Port NEGEMMLowp Part 1
Details:
Port NEGEMMLowpQuantizeDownInt32ScaleKernel to CpuGemmLowpQuantizeDownInt32ScaleKernel
Port NEGEMMLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel to CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel
Port NEGEMMLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel to CpuGemmLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel
Port NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel to CpuGemmLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel
Port NEGEMMLowpOutputStage functions to CpuGemmLowpOutputStage operators
Partially Resolves: COMPMID-4403
Change-Id: I6d5f45e43f35d731d564ed3b5c0e804d2a318fb1
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5833
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp b/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp
new file mode 100644
index 0000000..390e269
--- /dev/null
+++ b/src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.cpp
@@ -0,0 +1,227 @@
+/*
+ * Copyright (c) 2019-2021 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/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "src/core/NEON/NESymm.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 *bias, const ITensorInfo *dst, int min, int max)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::S32);
+ ARM_COMPUTE_RETURN_ERROR_ON(min > max);
+
+ // Check biases if exist
+ if(bias != nullptr)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, bias);
+ ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(src->dimension(0) != bias->dimension(0));
+ }
+
+ if(dst->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::QSYMM16);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, src);
+ }
+
+ return Status{};
+}
+} // namespace
+
+template <bool is_bounded_relu>
+void CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::run_internal(const ITensor *src, const ITensor *bias, ITensor *dst, const Window &window)
+{
+ const int16x8_t min_s16 = vdupq_n_s16(static_cast<int16_t>(_min));
+ const int16x8_t max_s16 = vdupq_n_s16(static_cast<int16_t>(_max));
+
+ ARM_COMPUTE_UNUSED(min_s16);
+ ARM_COMPUTE_UNUSED(max_s16);
+
+ 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());
+
+ Window win_collapsed = window.collapse_if_possible(window, Window::DimZ);
+ win_collapsed.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator in(src, win_collapsed);
+ Iterator out(dst, win_collapsed);
+ if(bias != nullptr)
+ {
+ Window win_biases;
+ win_biases.set(Window::DimX, Window::Dimension(0, 1, 1));
+ win_biases.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+ Iterator bias_i(bias, win_biases);
+ execute_window_loop(win_collapsed, [&](const Coordinates &)
+ {
+ // Compute 16 elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ int32x4x2_t in_s32 =
+ {
+ {
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4)
+ }
+ };
+
+ const int32x4x2_t bias_s32 =
+ {
+ {
+ vld1q_s32(reinterpret_cast<const int32_t *>(bias_i.ptr()) + x + 0),
+ vld1q_s32(reinterpret_cast<const int32_t *>(bias_i.ptr()) + x + 4)
+ }
+ };
+
+ // Add the bias to GEMM's result
+ in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]);
+ in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]);
+
+ vst1q_s16(reinterpret_cast<int16_t *>(out.ptr()) + x, finalize_quantization_int16<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, min_s16, max_s16));
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ const int32_t bias_value = *(reinterpret_cast<const int32_t *>(bias_i.ptr()) + x);
+ int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x);
+
+ // Add bias
+ in_value += bias_value;
+ // Finalize and store the result
+ *(reinterpret_cast<int16_t *>(out.ptr()) + x) = finalize_quantization_int16<is_bounded_relu>(in_value, _result_fixedpoint_multiplier, _result_shift, static_cast<int16_t>(_min),
+ static_cast<int16_t>(_max));
+ }
+ },
+ in, out, bias_i);
+ }
+ else
+ {
+ execute_window_loop(win_collapsed, [&](const Coordinates &)
+ {
+ // Compute 16 elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ int32x4x2_t in_s32 =
+ {
+ {
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0),
+ vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4)
+ }
+ };
+
+ vst1q_s16(reinterpret_cast<int16_t *>(out.ptr()) + x, finalize_quantization_int16<is_bounded_relu>(in_s32, _result_fixedpoint_multiplier, _result_shift, min_s16, max_s16));
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ const int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x);
+ ARM_COMPUTE_UNUSED(in_value);
+ // Finalize and store the result
+ *(reinterpret_cast<int16_t *>(out.ptr()) + x) = finalize_quantization_int16<is_bounded_relu>(in_value, _result_fixedpoint_multiplier, _result_shift, static_cast<int16_t>(_min),
+ static_cast<int16_t>(_max));
+ }
+ },
+ in, out);
+ }
+}
+
+void CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::configure(ITensorInfo *src, ITensorInfo *bias, ITensorInfo *dst, int result_fixedpoint_multiplier, int result_shift,
+ int min, int max)
+{
+ // Perform validate step
+ ARM_COMPUTE_UNUSED(bias, dst);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, bias, dst, min, max));
+
+ _result_fixedpoint_multiplier = result_fixedpoint_multiplier;
+ _result_shift = result_shift;
+ _min = min;
+ _max = max;
+
+ // Output auto inizialitation if not yet initialized
+ auto_init_if_empty(*src, src->clone()->set_data_type(DataType::QSYMM16));
+ // Configure kernel window
+ Window win_config = calculate_max_window(*src, Steps());
+ ICpuKernel::configure(win_config);
+
+ // Check if we need to clamp the result using min and max
+ const bool is_bounded_relu = !(min <= -32768 && max >= 32767);
+ _func = is_bounded_relu ? &CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::run_internal<true> :
+ &CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::run_internal<false>;
+}
+
+Status CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max));
+ return Status{};
+}
+
+void CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::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);
+ ARM_COMPUTE_ERROR_ON_MSG(tensors.empty(), "No inputs provided");
+
+ auto src = tensors.get_const_tensor(TensorType::ACL_SRC);
+ auto bias = tensors.get_const_tensor(TensorType::ACL_BIAS);
+ auto dst = tensors.get_tensor(TensorType::ACL_DST);
+
+ (this->*_func)(src, bias, dst, window);
+}
+
+const char *CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel::name() const
+{
+ return "CpuGemmLowpQuantizeDownInt32ToInt16ScaleByFixedPointKernel";
+}
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute