +1,399 @@
+/*
+ * Copyright (c) 2021-2022 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 "src/core/NEON/NEMath.h"
+#include "src/core/NEON/wrapper/wrapper.h"
+#include "support/SaturateCast.h"
+
+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;
+ }
+
+ *out_ptr = max_val;
+ },
+ input, output);
+}
+
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+template void neon_logits_1d_max<float16_t>(const ITensor *in, ITensor *out, const Window &window);
+#endif //defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+template void neon_logits_1d_max<float>(const ITensor *in, ITensor *out, const Window &window);
+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)
+{
+ 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);
+
+ Iterator in_it(in, window);
+ Iterator max_it(max, window);
+ Iterator out_it(out, window);
+ constexpr int vec_size = 16;
+
+ 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);
+
+ float sum{};
+ float sum_inversed{};
+
+ /* 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 */
+ 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);
+ auto 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 auto 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]));
+ 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);
+
+ /* 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_inversed = 256.f / sum;
+ }
+ else
+ {
+ sum = std::log(sum);
+ }
+ }
+
+ /* Normalize exponentials */
+ {
+ constexpr bool is_qasymm8_signed = std::is_same<T, qasymm8_signed_t>::value;
+ /* 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], 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)),
+ };
+ 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)),
+ };
+
+ 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);
+ }
+ else
+ {
+ out_ptr[x] = utils::cast::saturate_cast<T>((tmp_ptr[x] * sum_inversed) - (is_qasymm8_signed ? 128.f : 0));
+ }
+ }
+ }
+ },
+ in_it, max_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 <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)
+{
+ const int start_x = in->info()->valid_region().anchor.x();
+ 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);
+
+ 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);
+
+ T sum{};
+ T sum_inversed{};
+
+ /* 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 */
+ auto vec_sum = wrapper::vdup_n(static_cast<T>(0), ExactTagType{});
+
+ /* 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::vsub(vec_elements, vec_max);
+ 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));
+ }
+ 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);
+ }
+ wrapper::vstore(tmp_ptr + x, vec_elements);
+ }
+
+ /* Reduce sum */
+ 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);
+
+ /* Run remaining elements */
+ for(; x < input_width; ++x)
+ {
+ T element{};
+
+ if(is_log)
+ {
+ element = (in_ptr[x] - max_val) * beta;
+ sum += std::exp(element);
+ }
+ else
+ {
+ element = std::exp((in_ptr[x] - max_val) * beta);
+ sum += element;
+ }
+ tmp_ptr[x] = element;
+ }
+
+ if(!is_log)
+ {
+ sum_inversed = T(1) / sum;
+ }
+ else
+ {
+ sum = static_cast<T>(std::log(sum));
+ }
+ }
+
+ /* Normalize exponentials */
+ {
+ /* 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)
+ {
+ normalized_value = wrapper::vsub(vec_in, wrapper::vdup_n(static_cast<T>(sum), ExactTagType{}));
+ }
+ else
+ {
+ normalized_value = wrapper::vmul(vec_in, wrapper::vdup_n(static_cast<T>(sum_inversed), ExactTagType{}));
+ }
+ wrapper::vstore(out_ptr + x, normalized_value);
+ }
+ /* Run remaining elements */
+ for(; x < input_width; ++x)
+ {
+ if(is_log)
+ {
+ out_ptr[x] = tmp_ptr[x] - sum;
+ }
+ else
+ {
+ out_ptr[x] = tmp_ptr[x] * sum_inversed;
+ }
+ }
+ }
+ },
+ in_it, max_it, out_it);
+}
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+template void neon_softmax_logits_1d_float<float16_t>(const ITensor *in, const ITensor *max, void *const tmp,
+ ITensor *out, const float beta, bool is_log, const Window &window);
+#endif //defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+template void neon_softmax_logits_1d_float<float>(const ITensor *in, const ITensor *max, void *const tmp,
+ ITensor *out, const float beta, bool is_log, const Window &window);
+} // namespace cpu
+} // namespace arm_compute