Port Arm(R) Neon(TM) Quantization to new API
Partially resolves: COMPMID-4193
Change-Id: I91dc964d4308687e76127c305a6bedca796f8ba0
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5246
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/cpu/kernels/CpuQuantizationKernel.cpp b/src/core/cpu/kernels/CpuQuantizationKernel.cpp
new file mode 100644
index 0000000..9b1e017
--- /dev/null
+++ b/src/core/cpu/kernels/CpuQuantizationKernel.cpp
@@ -0,0 +1,271 @@
+/*
+ * 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/core/cpu/kernels/CpuQuantizationKernel.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/NEON/NEAsymm.h"
+#include "src/core/NEON/NEMath.h"
+#include "src/core/NEON/wrapper/wrapper.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/WindowHelpers.h"
+
+#include "src/core/CPP/Validate.h"
+
+#include <arm_neon.h>
+#include <map>
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+namespace
+{
+constexpr auto window_step = 16;
+
+Status validate_arguments(const ITensorInfo *src, const ITensorInfo *dst)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
+ ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON(dst->tensor_shape().total_size() == 0);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QASYMM16);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(src, dst);
+
+ return Status{};
+}
+
+template <typename T>
+inline float32x4x4_t load_value(const T *input_ptr)
+{
+ using Tx16_t = typename wrapper::traits::neon_vector<T, 16>::type;
+ return arm_compute::convert_to_float32x4x4<Tx16_t>(wrapper::vloadq(input_ptr));
+}
+
+template <>
+inline float32x4x4_t load_value(const float *input_ptr)
+{
+ return { wrapper::vloadq(input_ptr),
+ wrapper::vloadq(input_ptr + 4),
+ wrapper::vloadq(input_ptr + 8),
+ wrapper::vloadq(input_ptr + 12) };
+}
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+template <>
+inline float32x4x4_t load_value(const float16_t *input_ptr)
+{
+ return { vcvt_f32_f16(wrapper::vload(input_ptr)),
+ vcvt_f32_f16(wrapper::vload(input_ptr + 4)),
+ vcvt_f32_f16(wrapper::vload(input_ptr + 8)),
+ vcvt_f32_f16(wrapper::vload(input_ptr + 12)) };
+}
+
+#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+
+template <typename element_type>
+using vector_type = wrapper::traits::neon_vector_t<element_type, window_step>;
+
+template <typename quantized_type>
+vector_type<quantized_type> vquantize_qasymm8(const float32x4x4_t &qv, const UniformQuantizationInfo &qi);
+
+template <>
+vector_type<uint8_t> vquantize_qasymm8<uint8_t>(const float32x4x4_t &qv, const UniformQuantizationInfo &qi)
+{
+ return vquantize(qv, qi);
+}
+
+template <>
+vector_type<int8_t> vquantize_qasymm8<int8_t>(const float32x4x4_t &qv, const UniformQuantizationInfo &qi)
+{
+ return vquantize_signed(qv, qi);
+}
+
+} // namespace
+
+CpuQuantizationKernel::CpuQuantizationKernel()
+ : _func(nullptr)
+{
+}
+
+void CpuQuantizationKernel::configure(ITensorInfo *src, ITensorInfo *dst)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst));
+
+ static const std::map<std::string, QuantizationFunctionExecutorPtr> quant_map =
+ {
+ { "op_QASYMM8_QASYMM8", &CpuQuantizationKernel::run_quantize_qasymm8<uint8_t, uint8_t> },
+ { "op_QASYMM8_QASYMM8_SIGNED", &CpuQuantizationKernel::run_quantize_qasymm8<uint8_t, int8_t> },
+ { "op_QASYMM8_QASYMM16", &CpuQuantizationKernel::run_quantize_qasymm16<uint8_t> },
+
+ { "op_QASYMM8_SIGNED_QASYMM8", &CpuQuantizationKernel::run_quantize_qasymm8<int8_t, uint8_t> },
+ { "op_QASYMM8_SIGNED_QASYMM8_SIGNED", &CpuQuantizationKernel::run_quantize_qasymm8<int8_t, int8_t> },
+ { "op_QASYMM8_SIGNED_QASYMM16", &CpuQuantizationKernel::run_quantize_qasymm16<int8_t> },
+
