Move CPU/GPU files from Core/Runtime to the respective backend folders
Legacy structure contained two libraries core/runtime with two backends
in each.
We reduce the core/runtime libraries to a single library thus merging
the backend files
Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: I69545765fe7a730368105cdbd067d3135ec7a174
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/6155
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/cpu/kernels/CpuDequantizeKernel.cpp b/src/cpu/kernels/CpuDequantizeKernel.cpp
new file mode 100644
index 0000000..a2d24f9
--- /dev/null
+++ b/src/cpu/kernels/CpuDequantizeKernel.cpp
@@ -0,0 +1,400 @@
+/*
+ * Copyright (c) 2017-2021 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/CpuDequantizeKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+#include "src/core/CPP/Validate.h"
+#include "src/core/NEON/NEAsymm.h"
+#include "src/core/NEON/NESymm.h"
+#include "src/core/NEON/wrapper/wrapper.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/WindowHelpers.h"
+
+#include <arm_neon.h>
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+namespace
+{
+Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8, DataType::QSYMM16);
+
+ if(dst->tensor_shape().total_size() > 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(dst);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src, dst);
+ }
+
+ return Status{};
+}
+
+template <typename T>
+inline void store_result(T *ptr, const float32x4x4_t &v)
+{
+ ARM_COMPUTE_UNUSED(ptr, v);
+}
+
+template <>
+inline void store_result<float>(float *ptr, const float32x4x4_t &v)
+{
+ wrapper::vstore(ptr, v.val[0]);
+ wrapper::vstore(ptr + 4, v.val[1]);
+ wrapper::vstore(ptr + 8, v.val[2]);
+ wrapper::vstore(ptr + 12, v.val[3]);
+}
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+template <>
+inline void store_result<float16_t>(float16_t *ptr, const float32x4x4_t &v)
+{
+ wrapper::vstore(ptr, vcombine_f16(vcvt_f16_f32(v.val[0]), vcvt_f16_f32(v.val[1])));
+ wrapper::vstore(ptr + 8, vcombine_f16(vcvt_f16_f32(v.val[2]), vcvt_f16_f32(v.val[3])));
+}
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+
+template <typename T>
+inline void store_result(T *ptr, const float32x4x2_t &v)
+{
+ ARM_COMPUTE_UNUSED(ptr, v);
+}
+
+template <>
+inline void store_result<float>(float *ptr, const float32x4x2_t &v)
+{
+ wrapper::vstore(ptr, v.val[0]);
+ wrapper::vstore(ptr + 4, v.val[1]);
+}
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+template <>
+inline void store_result<float16_t>(float16_t *ptr, const float32x4x2_t &v)
+{
+ wrapper::vstore(ptr, vcombine_f16(vcvt_f16_f32(v.val[0]), vcvt_f16_f32(v.val[1])));
+}
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+
+template <typename TOut, typename TIn>
+void run_dequantization_qasymm8(const ITensor *input, ITensor *output, const Window &window)
+{
+ const UniformQuantizationInfo &qinfo = input->info()->quantization_info().uniform();
+ const float scale = qinfo.scale;
+ const int32_t offset = qinfo.offset;
+
+ const int window_step_x = 16;
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+
+ // Collapse window and reset first dimension to handle tail calculations manually
+ Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
+ win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ // Create iterators
+ Iterator in(input, win_collapsed);
+ Iterator out(output, win_collapsed);
+
+ execute_window_loop(win_collapsed, [&](const Coordinates &)
+ {
+ const auto in_ptr = reinterpret_cast<const TIn *>(in.ptr());
+ const auto out_ptr = reinterpret_cast<TOut *>(out.ptr());
+
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const auto vin = wrapper::vloadq(in_ptr + x);
+ const auto vdeq = vdequantize(vin, scale, offset);
+
+ store_result(reinterpret_cast<TOut *>(out_ptr + x), vdeq);
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ auto val = *(in_ptr + x);
+ *(out_ptr + x) = static_cast<TOut>(Qasymm8QuantizationHelper<TIn>::dequantize(val, qinfo));
+ }
+ },
+ in, out);
+}
+
+template <typename T>
+void run_dequantization_qsymm8_per_channel_nchw(const ITensor *input, ITensor *output, const Window &window)
+{
+ const auto scale = input->info()->quantization_info().scale();
+
+ const int window_step_x = 16;
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+
+ // Reset first dimension to handle tail calculations manually
+ Window win(window);
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ // Create iterators
+ Iterator in(input, win);
+ Iterator out(output, win);
+
+ execute_window_loop(win, [&](const Coordinates & id)
+ {
+ const auto in_ptr = reinterpret_cast<const int8_t *>(in.ptr());
+ const auto out_ptr = reinterpret_cast<T *>(out.ptr());
+
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const auto vin = wrapper::vloadq(in_ptr + x);
+ const auto vdeq = vdequantize(vin, scale[id.z()]);
+
+ store_result<T>(reinterpret_cast<T *>(out_ptr + x), vdeq);
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ int8_t val = *(in_ptr + x);
+ *(out_ptr + x) = static_cast<T>(dequantize(val, scale[id.z()]));
+ }
+ },
+ in, out);
+}
+
+template <typename T>
+void run_dequantization_qsymm8_per_channel_nhwc(const ITensor *input, ITensor *output, const Window &window)
+{
+ const auto scale = input->info()->quantization_info().scale();
+
+ const int window_step_x = 16;
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+
+ // Reset first dimension to handle tail calculations manually
+ Window win(window);
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ // Create iterators