+ { "op_F32_QASYMM8", &CpuQuantizationKernel::run_quantize_qasymm8<float, uint8_t> },
+ { "op_F32_QASYMM8_SIGNED", &CpuQuantizationKernel::run_quantize_qasymm8<float, int8_t> },
+ { "op_F32_QASYMM16", &CpuQuantizationKernel::run_quantize_qasymm16<float> },
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ { "op_F16_QASYMM8", &CpuQuantizationKernel::run_quantize_qasymm8<float16_t, uint8_t> },
+ { "op_F16_QASYMM8_SIGNED", &CpuQuantizationKernel::run_quantize_qasymm8<float16_t, int8_t> },
+ { "op_F16_QASYMM16", &CpuQuantizationKernel::run_quantize_qasymm16<float16_t> },
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC*/
+ };
+
+ std::string function_to_call("op_");
+ function_to_call += string_from_data_type(src->data_type()) + "_";
+ function_to_call += string_from_data_type(dst->data_type());
+
+ auto it = quant_map.find(function_to_call);
+
+ if(it == quant_map.end())
+ {
+ ARM_COMPUTE_ERROR("Unsupported combination of input and output data types");
+ }
+ _func = it->second;
+
+ // Configure kernel window
+ Window win_config = calculate_max_window(*src, Steps());
+ ICpuKernel::configure(win_config);
+}
+
+Status CpuQuantizationKernel::validate(const ITensorInfo *src, const ITensorInfo *dst)
+{
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst));
+ return Status{};
+}
+
+template <typename TIn, typename TOut>
+void CpuQuantizationKernel::run_quantize_qasymm8(const ITensor *src, ITensor *dst, const Window &window)
+{
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+
+ const UniformQuantizationInfo uqinfo_in = src->info()->quantization_info().uniform();
+ UniformQuantizationInfo uqinfo = dst->info()->quantization_info().uniform();
+ if(is_data_type_quantized_asymmetric(src->info()->data_type()))
+ {
+ uqinfo = compute_requantization_scale_offset(uqinfo_in, uqinfo);
+ }
+#ifdef __aarch64__
+ constexpr RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_EVEN;
+#else //__aarch64__
+ constexpr RoundingPolicy rounding_policy = RoundingPolicy::TO_ZERO;
+#endif //__aarch64__
+
+ // 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));
+
+ Iterator input(src, win_collapsed);
+ Iterator output(dst, win_collapsed);
+ execute_window_loop(win_collapsed, [&](const Coordinates &)
+ {
+ auto input_ptr = reinterpret_cast<const TIn *>(input.ptr());
+ auto output_ptr = reinterpret_cast<TOut *>(output.ptr());
+
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step); x += window_step)
+ {
+ wrapper::vstore(&output_ptr[x], vquantize_qasymm8<TOut>(load_value(&input_ptr[x]), uqinfo));
+ }
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ output_ptr[x] = Qasymm8QuantizationHelper<TOut>::quantize(input_ptr[x], uqinfo, rounding_policy);
+ }
+ },
+ input, output);
+}
+
+template <typename T>
+void CpuQuantizationKernel::run_quantize_qasymm16(const ITensor *src, ITensor *dst, const Window &window)
+{
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+
+ const UniformQuantizationInfo uqinfo_in = src->info()->quantization_info().uniform();
+ UniformQuantizationInfo uqinfo = dst->info()->quantization_info().uniform();
+ if(is_data_type_quantized_asymmetric(src->info()->data_type()))
+ {
+ uqinfo = compute_requantization_scale_offset(uqinfo_in, uqinfo);
+ }
+#ifdef __aarch64__
+ constexpr RoundingPolicy rounding_policy = RoundingPolicy::TO_NEAREST_EVEN;
+#else //__aarch64__
+ constexpr RoundingPolicy rounding_policy = RoundingPolicy::TO_ZERO;
+#endif //__aarch64__
+
+ // 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));
+
+ Iterator input(src, win_collapsed);
+ Iterator output(dst, win_collapsed);
+ execute_window_loop(win_collapsed, [&](const Coordinates &)
+ {
+ auto input_ptr = reinterpret_cast<const T *>(input.ptr());
+ auto output_ptr = reinterpret_cast<uint16_t *>(output.ptr());
+
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step); x += window_step)
+ {
+ uint16x8x2_t tmp = vquantize_qasymm16(load_value(&input_ptr[x]), uqinfo);
+ vst1q_u16(&output_ptr[x], tmp.val[0]);
+ vst1q_u16(&output_ptr[x + 8], tmp.val[1]);
+ }
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ output_ptr[x] = quantize_qasymm16(input_ptr[x], uqinfo, rounding_policy);
+ }
+ },
+ input, output);
+}
+
+void CpuQuantizationKernel::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(_func == nullptr);
+
+ const auto src = tensors.get_const_tensor(TensorType::ACL_SRC);
+ auto dst = tensors.get_tensor(TensorType::ACL_DST);
+ (this->*_func)(src, dst, window);
+}
+
+const char *CpuQuantizationKernel::name() const
+{
+ return "CpuQuantizationKernel";
+}
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/cpu/kernels/CpuQuantizationKernel.h b/src/core/cpu/kernels/CpuQuantizationKernel.h
new file mode 100644
index 0000000..74fd31f
--- /dev/null
+++ b/src/core/cpu/kernels/CpuQuantizationKernel.h
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 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.
+ */
+#ifndef ARM_COMPUTE_CPU_QUANTIZATIONKERNEL_H
+#define ARM_COMPUTE_CPU_QUANTIZATIONKERNEL_H
+
+#include "src/core/common/Macros.h"
+#include "src/core/cpu/ICpuKernel.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+/** Interface for the quantization layer kernel.
+ *
+ * @note The implementation supports only 3D input tensors
+ *
+ */
+class CpuQuantizationKernel : public ICpuKernel
+{
+public:
+ /** Default constructor */
+ CpuQuantizationKernel();
+ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuQuantizationKernel);
+ /** Set the input, output.
+ *
+ * @param[in] src Source tensor info. The dimensions over the third will be interpreted as batches. Data types supported: QASYMM8/QASYMM8_SIGNED/F32/F16.
+ * @param[out] dst Destination tensor info with the same dimensions of input. Data types supported: QASYMM8/QASYMM8_SIGNED/QASYMM16.
+ *
+ * @note Output auto initialization is not supported by this kernel
+ */
+ void configure(ITensorInfo *src, ITensorInfo *dst);
+ /** Static function to check if given info will lead to a valid configuration of @ref CpuQuantizationKernel
+ *
+ * @param[in] src Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F32/F16.
+ * @param[in] dst Output tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/QASYMM16.
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *src, const ITensorInfo *dst);
+
+ // Inherited methods overridden:
+ void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
+ const char *name() const override;
+
+private:
+ /** Common signature for all the specialised @ref NEQuantizationLayerKernel functions
+ *
+ * @param[in] window Region on which to execute the kernel.
+ */
+ using QuantizationFunctionExecutorPtr = void (CpuQuantizationKernel::*)(const ITensor *src, ITensor *dst, const Window &window);
+ /** Function to apply QASYMM8 or QASYMM8_SIGNED quantization on a tensor.
+ *
+ * @param[in] window Region on which to execute the kernel.
+ */
+ template <typename TIn, typename TOut>
+ void run_quantize_qasymm8(const ITensor *src, ITensor *dst, const Window &window);
+ /** Function to apply QASYMM16 quantization on a tensor.
+ *
+ * @param[in] window Region on which to execute the kernel.
+ */
+ template <typename T>
+ void run_quantize_qasymm16(const ITensor *src, ITensor *dst, const Window &window);
+
+ QuantizationFunctionExecutorPtr _func;
+};
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
+#endif /*ARM_COMPUTE_CPU_QUANTIZATIONKERNEL_H */