+ Iterator in(input, win);
+ Iterator out(output, win);
+
+ execute_window_loop(win, [&](const Coordinates &)
+ {
+ const auto in_ptr = reinterpret_cast<const int8_t *>(in.ptr());
+ const auto out_ptr = reinterpret_cast<T *>(out.ptr());
+
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const float32x4x4_t vscale =
+ {
+ {
+ scale[x + 0], scale[x + 1], scale[x + 2], scale[x + 3],
+ scale[x + 4], scale[x + 5], scale[x + 6], scale[x + 7],
+ scale[x + 8], scale[x + 9], scale[x + 10], scale[x + 11],
+ scale[x + 12], scale[x + 13], scale[x + 14], scale[x + 15]
+ }
+ };
+ const auto vin = wrapper::vloadq(in_ptr + x);
+ const auto vdeq = vdequantize(vin, vscale);
+
+ store_result<T>(reinterpret_cast<T *>(out_ptr + x), vdeq);
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ int8_t val = *(in_ptr + x);
+ *(out_ptr + x) = static_cast<T>(dequantize(val, scale[x]));
+ }
+ },
+ in, out);
+}
+
+template <typename T>
+void run_dequantization_qsymm8(const ITensor *input, ITensor *output, const Window &window)
+{
+ const UniformQuantizationInfo &qinfo = input->info()->quantization_info().uniform();
+ const float scale = qinfo.scale;
+
+ const int window_step_x = 16;
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+
+ // Collapse window and reset first dimension to handle tail calculations manually
+ Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
+ win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ // Create iterators
+ Iterator in(input, win_collapsed);
+ Iterator out(output, win_collapsed);
+
+ execute_window_loop(win_collapsed, [&](const Coordinates &)
+ {
+ const auto in_ptr = reinterpret_cast<const int8_t *>(in.ptr());
+ const auto out_ptr = reinterpret_cast<T *>(out.ptr());
+
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const auto vin = wrapper::vloadq(in_ptr + x);
+ const auto vdeq = vdequantize(vin, scale);
+
+ store_result<T>(reinterpret_cast<T *>(out_ptr + x), vdeq);
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ int8_t val = *(in_ptr + x);
+ *(out_ptr + x) = static_cast<T>(dequantize(val, scale));
+ }
+ },
+ in, out);
+}
+
+template <typename T>
+void run_dequantization_qsymm16(const ITensor *input, ITensor *output, const Window &window)
+{
+ const UniformQuantizationInfo &qinfo = input->info()->quantization_info().uniform();
+ const float scale = qinfo.scale;
+
+ const int window_step_x = 8;
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+
+ // Collapse window and reset first dimension to handle tail calculations manually
+ Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
+ win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ // Create iterators
+ Iterator in(input, win_collapsed);
+ Iterator out(output, win_collapsed);
+
+ execute_window_loop(win_collapsed, [&](const Coordinates &)
+ {
+ const auto in_ptr = reinterpret_cast<const int16_t *>(in.ptr());
+ const auto out_ptr = reinterpret_cast<T *>(out.ptr());
+
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const auto vin = wrapper::vloadq(in_ptr + x);
+ const auto vdeq = vdequantize_int16(vin, scale);
+
+ store_result<T>(reinterpret_cast<T *>(out_ptr + x), vdeq);
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ int16_t val = *(in_ptr + x);
+ *(out_ptr + x) = static_cast<T>(dequantize_qsymm16(val, scale));
+ }
+ },
+ in, out);
+}
+
+template <typename T>
+void run_dequantization_core(const ITensor *input, ITensor *output, const Window &window)
+{
+ switch(input->info()->data_type())
+ {
+ case DataType::QASYMM8:
+ run_dequantization_qasymm8<T, uint8_t>(input, output, window);
+ break;
+ case DataType::QASYMM8_SIGNED:
+ run_dequantization_qasymm8<T, int8_t>(input, output, window);
+ break;
+ case DataType::QSYMM8_PER_CHANNEL:
+ input->info()->data_layout() == DataLayout::NHWC ? run_dequantization_qsymm8_per_channel_nhwc<T>(input, output, window) : run_dequantization_qsymm8_per_channel_nchw<T>(input, output, window);
+ break;
+ case DataType::QSYMM8:
+ run_dequantization_qsymm8<T>(input, output, window);
+ break;
+ case DataType::QSYMM16:
+ run_dequantization_qsymm16<T>(input, output, window);
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Unsupported data type.");
+ }
+}
+} // namespace
+
+void CpuDequantizeKernel::configure(const ITensorInfo *src, ITensorInfo *dst)
+{
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst));
+
+ // Configure kernel window
+ Window win = calculate_max_window(*src, Steps());
+
+ // Output tensor auto initialization if not yet initialized
+ auto_init_if_empty(*dst, src->tensor_shape(), 1, DataType::F32);
+
+ ICpuKernel::configure(win);
+}
+
+Status CpuDequantizeKernel::validate(const ITensorInfo *src, const ITensorInfo *dst)
+{
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst));
+ return Status{};
+}
+
+void CpuDequantizeKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
+{
+ ARM_COMPUTE_UNUSED(info);
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
+
+ const auto src = tensors.get_const_tensor(TensorType::ACL_SRC);
+ auto dst = tensors.get_tensor(TensorType::ACL_DST);
+
+ switch(dst->info()->data_type())
+ {
+ case DataType::F32:
+ run_dequantization_core<float>(src, dst, window);
+ break;
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ run_dequantization_core<float16_t>(src, dst, window);
+ break;
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+ default:
+ ARM_COMPUTE_ERROR("Unsupported data type.");
+ }
+}
+const char *CpuDequantizeKernel::name() const
+{
+ return "CpuDequantizeKernel";
+}
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute