COMPMID-3477: Remove padding from NEPixelWiseMultiplicationKernel
Remove padding from all NEPixelWiseMultiplicationKernel functions.
Add test case for U8_U8_S16(input1,input2,output).
Add reference code for U8_U8_S16(input1,input2,output).
Remove window shrink test from NormalizationLayer.
Signed-off-by: Sheri Zhang <sheri.zhang@arm.com>
Change-Id: I28d89790c5527a42f918814a0ee3d6ec4c273532
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3468
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
diff --git a/arm_compute/core/NEON/kernels/NEPixelWiseMultiplicationKernel.h b/arm_compute/core/NEON/kernels/NEPixelWiseMultiplicationKernel.h
index 1a9dd6b..3cb0874 100644
--- a/arm_compute/core/NEON/kernels/NEPixelWiseMultiplicationKernel.h
+++ b/arm_compute/core/NEON/kernels/NEPixelWiseMultiplicationKernel.h
@@ -100,38 +100,36 @@
// Inherited methods overridden:
void run(const Window &window, const ThreadInfo &info) override;
- BorderSize border_size() const override;
private:
/** Common signature for all the specialised multiplication functions with integer scaling factor
*
- * @param[in] input1_ptr Pointer to the first input tensor.
- * @param[in] input2_ptr Pointer to the second input tensor.
- * @param[out] output_ptr Pointer to the output tensor.
- * @param[in] scale Integer scale factor.
+ * @param[in] in1 Input1 tensor object.
+ * @param[in] in2 Input2 tensor object.
+ * @param[out] out Output tensor object.
+ * @param[in] window Region on which to execute the kernel
+ * @param[in] scale Integer scale factor.
*/
- using MulFunctionInt = void(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int scale);
+ using MulFunctionInt = void(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int scale);
/** Common signature for all the specialised multiplication functions with float scaling factor
*
- * @param[in] input1_ptr Pointer to the first input tensor.
- * @param[in] input2_ptr Pointer to the second input tensor.
- * @param[out] output_ptr Pointer to the output tensor.
- * @param[in] scale Float scale factor.
+ * @param[in] in1 Input1 tensor object.
+ * @param[in] in2 Input2 tensor object.
+ * @param[out] out Output tensor object.
+ * @param[in] window Region on which to execute the kernel
+ * @param[in] scale Float scale factor.
*/
- using MulFunctionFloat = void(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, float scale);
+ using MulFunctionFloat = void(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, float scale);
/** Common signature for all the specialised QASYMM8 multiplication functions with float scaling factor
*
- * @param[in] input1_ptr Pointer to the first input tensor.
- * @param[in] input2_ptr Pointer to the second input tensor.
- * @param[out] output_ptr Pointer to the output tensor.
- * @param[in] scale Float scale factor.
- * @param[in] input1_qua_info Quantization Info of tensor input1.
- * @param[in] input2_qua_info Quantization Info of tensor input2.
- * @param[in] output_qua_info Quantization Info of tensor output.
+ * @param[in] in1 Input1 tensor object.
+ * @param[in] in2 Input2 tensor object.
+ * @param[out] out Output tensor object.
+ * @param[in] window Region on which to execute the kernel
+ * @param[in] scale Float scale factor.
*
*/
- using MulFunctionQuantized = void(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, float scale,
- const UniformQuantizationInfo &input1_qua_info, const UniformQuantizationInfo &input2_qua_info, const UniformQuantizationInfo &output_qua_info);
+ using MulFunctionQuantized = void(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, float scale);
MulFunctionFloat *_func_float;
MulFunctionInt *_func_int;
@@ -143,7 +141,6 @@
ITensor *_output;
float _scale;
int _scale_exponent;
- bool _run_optimized_qasymm8;
};
/** Interface for the complex pixelwise multiplication kernel. */
diff --git a/arm_compute/runtime/NEON/functions/NEPixelWiseMultiplication.h b/arm_compute/runtime/NEON/functions/NEPixelWiseMultiplication.h
index 2b31032..d84dff2 100644
--- a/arm_compute/runtime/NEON/functions/NEPixelWiseMultiplication.h
+++ b/arm_compute/runtime/NEON/functions/NEPixelWiseMultiplication.h
@@ -26,13 +26,14 @@
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/NEON/INESimpleFunction.h"
+#include "arm_compute/runtime/NEON/INESimpleFunctionNoBorder.h"
namespace arm_compute
{
class ITensor;
/** Basic function to run @ref NEPixelWiseMultiplicationKernel */
-class NEPixelWiseMultiplication : public INESimpleFunction
+class NEPixelWiseMultiplication : public INESimpleFunctionNoBorder
{
public:
/** Initialise the kernel's inputs, output and convertion policy.
diff --git a/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp b/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp
index ca59e66..b23a20d 100644
--- a/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp
+++ b/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp
@@ -43,8 +43,6 @@
const float32x4_t scale255_constant_f32q = vdupq_n_f32(scale255_constant);
const float32x4_t positive_round_f32q = vdupq_n_f32(0.5f);
-constexpr unsigned int num_elems_processed_per_iteration = 16;
-
inline Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy)
{
ARM_COMPUTE_UNUSED(overflow_policy);
@@ -100,60 +98,6 @@
return Status{};
}
-inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output)
-{
- const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(*input1, *input2);
- const ValidRegion &valid_region = broadcast_pair.second;
-
- // Auto initialize output if not initialized
- {
- ARM_COMPUTE_UNUSED(set_shape_if_empty(*output, input1->tensor_shape()));
-
- if(input1->data_type() == DataType::S16 || input2->data_type() == DataType::S16)
- {
- set_format_if_unknown(*output, Format::S16);
- }
- else if(input1->data_type() == DataType::F32 || input2->data_type() == DataType::F32)
- {
- set_format_if_unknown(*output, Format::F32);
- }
- else if(input1->data_type() == DataType::F16 || input2->data_type() == DataType::F16)
- {
- set_format_if_unknown(*output, Format::F16);
- }
- else if(input1->data_type() == DataType::QASYMM8 || input2->data_type() == DataType::QASYMM8)
- {
- set_data_type_if_unknown(*output, DataType::QASYMM8);
- }
- else if(input1->data_type() == DataType::QASYMM8_SIGNED || input2->data_type() == DataType::QASYMM8_SIGNED)
- {
- set_data_type_if_unknown(*output, DataType::QASYMM8_SIGNED);
- }
- else if(input1->data_type() == DataType::QSYMM16 || input2->data_type() == DataType::QSYMM16)
- {
- set_data_type_if_unknown(*output, DataType::QSYMM16);
- }
- }
-
- // Configure kernel window
- Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
- Window win_input1 = win.broadcast_if_dimension_le_one(*input1);
- Window win_input2 = win.broadcast_if_dimension_le_one(*input2);
-
- AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration);
- AccessWindowHorizontal input2_access(input2, 0, num_elems_processed_per_iteration);
- AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
-
- bool window_changed = update_window_and_padding(win_input1, input1_access)
- || update_window_and_padding(win_input2, input2_access)
- || update_window_and_padding(win, output_access);
-
- output_access.set_valid_region(win, valid_region);
-
- Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
- return std::make_pair(err, win);
-}
-
/* Scales a given vector by 1/255.
*
* @note This does not work for all cases. e.g. for float of 0.49999999999999994 and large floats.
@@ -178,224 +122,390 @@
return vreinterpretq_u16_s16(vcombine_s16(vmovn_s32(tmp_s2), vmovn_s32(tmp_s1)));
}
-inline void mul_saturate_QASYMM8_QASYMM8_QASYMM8_n_opt(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr,
- float32x4_t input1_vscale, int32x4_t input1_voffset, float32x4_t input2_vscale, int32x4_t input2_voffset, float32x4_t output_voffset, float32x4_t vinvscale)
+template <typename T>
+inline typename std::enable_if<std::is_same<T, int8_t>::value, int8x16_t>::type
+vquantize(float32x4x4_t val, const UniformQuantizationInfo &info)
{
- const auto input1 = static_cast<const qasymm8_t *__restrict>(input1_ptr);
- const auto input2 = static_cast<const qasymm8_t *__restrict>(input2_ptr);
- const auto output = static_cast<qasymm8_t *__restrict>(output_ptr);
-
- const qasymm8x16_t input1_q = vld1q_u8(input1);
- const qasymm8x16_t input2_q = vld1q_u8(input2);
-
- // Dequantitize inputs
- float32x4x4_t in1_f32x4x4;
- float32x4x4_t in2_f32x4x4;
- in1_f32x4x4.val[0] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(input1_q))))), input1_voffset)), input1_vscale);
- in1_f32x4x4.val[1] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(input1_q))))), input1_voffset)), input1_vscale);
- in1_f32x4x4.val[2] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(input1_q))))), input1_voffset)), input1_vscale);
- in1_f32x4x4.val[3] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(input1_q))))), input1_voffset)), input1_vscale);
-
- in2_f32x4x4.val[0] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(input2_q))))), input2_voffset)), input2_vscale);
- in2_f32x4x4.val[1] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_low_u8(input2_q))))), input2_voffset)), input2_vscale);
- in2_f32x4x4.val[2] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_high_u8(input2_q))))), input2_voffset)), input2_vscale);
- in2_f32x4x4.val[3] = vmulq_f32(vcvtq_f32_s32(vsubq_s32(vreinterpretq_s32_u32(vmovl_u16(vget_high_u16(vmovl_u8(vget_high_u8(input2_q))))), input2_voffset)), input2_vscale);
-
- float32x4x4_t out_f32x4x4;
- out_f32x4x4.val[0] = vmulq_f32(in1_f32x4x4.val[0], in2_f32x4x4.val[0]);
- out_f32x4x4.val[1] = vmulq_f32(in1_f32x4x4.val[1], in2_f32x4x4.val[1]);
- out_f32x4x4.val[2] = vmulq_f32(in1_f32x4x4.val[2], in2_f32x4x4.val[2]);
- out_f32x4x4.val[3] = vmulq_f32(in1_f32x4x4.val[3], in2_f32x4x4.val[3]);
-
- int32x4x4_t rf;
-#ifdef __aarch64__
- rf.val[0] = vcvtnq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[0], vinvscale));
- rf.val[1] = vcvtnq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[1], vinvscale));
- rf.val[2] = vcvtnq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[2], vinvscale));
- rf.val[3] = vcvtnq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[3], vinvscale));
-#else //__aarch64__
- rf.val[0] = vcvtq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[0], vinvscale));
- rf.val[1] = vcvtq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[1], vinvscale));
- rf.val[2] = vcvtq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[2], vinvscale));
- rf.val[3] = vcvtq_s32_f32(vmlaq_f32(output_voffset, out_f32x4x4.val[3], vinvscale));
-#endif //__aarch64__
- const uint8x8_t pa = vqmovun_s16(vcombine_s16(vqmovn_s32(rf.val[0]), vqmovn_s32(rf.val[1])));
- const uint8x8_t pb = vqmovun_s16(vcombine_s16(vqmovn_s32(rf.val[2]), vqmovn_s32(rf.val[3])));
-
- vst1q_u8(output, vcombine_u8(pa, pb));
+ return vquantize_signed(val, info);
}
-inline void mul_saturate_QASYMM8_SIGNED_QASYMM8_SIGNED_QASYMM8_SIGNED_n(
- const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr,
- float scale, const UniformQuantizationInfo &input1_qua_info, const UniformQuantizationInfo &input2_qua_info,
- const UniformQuantizationInfo &output_qua_info)
-
+template <typename T>
+inline typename std::enable_if<std::is_same<T, uint8_t>::value, uint8x16_t>::type
+vquantize(float32x4x4_t val, const UniformQuantizationInfo &info)
{
- const auto input1 = static_cast<const qasymm8_signed_t *__restrict>(input1_ptr);
- const auto input2 = static_cast<const qasymm8_signed_t *__restrict>(input2_ptr);
- const auto output = static_cast<qasymm8_signed_t *__restrict>(output_ptr);
- const qasymm8x16_signed_t input1_q = vld1q_s8(input1);
- const qasymm8x16_signed_t input2_q = vld1q_s8(input2);
- // Dequantitize inputs
- const float32x4x4_t in1_f32x4x4 = vdequantize(input1_q, input1_qua_info);
- const float32x4x4_t in2_f32x4x4 = vdequantize(input2_q, input2_qua_info);
- const UniformQuantizationInfo tmp_qua_info = { output_qua_info.scale / scale, output_qua_info.offset };
- const float32x4x4_t out_f32x4x4 =
- {
- vmulq_f32(in1_f32x4x4.val[0], in2_f32x4x4.val[0]),
- vmulq_f32(in1_f32x4x4.val[1], in2_f32x4x4.val[1]),
- vmulq_f32(in1_f32x4x4.val[2], in2_f32x4x4.val[2]),
- vmulq_f32(in1_f32x4x4.val[3], in2_f32x4x4.val[3]),
- };
- const int8x16_t result = vquantize_signed(out_f32x4x4, tmp_qua_info);
- vst1q_s8(output, result);
+ return vquantize(val, info);
}
-void mul_saturate_QSYMM16_QSYMM16_QSYMM16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, float scale,
- const UniformQuantizationInfo &input1_qua_info, const UniformQuantizationInfo &input2_qua_info, const UniformQuantizationInfo &output_qua_info)
+template <typename T>
+inline typename std::enable_if<std::is_same<T, int8_t>::value, int8_t>::type
+quantize(float val, const UniformQuantizationInfo &info)
{
- const auto input1 = static_cast<const qsymm16_t *__restrict>(input1_ptr);
- const auto input2 = static_cast<const qsymm16_t *__restrict>(input2_ptr);
- const auto output = static_cast<qsymm16_t *__restrict>(output_ptr);
+ int32_t tmp = static_cast<int32_t>(val / info.scale) + info.offset;
- const qsymm16x8x2_t input1_q =
- {
- {
- vld1q_s16(input1),
- vld1q_s16(input1 + 8),
- }
- };
- const qsymm16x8x2_t input2_q =
- {
- {
- vld1q_s16(input2),
- vld1q_s16(input2 + 8),
- }
- };
+ T tmp_qua = static_cast<T>(tmp > SCHAR_MAX) ? SCHAR_MAX : ((tmp < SCHAR_MIN) ? SCHAR_MIN : tmp);
+ return tmp_qua;
+}
- // Dequantitize inputs
- const float32x4x4_t in1_f32x4x4 = vdequantize(input1_q, input1_qua_info);
- const float32x4x4_t in2_f32x4x4 = vdequantize(input2_q, input2_qua_info);
+template <typename T>
+inline typename std::enable_if<std::is_same<T, uint8_t>::value, uint8_t>::type
+quantize(float val, const UniformQuantizationInfo &info)
+{
+ int32_t tmp = static_cast<int32_t>(val / info.scale) + info.offset;
+
+ T tmp_qua = static_cast<T>((tmp > UCHAR_MAX) ? UCHAR_MAX : tmp);
+ return tmp_qua;
+}
+
+template <typename T>
+inline float dequantize(const T *input, const UniformQuantizationInfo &info)
+{
+ return static_cast<float>((*input) - info.offset) * info.scale;
+}
+
+template <typename T>
+void mul_saturate_quantized_8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, float scale)
+{
+ const UniformQuantizationInfo input1_qua_info = in1->info()->quantization_info().uniform();
+ const UniformQuantizationInfo input2_qua_info = in2->info()->quantization_info().uniform();
+ const UniformQuantizationInfo output_qua_info = out->info()->quantization_info().uniform();
+
+ // Create input windows
+ Window win = window;
+ Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+
+ // Clear X Dimension on execution window as we handle manually
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator input1(in1, input1_win);
+ Iterator input2(in2, input2_win);
+ Iterator output(out, win);
+
+ const 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());
const UniformQuantizationInfo tmp_qua_info = { output_qua_info.scale / scale, output_qua_info.offset };
- const float32x4x4_t out_f32x4x4 =
+ execute_window_loop(win, [&](const Coordinates &)
{
- vmulq_f32(in1_f32x4x4.val[0], in2_f32x4x4.val[0]),
- vmulq_f32(in1_f32x4x4.val[1], in2_f32x4x4.val[1]),
- vmulq_f32(in1_f32x4x4.val[2], in2_f32x4x4.val[2]),
- vmulq_f32(in1_f32x4x4.val[3], in2_f32x4x4.val[3]),
- };
+ const auto input1_ptr = reinterpret_cast<const T *>(input1.ptr());
+ const auto input2_ptr = reinterpret_cast<const T *>(input2.ptr());
+ const auto output_ptr = reinterpret_cast<T *>(output.ptr());
- const qsymm16x8x2_t result = vquantize_qsymm16(out_f32x4x4, tmp_qua_info);
- vst1q_s16(output, result.val[0]);
- vst1q_s16(output + 8, result.val[1]);
+ // Compute window_step_x elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const auto input1_q = wrapper::vloadq(input1_ptr + x);
+ const auto input2_q = wrapper::vloadq(input2_ptr + x);
+
+ // Dequantize inputs
+ const float32x4x4_t in1_f32x4x4 = vdequantize(input1_q, input1_qua_info);
+ const float32x4x4_t in2_f32x4x4 = vdequantize(input2_q, input2_qua_info);
+
+ const float32x4x4_t out_f32x4x4 =
+ {
+ vmulq_f32(in1_f32x4x4.val[0], in2_f32x4x4.val[0]),
+ vmulq_f32(in1_f32x4x4.val[1], in2_f32x4x4.val[1]),
+ vmulq_f32(in1_f32x4x4.val[2], in2_f32x4x4.val[2]),
+ vmulq_f32(in1_f32x4x4.val[3], in2_f32x4x4.val[3]),
+ };
+
+ // Quantize output
+ const auto result = vquantize<T>(out_f32x4x4, tmp_qua_info);
+ wrapper::vstore(output_ptr + x, result);
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ // Dequantize inputs
+ float tmp_in1 = dequantize(input1_ptr + x, input1_qua_info);
+ float tmp_in2 = dequantize(input2_ptr + x, input2_qua_info);
+ float tmp_f = tmp_in1 * tmp_in2;
+
+ // Quantize output
+ const auto tmp_qua = quantize<T>(tmp_f, tmp_qua_info);
+ *(output_ptr + x) = tmp_qua;
+ }
+ },
+ input1, input2, output);
}
-void mul_QSYMM16_QSYMM16_S32_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int scale)
+void mul_saturate_QSYMM16_QSYMM16_QSYMM16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, float scale)
+{
+ const UniformQuantizationInfo input1_qua_info = in1->info()->quantization_info().uniform();
+ const UniformQuantizationInfo input2_qua_info = in2->info()->quantization_info().uniform();
+ const UniformQuantizationInfo output_qua_info = out->info()->quantization_info().uniform();
+
+ // Create input windows
+ Window win = window;
+ Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+
+ // Clear X Dimension on execution window as we handle manually
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator input1(in1, input1_win);
+ Iterator input2(in2, input2_win);
+ Iterator output(out, win);
+
+ 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());
+
+ const UniformQuantizationInfo tmp_qua_info = { output_qua_info.scale / scale, output_qua_info.offset };
+
+ execute_window_loop(win, [&](const Coordinates &)
+ {
+ const auto input1_ptr = reinterpret_cast<const qsymm16_t *>(input1.ptr());
+ const auto input2_ptr = reinterpret_cast<const qsymm16_t *>(input2.ptr());
+ const auto output_ptr = reinterpret_cast<qsymm16_t *>(output.ptr());
+
+ // Compute window_step_x elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const qsymm16x8x2_t input1_q =
+ {
+ {
+ vld1q_s16(input1_ptr + x),
+ vld1q_s16(input1_ptr + x + 8),
+ }
+ };
+ const qsymm16x8x2_t input2_q =
+ {
+ {
+ vld1q_s16(input2_ptr + x),
+ vld1q_s16(input2_ptr + x + 8),
+ }
+ };
+
+ // Dequantize inputs
+ const float32x4x4_t in1_f32x4x4 = vdequantize(input1_q, input1_qua_info);
+ const float32x4x4_t in2_f32x4x4 = vdequantize(input2_q, input2_qua_info);
+
+ const float32x4x4_t out_f32x4x4 =
+ {
+ vmulq_f32(in1_f32x4x4.val[0], in2_f32x4x4.val[0]),
+ vmulq_f32(in1_f32x4x4.val[1], in2_f32x4x4.val[1]),
+ vmulq_f32(in1_f32x4x4.val[2], in2_f32x4x4.val[2]),
+ vmulq_f32(in1_f32x4x4.val[3], in2_f32x4x4.val[3]),
+ };
+
+ const qsymm16x8x2_t result = vquantize_qsymm16(out_f32x4x4, tmp_qua_info);
+ vst1q_s16(output_ptr + x, result.val[0]);
+ vst1q_s16(output_ptr + x + 8, result.val[1]);
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ // Dequantize inputs
+ float tmp_in1 = static_cast<float>(*(input1_ptr + x)) * input1_qua_info.scale;
+ float tmp_in2 = static_cast<float>(*(input2_ptr + x)) * input2_qua_info.scale;
+ float tmp_f = tmp_in1 * tmp_in2;
+
+ // Quantize output, lrintf() has same rounding mode as vcombine_s16
+ int32_t tmp = lrintf(tmp_f / tmp_qua_info.scale);
+ qsymm16_t tmp_qua = static_cast<qsymm16_t>(tmp > SHRT_MAX) ? SHRT_MAX : ((tmp < SHRT_MIN) ? SHRT_MIN : tmp);
+ *(output_ptr + x) = tmp_qua;
+ }
+ },
+ input1, input2, output);
+}
+
+void mul_QSYMM16_QSYMM16_S32(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int scale)
{
ARM_COMPUTE_UNUSED(scale);
- const auto input1 = static_cast<const qsymm16_t *__restrict>(input1_ptr);
- const auto input2 = static_cast<const qsymm16_t *__restrict>(input2_ptr);
- const auto output = static_cast<int32_t *__restrict>(output_ptr);
- const qsymm16x8x2_t input1_q =
- {
- {
- vld1q_s16(input1),
- vld1q_s16(input1 + 8),
- }
- };
- const qsymm16x8x2_t input2_q =
- {
- {
- vld1q_s16(input2),
- vld1q_s16(input2 + 8),
- }
- };
+ // Create input windows
+ Window win = window;
+ Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
- const int32x4x4_t in1_s32 =
- {
- {
- vmovl_s16(vget_low_s16(input1_q.val[0])),
- vmovl_s16(vget_high_s16(input1_q.val[0])),
- vmovl_s16(vget_low_s16(input1_q.val[1])),
- vmovl_s16(vget_high_s16(input1_q.val[1])),
- }
- };
- const int32x4x4_t in2_s32 =
- {
- {
- vmovl_s16(vget_low_s16(input2_q.val[0])),
- vmovl_s16(vget_high_s16(input2_q.val[0])),
- vmovl_s16(vget_low_s16(input2_q.val[1])),
- vmovl_s16(vget_high_s16(input2_q.val[1])),
- }
- };
+ // Clear X Dimension on execution window as we handle manually
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- const int32x4x4_t result =
- {
- {
- vmulq_s32(in1_s32.val[0], in2_s32.val[0]),
- vmulq_s32(in1_s32.val[1], in2_s32.val[1]),
- vmulq_s32(in1_s32.val[2], in2_s32.val[2]),
- vmulq_s32(in1_s32.val[3], in2_s32.val[3]),
- }
- };
+ Iterator input1(in1, input1_win);
+ Iterator input2(in2, input2_win);
+ Iterator output(out, win);
- vst1q_s32(output, result.val[0]);
- vst1q_s32(output + 4, result.val[1]);
- vst1q_s32(output + 8, result.val[2]);
- vst1q_s32(output + 12, result.val[3]);
+ 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());
+
+ execute_window_loop(win, [&](const Coordinates &)
+ {
+ const auto input1_ptr = reinterpret_cast<const qsymm16_t *>(input1.ptr());
+ const auto input2_ptr = reinterpret_cast<const qsymm16_t *>(input2.ptr());
+ const auto output_ptr = reinterpret_cast<int32_t *>(output.ptr());
+
+ // Compute window_step_x elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const qsymm16x8x2_t input1_q =
+ {
+ {
+ vld1q_s16(input1_ptr + x),
+ vld1q_s16(input1_ptr + x + 8),
+ }
+ };
+ const qsymm16x8x2_t input2_q =
+ {
+ {
+ vld1q_s16(input2_ptr + x),
+ vld1q_s16(input2_ptr + x + 8),
+ }
+ };
+
+ const int32x4x4_t in1_s32 =
+ {
+ {
+ vmovl_s16(vget_low_s16(input1_q.val[0])),
+ vmovl_s16(vget_high_s16(input1_q.val[0])),
+ vmovl_s16(vget_low_s16(input1_q.val[1])),
+ vmovl_s16(vget_high_s16(input1_q.val[1])),
+ }
+ };
+ const int32x4x4_t in2_s32 =
+ {
+ {
+ vmovl_s16(vget_low_s16(input2_q.val[0])),
+ vmovl_s16(vget_high_s16(input2_q.val[0])),
+ vmovl_s16(vget_low_s16(input2_q.val[1])),
+ vmovl_s16(vget_high_s16(input2_q.val[1])),
+ }
+ };
+
+ const int32x4x4_t result =
+ {
+ {
+ vmulq_s32(in1_s32.val[0], in2_s32.val[0]),
+ vmulq_s32(in1_s32.val[1], in2_s32.val[1]),
+ vmulq_s32(in1_s32.val[2], in2_s32.val[2]),
+ vmulq_s32(in1_s32.val[3], in2_s32.val[3]),
+ }
+ };
+
+ vst1q_s32(output_ptr + x, result.val[0]);
+ vst1q_s32(output_ptr + x + 4, result.val[1]);
+ vst1q_s32(output_ptr + x + 8, result.val[2]);
+ vst1q_s32(output_ptr + x + 12, result.val[3]);
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ int32_t tmp = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x));
+ *(output_ptr + x) = tmp;
+ }
+ },
+ input1, input2, output);
}
template <bool is_scale255, bool is_sat>
-void mul_U8_U8_U8_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n)
+void mul_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
- const auto input1 = static_cast<const uint8_t *__restrict>(input1_ptr);
- const auto input2 = static_cast<const uint8_t *__restrict>(input2_ptr);
- const auto output = static_cast<uint8_t *__restrict>(output_ptr);
+ // Create input windows
+ Window win = window;
+ Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
- const uint8x16_t ta1 = vld1q_u8(input1);
- const uint8x16_t ta2 = vld1q_u8(input2);
+ // Clear X Dimension on execution window as we handle manually
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- uint16x8_t tmp1_high = vmovl_u8(vget_high_u8(ta1));
- const uint16x8_t tmp2_high = vmovl_u8(vget_high_u8(ta2));
- uint16x8_t tmp1_low = vmovl_u8(vget_low_u8(ta1));
- const uint16x8_t tmp2_low = vmovl_u8(vget_low_u8(ta2));
+ Iterator input1(in1, input1_win);
+ Iterator input2(in2, input2_win);
+ Iterator output(out, win);
- tmp1_high = vmulq_u16(tmp1_high, tmp2_high);
- tmp1_low = vmulq_u16(tmp1_low, tmp2_low);
+ const int window_step_x = 16 / sizeof(uint8_t);
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
- if(is_scale255)
+ execute_window_loop(win, [&](const Coordinates &)
{
- tmp1_high = scale255_U16_U16(tmp1_high);
- tmp1_low = scale255_U16_U16(tmp1_low);
- }
- else
- {
- const int16x8_t vn = vdupq_n_s16(-n);
+ const auto input1_ptr = reinterpret_cast<const uint8_t *>(input1.ptr());
+ const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr());
+ const auto output_ptr = reinterpret_cast<uint8_t *>(output.ptr());
- if(is_sat)
+ // Compute window_step_x elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
- tmp1_high = vqshlq_u16(tmp1_high, vn);
- tmp1_low = vqshlq_u16(tmp1_low, vn);
- }
- else
- {
- tmp1_high = vshlq_u16(tmp1_high, vn);
- tmp1_low = vshlq_u16(tmp1_low, vn);
- }
- }
+ const uint8x16_t ta1 = wrapper::vloadq(input1_ptr + x);
+ const uint8x16_t ta2 = wrapper::vloadq(input2_ptr + x);
- if(is_sat)
- {
- vst1q_u8(output, vcombine_u8(vqmovn_u16(tmp1_low), vqmovn_u16(tmp1_high)));
- }
- else
- {
- vst1q_u8(output, vcombine_u8(vmovn_u16(tmp1_low), vmovn_u16(tmp1_high)));
- }
+ uint16x8_t tmp1_high = vmovl_u8(vget_high_u8(ta1));
+ const uint16x8_t tmp2_high = vmovl_u8(vget_high_u8(ta2));
+ uint16x8_t tmp1_low = vmovl_u8(vget_low_u8(ta1));
+ const uint16x8_t tmp2_low = vmovl_u8(vget_low_u8(ta2));
+
+ tmp1_high = vmulq_u16(tmp1_high, tmp2_high);
+ tmp1_low = vmulq_u16(tmp1_low, tmp2_low);
+
+ if(is_scale255)
+ {
+ tmp1_high = scale255_U16_U16(tmp1_high);
+ tmp1_low = scale255_U16_U16(tmp1_low);
+ }
+ else
+ {
+ const int16x8_t vn = vdupq_n_s16(-n);
+
+ if(is_sat)
+ {
+ tmp1_high = vqshlq_u16(tmp1_high, vn);
+ tmp1_low = vqshlq_u16(tmp1_low, vn);
+ }
+ else
+ {
+ tmp1_high = vshlq_u16(tmp1_high, vn);
+ tmp1_low = vshlq_u16(tmp1_low, vn);
+ }
+ }
+ if(is_sat)
+ {
+ vst1q_u8(output_ptr, vcombine_u8(vqmovn_u16(tmp1_low), vqmovn_u16(tmp1_high)));
+ }
+ else
+ {
+ vst1q_u8(output_ptr, vcombine_u8(vmovn_u16(tmp1_low), vmovn_u16(tmp1_high)));
+ }
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ uint16_t tmp = static_cast<uint16_t>(*(input1_ptr + x)) * static_cast<uint16_t>(*(input2_ptr + x));
+
+ if(is_scale255)
+ {
+ float tmp_f = static_cast<float>(tmp) * scale255_constant;
+ tmp = static_cast<uint16_t>(tmp_f + 0.5f);
+ }
+ else
+ {
+ tmp >>= n;
+ }
+ if(is_sat && tmp > 255)
+ {
+ tmp = 255;
+ }
+ *(output_ptr + x) = static_cast<uint8_t>(tmp);
+ }
+ },
+ input1, input2, output);
}
template <bool is_scale255, bool is_sat>
@@ -468,51 +578,189 @@
}
template <bool is_scale255, bool is_sat>
-void mul_S16_S16_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n)
+void mul_S16_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
- const auto input1 = static_cast<const int16_t *__restrict>(input1_ptr);
- const auto input2 = static_cast<const int16_t *__restrict>(input2_ptr);
- const auto output = static_cast<int16_t *__restrict>(output_ptr);
+ // Create input windows
+ Window win = window;
+ Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
- const int16x8x2_t ta1 =
- {
- {
- vld1q_s16(input1),
- vld1q_s16(input1 + 8),
- }
- };
- const int16x8x2_t ta2 =
- {
- {
- vld1q_s16(input2),
- vld1q_s16(input2 + 8),
- }
- };
- const int16x8x2_t result = mul_S16_S16_S16_n_k<is_scale255, is_sat>(ta1, ta2, n);
+ // Clear X Dimension on execution window as we handle manually
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- vst1q_s16(output, result.val[0]);
- vst1q_s16(output + 8, result.val[1]);
+ Iterator input1(in1, input1_win);
+ Iterator input2(in2, input2_win);
+ Iterator output(out, win);
+
+ 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());
+
+ execute_window_loop(win, [&](const Coordinates &)
+ {
+ const auto input1_ptr = reinterpret_cast<const int16_t *>(input1.ptr());
+ const auto input2_ptr = reinterpret_cast<const int16_t *>(input2.ptr());
+ const auto output_ptr = reinterpret_cast<int16_t *>(output.ptr());
+
+ // Compute window_step_x elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const int16x8x2_t ta1 =
+ {
+ {
+ vld1q_s16(input1_ptr + x),
+ vld1q_s16(input1_ptr + x + 8),
+ }
+ };
+ const int16x8x2_t ta2 =
+ {
+ {
+ vld1q_s16(input2_ptr + x),
+ vld1q_s16(input2_ptr + x + 8),
+ }
+ };
+ const int16x8x2_t result = mul_S16_S16_S16_n_k<is_scale255, is_sat>(ta1, ta2, n);
+
+ vst1q_s16(output_ptr + x, result.val[0]);
+ vst1q_s16(output_ptr + x + 8, result.val[1]);
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ int32_t tmp = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x));
+
+ if(is_scale255)
+ {
+ float tmp_f = static_cast<float>(tmp) * scale255_constant;
+
+ tmp = static_cast<int32_t>(tmp_f + 0.5f);
+ }
+ else
+ {
+ if(tmp >= 0)
+ {
+ tmp >>= n;
+ }
+ else
+ {
+ uint32_t mask = (1u << n) - 1;
+ tmp = (tmp + static_cast<int32_t>(mask)) >> n;
+ }
+ }
+ if(is_sat)
+ {
+ tmp = (tmp > SHRT_MAX) ? SHRT_MAX : ((tmp < SHRT_MIN) ? SHRT_MIN : tmp);
+ }
+ *(output_ptr + x) = static_cast<int16_t>(tmp);
+ }
+ },
+ input1, input2, output);
}
-void mul_F32_F32_F32_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, float scale)
+void mul_F32_F32_F32(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, float scale)
{
- const auto input1 = static_cast<const float *__restrict>(input1_ptr);
- const auto input2 = static_cast<const float *__restrict>(input2_ptr);
- const auto output = static_cast<float *__restrict>(output_ptr);
+ // Create input windows
+ Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
- const float32x4x4_t ta1 = vld4q_f32(input1);
- const float32x4x4_t ta2 = vld4q_f32(input2);
- const float32x4_t scale_vec = vdupq_n_f32(scale);
- const float32x4x4_t result =
+ // Clear X Dimension on execution window as we handle manually
+ Window win = window;
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ constexpr int window_step_x = 16 / sizeof(float);
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+ const bool is_broadcast_across_x = (input1_win.x().step() == 0) || (input2_win.x().step() == 0);
+
+ Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()));
+ Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape()));
+ Iterator output(out, window);
+
+ using ExactTagType = typename wrapper::traits::neon_vector<float, window_step_x>::tag_type;
+
+ if(is_broadcast_across_x)
{
+ const bool is_broadcast_input_2 = input2_win.x().step() == 0;
+ Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
+ Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
+ const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
+ const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
+
+ // Clear X Dimension on execution window as we handle manually
+ non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator broadcast_input(broadcast_tensor, broadcast_win);
+ Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
+ Iterator output(out, win);
+
+ execute_window_loop(win, [&](const Coordinates &)
{
- vmulq_f32(vmulq_f32(ta1.val[0], ta2.val[0]), scale_vec),
- vmulq_f32(vmulq_f32(ta1.val[1], ta2.val[1]), scale_vec),
- vmulq_f32(vmulq_f32(ta1.val[2], ta2.val[2]), scale_vec),
- vmulq_f32(vmulq_f32(ta1.val[3], ta2.val[3]), scale_vec)
- }
- };
- vst4q_f32(output, result);
+ const auto non_broadcast_input_ptr = reinterpret_cast<const float *>(non_broadcast_input.ptr());
+ const auto output_ptr = reinterpret_cast<float *>(output.ptr());
+
+ const float broadcast_value = *reinterpret_cast<const float *>(broadcast_input.ptr());
+ const auto broadcast_value_vec = wrapper::vdup_n(broadcast_value, ExactTagType{});
+ const auto scale_vec = wrapper::vdup_n(scale, ExactTagType{});
+
+ // Compute window_step_x elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const auto non_broadcast_v = wrapper::vloadq(non_broadcast_input_ptr + x);
+ auto res = wrapper::vmul(wrapper::vmul(broadcast_value_vec, non_broadcast_v), scale_vec);
+ wrapper::vstore(output_ptr + x, res);
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ const auto non_broadcast_v = *(non_broadcast_input_ptr + x);
+ *(output_ptr + x) = broadcast_value * non_broadcast_v * scale;
+ }
+ },
+ broadcast_input, non_broadcast_input, output);
+ }
+ else
+ {
+ // Clear X Dimension on execution window as we handle manually
+ input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator input1(in1, input1_win);
+ Iterator input2(in2, input2_win);
+ Iterator output(out, win);
+
+ execute_window_loop(win, [&](const Coordinates &)
+ {
+ const auto input1_ptr = reinterpret_cast<const float *>(input1.ptr());
+ const auto input2_ptr = reinterpret_cast<const float *>(input2.ptr());
+ const auto output_ptr = reinterpret_cast<float *>(output.ptr());
+
+ // Compute window_step_x elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const auto ta1 = wrapper::vloadq(input1_ptr + x);
+ const auto ta2 = wrapper::vloadq(input2_ptr + x);
+ const auto scale_vec = wrapper::vdup_n(scale, ExactTagType{});
+ const auto res = wrapper::vmul(wrapper::vmul(ta1, ta2), scale_vec);
+ wrapper::vstore(output_ptr + x, res);
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ const auto ta1 = *(input1_ptr + x);
+ const auto ta2 = *(input2_ptr + x);
+ *(output_ptr + x) = ta1 * ta2 * scale;
+ }
+ },
+ input1, input2, output);
+ }
}
void c_mul_F32_F32_F32_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr)
@@ -544,138 +792,275 @@
wrapper::vstore(output, res);
}
-void mul_F16_F16_F16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, float scale)
-{
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- const auto input1 = static_cast<const float16_t *__restrict>(input1_ptr);
- const auto input2 = static_cast<const float16_t *__restrict>(input2_ptr);
- const auto output = static_cast<float16_t *__restrict>(output_ptr);
- const float16x8x2_t ta1 =
+void mul_F16_F16_F16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, float scale)
+{
+ // Create input windows
+ Window win = window;
+ Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+
+ // Clear X Dimension on execution window as we handle manually
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+
+ Iterator input1(in1, input1_win);
+ Iterator input2(in2, input2_win);
+ Iterator output(out, win);
+
+ 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());
+
+ execute_window_loop(win, [&](const Coordinates &)
{
+ const auto input1_ptr = reinterpret_cast<const float16_t *>(input1.ptr());
+ const auto input2_ptr = reinterpret_cast<const float16_t *>(input2.ptr());
+ const auto output_ptr = reinterpret_cast<float16_t *>(output.ptr());
+
+ // Compute window_step_x elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
- vld1q_f16(input1),
- vld1q_f16(input1 + 8),
+ const float16x8x2_t ta1 =
+ {
+ {
+ vld1q_f16(input1_ptr + x),
+ vld1q_f16(input1_ptr + x + 8),
+ }
+ };
+ const float16x8x2_t ta2 =
+ {
+ {
+ vld1q_f16(input2_ptr + x),
+ vld1q_f16(input2_ptr + x + 8),
+ }
+ };
+ const float16x8_t scale_vec = vdupq_n_f16(scale);
+ const float16x8x2_t result =
+ {
+ {
+ vmulq_f16(vmulq_f16(ta1.val[0], ta2.val[0]), scale_vec),
+ vmulq_f16(vmulq_f16(ta1.val[1], ta2.val[1]), scale_vec),
+ }
+ };
+ vst1q_f16(output_ptr + x, result.val[0]);
+ vst1q_f16(output_ptr + x + 8, result.val[1]);
}
- };
- const float16x8x2_t ta2 =
- {
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
{
- vld1q_f16(input2),
- vld1q_f16(input2 + 8),
+ const auto ta1 = *(input1_ptr + x);
+ const auto ta2 = *(input2_ptr + x);
+ *(output_ptr + x) = ta1 * ta2 * scale;
}
- };
- const float16x8_t scale_vec = vdupq_n_f16(scale);
- const float16x8x2_t result =
- {
- {
- vmulq_f16(vmulq_f16(ta1.val[0], ta2.val[0]), scale_vec),
- vmulq_f16(vmulq_f16(ta1.val[1], ta2.val[1]), scale_vec),
- }
- };
- vst1q_f16(output, result.val[0]);
- vst1q_f16(output + 8, result.val[1]);
-#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
- ARM_COMPUTE_UNUSED(input1_ptr);
- ARM_COMPUTE_UNUSED(input2_ptr);
- ARM_COMPUTE_UNUSED(output_ptr);
- ARM_COMPUTE_UNUSED(scale);
- ARM_COMPUTE_ERROR("Not supported. Recompile the library with arch=arm64-v8.2-a.");
+ },
+ input1, input2, output);
+}
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
-}
template <bool is_scale255, bool is_sat>
-void mul_U8_U8_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n)
+void mul_U8_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
- const auto input1 = static_cast<const uint8_t *__restrict>(input1_ptr);
- const auto input2 = static_cast<const uint8_t *__restrict>(input2_ptr);
- const auto output = static_cast<int16_t *__restrict>(output_ptr);
+ // Create input windows
+ Window win = window;
+ Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
- const uint8x16_t bv = vld1q_u8(input2);
- const uint8x16_t av = vld1q_u8(input1);
+ // Clear X Dimension on execution window as we handle manually
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- uint16x8_t tmp_low = vmovl_u8(vget_low_u8(av));
- uint16x8_t tmp_high = vmovl_u8(vget_high_u8(av));
- tmp_low = vmulq_u16(tmp_low, vmovl_u8(vget_low_u8(bv)));
- tmp_high = vmulq_u16(tmp_high, vmovl_u8(vget_high_u8(bv)));
+ Iterator input1(in1, input1_win);
+ Iterator input2(in2, input2_win);
+ Iterator output(out, win);
- if(is_scale255)
+ const int window_step_x = 16 / sizeof(uint8_t);
+ const auto window_start_x = static_cast<int>(window.x().start());
+ const auto window_end_x = static_cast<int>(window.x().end());
+
+ execute_window_loop(win, [&](const Coordinates &)
{
- tmp_low = scale255_U16_U16(tmp_low);
- tmp_high = scale255_U16_U16(tmp_high);
- }
- else
- {
- const int16x8_t vn = vdupq_n_s16(-n);
+ const auto input1_ptr = reinterpret_cast<const uint8_t *>(input1.ptr());
+ const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr());
+ const auto output_ptr = reinterpret_cast<int16_t *>(output.ptr());
- if(is_sat)
+ // Compute window_step_x elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
{
- tmp_low = vqshlq_u16(tmp_low, vn);
- tmp_high = vqshlq_u16(tmp_high, vn);
+ const uint8x16_t bv = wrapper::vloadq(input2_ptr + x);
+ const uint8x16_t av = wrapper::vloadq(input1_ptr + x);
+
+ uint16x8_t tmp_low = vmovl_u8(vget_low_u8(av));
+ uint16x8_t tmp_high = vmovl_u8(vget_high_u8(av));
+ tmp_low = vmulq_u16(tmp_low, vmovl_u8(vget_low_u8(bv)));
+ tmp_high = vmulq_u16(tmp_high, vmovl_u8(vget_high_u8(bv)));
+
+ if(is_scale255)
+ {
+ tmp_low = scale255_U16_U16(tmp_low);
+ tmp_high = scale255_U16_U16(tmp_high);
+ }
+ else
+ {
+ const int16x8_t vn = vdupq_n_s16(-n);
+
+ if(is_sat)
+ {
+ tmp_low = vqshlq_u16(tmp_low, vn);
+ tmp_high = vqshlq_u16(tmp_high, vn);
+ }
+ else
+ {
+ tmp_low = vshlq_u16(tmp_low, vn);
+ tmp_high = vshlq_u16(tmp_high, vn);
+ }
+ }
+
+ if(is_sat)
+ {
+ static const uint16x8_t max = vdupq_n_u16(SHRT_MAX);
+
+ tmp_low = vminq_u16(tmp_low, max);
+ tmp_high = vminq_u16(tmp_high, max);
+ }
+
+ vst1q_s16(output_ptr + x, vreinterpretq_s16_u16(tmp_low));
+ vst1q_s16(output_ptr + x + 8, vreinterpretq_s16_u16(tmp_high));
}
- else
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
{
- tmp_low = vshlq_u16(tmp_low, vn);
- tmp_high = vshlq_u16(tmp_high, vn);
+ int32_t tmp = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x));
+
+ if(is_scale255)
+ {
+ float tmp_f = static_cast<float>(tmp) * scale255_constant;
+ tmp = static_cast<int32_t>(tmp_f + 0.5f);
+ }
+ else
+ {
+ tmp >>= n;
+ }
+
+ if(is_sat)
+ {
+ tmp = (tmp > SHRT_MAX) ? SHRT_MAX : tmp;
+ }
+
+ *(output_ptr + x) = static_cast<int16_t>(tmp);
}
- }
-
- if(is_sat)
- {
- static const uint16x8_t max = vdupq_n_u16(SHRT_MAX);
-
- tmp_low = vminq_u16(tmp_low, max);
- tmp_high = vminq_u16(tmp_high, max);
- }
-
- vst1q_s16(output, vreinterpretq_s16_u16(tmp_low));
- vst1q_s16(output + 8, vreinterpretq_s16_u16(tmp_high));
+ },
+ input1, input2, output);
}
template <bool is_scale255, bool is_sat>
-void mul_S16_U8_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n)
+void mul_S16_U8_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
- const auto input1 = static_cast<const int16_t *__restrict>(input1_ptr);
- const auto input2 = static_cast<const uint8_t *__restrict>(input2_ptr);
- const auto output = static_cast<int16_t *__restrict>(output_ptr);
+ // Create input windows
+ Window win = window;
+ Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
- const int16x8x2_t ta1 =
- {
- {
- vld1q_s16(input1),
- vld1q_s16(input1 + 8),
- }
- };
- const uint8x8x2_t ta2u =
- {
- {
- vld1_u8(input2),
- vld1_u8(input2 + 8),
- }
- };
- const int16x8x2_t ta2 =
- {
- {
- vreinterpretq_s16_u16(vmovl_u8(ta2u.val[0])),
- vreinterpretq_s16_u16(vmovl_u8(ta2u.val[1]))
- }
- };
+ // Clear X Dimension on execution window as we handle manually
+ win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
+ input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- const int16x8x2_t result = mul_S16_S16_S16_n_k<is_scale255, is_sat>(ta1, ta2, n);
+ Iterator input1(in1, input1_win);
+ Iterator input2(in2, input2_win);
+ Iterator output(out, win);
- vst1q_s16(output, result.val[0]);
- vst1q_s16(output + 8, result.val[1]);
+ 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());
+
+ execute_window_loop(win, [&](const Coordinates &)
+ {
+ const auto input1_ptr = reinterpret_cast<const int16_t *>(input1.ptr());
+ const auto input2_ptr = reinterpret_cast<const uint8_t *>(input2.ptr());
+ const auto output_ptr = reinterpret_cast<int16_t *>(output.ptr());
+
+ // Compute window_step_x elements per iteration
+ int x = window_start_x;
+ for(; x <= (window_end_x - window_step_x); x += window_step_x)
+ {
+ const int16x8x2_t ta1 =
+ {
+ {
+ vld1q_s16(input1_ptr + x),
+ vld1q_s16(input1_ptr + x + 8),
+ }
+ };
+ const uint8x8x2_t ta2u =
+ {
+ {
+ vld1_u8(input2_ptr + x),
+ vld1_u8(input2_ptr + x + 8),
+ }
+ };
+ const int16x8x2_t ta2 =
+ {
+ {
+ vreinterpretq_s16_u16(vmovl_u8(ta2u.val[0])),
+ vreinterpretq_s16_u16(vmovl_u8(ta2u.val[1]))
+ }
+ };
+
+ const int16x8x2_t result = mul_S16_S16_S16_n_k<is_scale255, is_sat>(ta1, ta2, n);
+
+ vst1q_s16(output_ptr + x, result.val[0]);
+ vst1q_s16(output_ptr + x + 8, result.val[1]);
+ }
+
+ // Compute left-over elements
+ for(; x < window_end_x; ++x)
+ {
+ int32_t tmp = static_cast<int32_t>(*(input1_ptr + x)) * static_cast<int32_t>(*(input2_ptr + x));
+
+ if(is_scale255)
+ {
+ float tmp_f = static_cast<float>(tmp) * scale255_constant;
+
+ tmp = static_cast<int32_t>(tmp_f + 0.5f);
+ }
+ else
+ {
+ if(tmp >= 0)
+ {
+ tmp >>= n;
+ }
+ else
+ {
+ uint32_t mask = (1u << n) - 1;
+ tmp = (tmp + static_cast<int32_t>(mask)) >> n;
+ }
+ }
+ if(is_sat)
+ {
+ tmp = (tmp > SHRT_MAX) ? SHRT_MAX : ((tmp < SHRT_MIN) ? SHRT_MIN : tmp);
+ }
+ *(output_ptr + x) = static_cast<int16_t>(tmp);
+ }
+ },
+ input1, input2, output);
}
template <bool is_scale255, bool is_sat>
-void mul_U8_S16_S16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n)
+void mul_U8_S16_S16(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window, int n)
{
// Simply swap the two input buffers
- mul_S16_U8_S16_n<is_scale255, is_sat>(input2_ptr, input1_ptr, output_ptr, n);
+ mul_S16_U8_S16<is_scale255, is_sat>(in2, in1, out, window, n);
}
} // namespace
NEPixelWiseMultiplicationKernel::NEPixelWiseMultiplicationKernel()
- : _func_float(nullptr), _func_int(nullptr), _func_quantized(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _scale{ 0 }, _scale_exponent{ 0 }, _run_optimized_qasymm8(false)
+ : _func_float(nullptr), _func_int(nullptr), _func_quantized(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _scale{ 0 }, _scale_exponent{ 0 }
{
}
@@ -686,19 +1071,21 @@
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info(), scale, overflow_policy, rounding_policy));
- // Configure kernel window
- auto win_config = validate_and_configure_window(input1->info(), input2->info(), output->info());
- ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(*input1->info(), *input2->info());
+ const TensorShape &out_shape = broadcast_pair.first;
+ const ValidRegion &valid_region = broadcast_pair.second;
- _input1 = input1;
- _input2 = input2;
- _output = output;
- _scale = scale;
- _scale_exponent = 0;
- _func_quantized = nullptr;
- _func_int = nullptr;
- _func_float = nullptr;
- _run_optimized_qasymm8 = false;
+ // Auto initialize output if not initialized
+ set_shape_if_empty(*output->info(), out_shape);
+
+ _input1 = input1;
+ _input2 = input2;
+ _output = output;
+ _scale = scale;
+ _scale_exponent = 0;
+ _func_quantized = nullptr;
+ _func_int = nullptr;
+ _func_float = nullptr;
bool is_scale_255 = false;
// Check and validate scaling factor
@@ -722,93 +1109,109 @@
const DataType dt_output = output->info()->data_type();
const bool is_sat = (overflow_policy == ConvertPolicy::SATURATE);
- if(dt_input1 == DataType::QASYMM8 && dt_input2 == DataType::QASYMM8)
+ switch(dt_input1)
{
- _run_optimized_qasymm8 = true;
- }
- else if(dt_input1 == DataType::QASYMM8_SIGNED && dt_input2 == DataType::QASYMM8_SIGNED)
- {
- _func_quantized = &mul_saturate_QASYMM8_SIGNED_QASYMM8_SIGNED_QASYMM8_SIGNED_n;
- }
- else if(dt_input1 == DataType::QSYMM16 && dt_input2 == DataType::QSYMM16 && dt_output == DataType::QSYMM16)
- {
- _func_quantized = &mul_saturate_QSYMM16_QSYMM16_QSYMM16_n;
- }
- else if(dt_input1 == DataType::QSYMM16 && dt_input2 == DataType::QSYMM16 && dt_output == DataType::S32)
- {
- _func_int = &mul_QSYMM16_QSYMM16_S32_n;
- }
- else if(DataType::U8 == dt_input1 && DataType::U8 == dt_input2 && DataType::U8 == dt_output)
- {
- if(is_scale_255)
- {
- _func_int = is_sat ? &mul_U8_U8_U8_n<true, true> : &mul_U8_U8_U8_n<true, false>;
- }
- else
- {
- _func_int = is_sat ? &mul_U8_U8_U8_n<false, true> : &mul_U8_U8_U8_n<false, false>;
- }
- }
- else if(DataType::S16 == dt_input1 && DataType::S16 == dt_input2 && DataType::S16 == dt_output)
- {
- if(is_scale_255)
- {
- _func_int = is_sat ? &mul_S16_S16_S16_n<true, true> : &mul_S16_S16_S16_n<true, false>;
- }
- else
- {
- _func_int = is_sat ? &mul_S16_S16_S16_n<false, true> : &mul_S16_S16_S16_n<false, false>;
- }
- }
- else if(DataType::S16 == dt_input1 && DataType::U8 == dt_input2 && DataType::S16 == dt_output)
- {
- if(is_scale_255)
- {
- _func_int = is_sat ? &mul_S16_U8_S16_n<true, true> : &mul_S16_U8_S16_n<true, false>;
- }
- else
- {
- _func_int = is_sat ? &mul_S16_U8_S16_n<false, true> : &mul_S16_U8_S16_n<false, false>;
- }
- }
- else if(DataType::U8 == dt_input1 && DataType::S16 == dt_input2 && DataType::S16 == dt_output)
- {
- if(is_scale_255)
- {
- _func_int = is_sat ? &mul_U8_S16_S16_n<true, true> : &mul_U8_S16_S16_n<true, false>;
- }
- else
- {
- _func_int = is_sat ? &mul_U8_S16_S16_n<false, true> : &mul_U8_S16_S16_n<false, false>;
- }
- }
- else if(DataType::U8 == dt_input1 && DataType::U8 == dt_input2 && DataType::S16 == dt_output)
- {
- if(is_scale_255)
- {
- _func_int = is_sat ? &mul_U8_U8_S16_n<true, true> : &mul_U8_U8_S16_n<true, false>;
- }
- else
- {
- _func_int = is_sat ? &mul_U8_U8_S16_n<false, true> : &mul_U8_U8_S16_n<false, false>;
- }
- }
- else if(DataType::F16 == dt_input1 && DataType::F16 == dt_input2 && DataType::F16 == dt_output)
- {
- _func_float = &mul_F16_F16_F16_n;
- _func_int = nullptr;
- }
- else if(DataType::F32 == dt_input1 && DataType::F32 == dt_input2 && DataType::F32 == dt_output)
- {
- _func_float = &mul_F32_F32_F32_n;
- _func_int = nullptr;
- }
- else
- {
- ARM_COMPUTE_ERROR("You called with the wrong img formats");
+ case DataType::QASYMM8:
+ if(dt_input2 == DataType::QASYMM8 && dt_output == DataType::QASYMM8)
+ {
+ _func_quantized = &mul_saturate_quantized_8<uint8_t>;
+ }
+ break;
+ case DataType::QASYMM8_SIGNED:
+ if(dt_input2 == DataType::QASYMM8_SIGNED)
+ {
+ _func_quantized = &mul_saturate_quantized_8<int8_t>;
+ ;
+ }
+ break;
+ case DataType::QSYMM16:
+ if(dt_input2 == DataType::QSYMM16 && dt_output == DataType::QSYMM16)
+ {
+ _func_quantized = &mul_saturate_QSYMM16_QSYMM16_QSYMM16;
+ }
+ else if(dt_input2 == DataType::QSYMM16 && dt_output == DataType::S32)
+ {
+ _func_int = &mul_QSYMM16_QSYMM16_S32;
+ }
+ break;
+ case DataType::S16:
+ if(DataType::U8 == dt_input2 && DataType::S16 == dt_output)
+ {
+ if(is_scale_255)
+ {
+ _func_int = is_sat ? &mul_S16_U8_S16<true, true> : &mul_S16_U8_S16<true, false>;
+ }
+ else
+ {
+ _func_int = is_sat ? &mul_S16_U8_S16<false, true> : &mul_S16_U8_S16<false, false>;
+ }
+ }
+ if(DataType::S16 == dt_input2 && DataType::S16 == dt_output)
+ {
+ if(is_scale_255)
+ {
+ _func_int = is_sat ? &mul_S16_S16_S16<true, true> : &mul_S16_S16_S16<true, false>;
+ }
+ else
+ {
+ _func_int = is_sat ? &mul_S16_S16_S16<false, true> : &mul_S16_S16_S16<false, false>;
+ }
+ }
+ break;
+ case DataType::U8:
+ if(DataType::U8 == dt_input2 && DataType::U8 == dt_output)
+ {
+ if(is_scale_255)
+ {
+ _func_int = is_sat ? &mul_U8_U8_U8<true, true> : &mul_U8_U8_U8<true, false>;
+ }
+ else
+ {
+ _func_int = is_sat ? &mul_U8_U8_U8<false, true> : &mul_U8_U8_U8<false, false>;
+ }
+ }
+ else if(DataType::U8 == dt_input2 && DataType::S16 == dt_output)
+ {
+ if(is_scale_255)
+ {
+ _func_int = is_sat ? &mul_U8_U8_S16<true, true> : &mul_U8_U8_S16<true, false>;
+ }
+ else
+ {
+ _func_int = is_sat ? &mul_U8_U8_S16<false, true> : &mul_U8_U8_S16<false, false>;
+ }
+ }
+ else if(DataType::S16 == dt_input2 && DataType::S16 == dt_output)
+ {
+ if(is_scale_255)
+ {
+ _func_int = is_sat ? &mul_U8_S16_S16<true, true> : &mul_U8_S16_S16<true, false>;
+ }
+ else
+ {
+ _func_int = is_sat ? &mul_U8_S16_S16<false, true> : &mul_U8_S16_S16<false, false>;
+ }
+ }
+ break;
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+ case DataType::F16:
+ _func_float = &mul_F16_F16_F16;
+ break;
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+ case DataType::F32:
+ _func_float = &mul_F32_F32_F32;
+ break;
+ default:
+ ARM_COMPUTE_ERROR("You called with the wrong img formats");
}
- INEKernel::configure(win_config.second);
+ // Configure kernel window
+ Coordinates coord;
+ coord.set_num_dimensions(output->info()->num_dimensions());
+ output->info()->set_valid_region(valid_region);
+ Window win = calculate_max_window(valid_region, Steps());
+
+ INEKernel::configure(win);
}
Status NEPixelWiseMultiplicationKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float scale, ConvertPolicy overflow_policy,
@@ -816,7 +1219,6 @@
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input1, input2, output, scale, overflow_policy, rounding_policy));
- ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input1->clone().get(), input2->clone().get(), output->clone().get()).first);
return Status{};
}
@@ -827,97 +1229,21 @@
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
- const TensorShape &in_shape1 = _input1->info()->tensor_shape();
- const TensorShape &in_shape2 = _input2->info()->tensor_shape();
- const TensorShape &out_shape = _output->info()->tensor_shape();
-
- bool can_collapse = true;
- if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1)
+ if(_func_quantized != nullptr)
{
- can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
- for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); ++d)
- {
- can_collapse = (in_shape1[d] == in_shape2[d]);
- }
- }
-
- bool has_collapsed = false;
- Window collapsed = can_collapse ? window.collapse_if_possible(INEKernel::window(), Window::DimZ, &has_collapsed) : window;
-
- const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
- const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;
-
- Window slice = collapsed.first_slice_window_3D();
- Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
- Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);
-
- Iterator input1(_input1, slice_input1);
- Iterator input2(_input2, slice_input2);
- Iterator output(_output, slice);
-
- if((_run_optimized_qasymm8) || (_func_quantized != nullptr))
- {
- if(_run_optimized_qasymm8)
- {
- const int32x4_t input1_voffset = vdupq_n_s32(_input1->info()->quantization_info().uniform().offset);
- const float32x4_t input1_vscale = vdupq_n_f32(_input1->info()->quantization_info().uniform().scale);
- const int32x4_t input2_voffset = vdupq_n_s32(_input2->info()->quantization_info().uniform().offset);
- const float32x4_t input2_vscale = vdupq_n_f32(_input2->info()->quantization_info().uniform().scale);
- const float32x4_t output_voffset = vdupq_n_f32(static_cast<float>(_output->info()->quantization_info().uniform().offset));
- const float output_scale = _output->info()->quantization_info().uniform().scale;
- const float32x4_t vinvscale = vdupq_n_f32(1.f / (output_scale / _scale));
-
- execute_window_loop(collapsed, [&](const Coordinates &)
- {
- mul_saturate_QASYMM8_QASYMM8_QASYMM8_n_opt(input1.ptr(), input2.ptr(), output.ptr(),
- input1_vscale, input1_voffset, input2_vscale, input2_voffset, output_voffset, vinvscale);
- ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input1));
- ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input2));
- },
- input1, input2, output);
- }
- else
- {
- execute_window_loop(collapsed, [&](const Coordinates &)
- {
- (*_func_quantized)(input1.ptr(), input2.ptr(), output.ptr(), _scale,
- _input1->info()->quantization_info().uniform(), _input2->info()->quantization_info().uniform(), _output->info()->quantization_info().uniform());
- ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input1));
- ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input2));
- },
- input1, input2, output);
- }
+ (*_func_quantized)(_input1, _input2, _output, window, _scale);
}
else if(_func_int != nullptr)
{
- execute_window_loop(collapsed, [&](const Coordinates &)
- {
- (*_func_int)(input1.ptr(), input2.ptr(), output.ptr(), _scale_exponent);
- ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input1));
- ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input2));
- },
- input1, input2, output);
+ (*_func_int)(_input1, _input2, _output, window, _scale_exponent);
}
else
{
ARM_COMPUTE_ERROR_ON(_func_float == nullptr);
- execute_window_loop(collapsed, [&](const Coordinates &)
- {
- (*_func_float)(input1.ptr(), input2.ptr(), output.ptr(), _scale);
- ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input1));
- ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input2));
- },
- input1, input2, output);
+ (*_func_float)(_input1, _input2, _output, window, _scale);
}
}
-BorderSize NEPixelWiseMultiplicationKernel::border_size() const
-{
- const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
- const unsigned int border = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
- return BorderSize{ 0, border, 0, 0 };
-}
-
namespace
{
constexpr unsigned int num_elems_processed_per_iteration_complex = 2;
diff --git a/src/runtime/NEON/functions/NEPixelWiseMultiplication.cpp b/src/runtime/NEON/functions/NEPixelWiseMultiplication.cpp
index eaf233b..95bc08a 100644
--- a/src/runtime/NEON/functions/NEPixelWiseMultiplication.cpp
+++ b/src/runtime/NEON/functions/NEPixelWiseMultiplication.cpp
@@ -38,16 +38,6 @@
auto k = arm_compute::support::cpp14::make_unique<NEPixelWiseMultiplicationKernel>();
k->configure(input1, input2, output, scale, overflow_policy, rounding_policy);
_kernel = std::move(k);
-
- if(output->info()->dimension(0) > 1)
- {
- ITensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2;
-
- if(broadcasted_info->info()->dimension(0) == 1)
- {
- _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE);
- }
- }
}
Status NEPixelWiseMultiplication::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, float scale, ConvertPolicy overflow_policy, RoundingPolicy rounding_policy,
const ActivationLayerInfo &act_info)
@@ -62,16 +52,6 @@
auto k = arm_compute::support::cpp14::make_unique<NEComplexPixelWiseMultiplicationKernel>();
k->configure(input1, input2, output);
_kernel = std::move(k);
-
- if(output->info()->dimension(0) > 1)
- {
- ITensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2;
-
- if(broadcasted_info->info()->dimension(0) == 1)
- {
- _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE);
- }
- }
}
Status NEComplexPixelWiseMultiplication::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const ActivationLayerInfo &act_info)
diff --git a/tests/validation/NEON/NormalizationLayer.cpp b/tests/validation/NEON/NormalizationLayer.cpp
index f72d156..e4f7207 100644
--- a/tests/validation/NEON/NormalizationLayer.cpp
+++ b/tests/validation/NEON/NormalizationLayer.cpp
@@ -68,24 +68,21 @@
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching shapes
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Even normalization
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Non implemented IN_MAP_2D
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Window shrink
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
}),
framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16),
TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
- TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
})),
framework::dataset::make("NormInfo", { NormalizationLayerInfo(NormType::IN_MAP_1D, 5),
NormalizationLayerInfo(NormType::IN_MAP_1D, 5),
NormalizationLayerInfo(NormType::IN_MAP_1D, 4),
NormalizationLayerInfo(NormType::IN_MAP_2D, 5),
- NormalizationLayerInfo(NormType::IN_MAP_1D, 5),
NormalizationLayerInfo(NormType::CROSS_MAP, 1),
})),
- framework::dataset::make("Expected", { false, false, false, false, false, true })),
+ framework::dataset::make("Expected", { false, false, false, true, true })),
input_info, output_info, norm_info, expected)
{
bool is_valid = bool(NENormalizationLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), norm_info));
@@ -110,9 +107,7 @@
NENormalizationLayer norm;
norm.configure(&src, &dst, info);
- // To enable check on src as soon as NEPixelWiseMultiplicationKernel stops using padding anymore: COMPMID-3477
- //validate(src.info()->padding(), PaddingSize(0,0,0,0));
- validate(dst.info()->padding(), PaddingSize());
+ validate(src.info()->padding(), PaddingSize(0, 0, 0, 0));
}
template <typename T>
diff --git a/tests/validation/NEON/PixelWiseMultiplication.cpp b/tests/validation/NEON/PixelWiseMultiplication.cpp
index 9c0417b..29eaf0c 100644
--- a/tests/validation/NEON/PixelWiseMultiplication.cpp
+++ b/tests/validation/NEON/PixelWiseMultiplication.cpp
@@ -83,16 +83,17 @@
// *INDENT-OFF*
// clang-format off
-#define PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(TEST_NAME, FIXTURE, MODE, SHAPES, DT1, DT2, SCALE, RP, INPLACE_DATASET, VALIDATE) \
- FIXTURE_DATA_TEST_CASE(TEST_NAME, NEPixelWiseMultiplication##FIXTURE, framework::DatasetMode::MODE, \
- combine(combine(combine(combine(combine(combine( \
+#define PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(TEST_NAME, FIXTURE, MODE, SHAPES, DT1, DT2, DT3, SCALE, RP, INPLACE_DATASET, VALIDATE) \
+ FIXTURE_DATA_TEST_CASE(TEST_NAME, NEPixelWiseMultiplication##FIXTURE, framework::DatasetMode::MODE, \
+ combine(combine(combine(combine(combine(combine(combine( \
datasets::SHAPES, \
framework::dataset::make("DataType1", DataType::DT1)), \
framework::dataset::make("DataType2", DataType::DT2)), \
+ framework::dataset::make("DataType3", DataType::DT3)), \
framework::dataset::make("Scale", std::move(SCALE))), \
datasets::ConvertPolicies()), \
- framework::dataset::make("RoundingPolicy", RoundingPolicy::RP)), \
- (INPLACE_DATASET))) \
+ framework::dataset::make("RoundingPolicy", RoundingPolicy::RP)), \
+ (INPLACE_DATASET))) \
{ \
VALIDATE \
}
@@ -115,6 +116,7 @@
using NEPixelWiseMultiplicationToF32Fixture = PixelWiseMultiplicationValidationFixture<Tensor, Accessor, NEPixelWiseMultiplication, T, float>;
template <typename T>
using NEPixelWiseMultiplicationBroadcastFixture = PixelWiseMultiplicationBroadcastValidationFixture<Tensor, Accessor, NEPixelWiseMultiplication, T, float>;
+using NEPixelWiseMultiplicationU8U8ToS16Fixture = PixelWiseMultiplicationValidationFixture<Tensor, Accessor, NEPixelWiseMultiplication, uint8_t, uint8_t, int16_t>;
TEST_SUITE(NEON)
TEST_SUITE(PixelWiseMultiplication)
@@ -193,7 +195,7 @@
ConvertPolicy::WRAP,
})),
- framework::dataset::make("Expected", { true, true, false, false, false, false, false, false, true , false, false, true, false })),
+ framework::dataset::make("Expected", { true, true, true, false, false, false, false, false, true , false, false, true, false })),
input1_info, input2_info, output_info, scale, policy, expected)
{
bool has_error = bool(NEPixelWiseMultiplication::validate(&input1_info.clone()->set_is_resizable(false), &input2_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), scale, policy, RoundingPolicy::TO_ZERO));
@@ -376,18 +378,48 @@
TEST_SUITE_END() // QSYMM16toS32
TEST_SUITE_END() // Quantized
+TEST_SUITE(U8U8toS16)
+
+FIXTURE_DATA_TEST_CASE(RunSmall, NEPixelWiseMultiplicationU8U8ToS16Fixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(combine(combine(datasets::SmallShapes(),
+ framework::dataset::make("DataTypeIn1", DataType::U8)),
+ framework::dataset::make("DataTypeIn2", DataType::U8)),
+ framework::dataset::make("DataTypeOut", DataType::S16)),
+ framework::dataset::make("Scale", { scale_255 })),
+ datasets::ConvertPolicies()),
+ framework::dataset::make("RoundingPolicy", RoundingPolicy::TO_NEAREST_UP)),
+ framework::dataset::make("InPlace", { false })))
+{
+ // Validate output
+ validate_wrap(Accessor(_target), _reference, AbsoluteTolerance<int16_t>(1), 0.f);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall1, NEPixelWiseMultiplicationU8U8ToS16Fixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(combine(combine(combine(datasets::SmallShapes(),
+ framework::dataset::make("DataTypeIn1", DataType::U8)),
+ framework::dataset::make("DataTypeIn2", DataType::U8)),
+ framework::dataset::make("DataTypeOut", DataType::S16)),
+ framework::dataset::make("Scale", { scale_other })),
+ datasets::ConvertPolicies()),
+ framework::dataset::make("RoundingPolicy", RoundingPolicy::TO_ZERO)),
+ framework::dataset::make("InPlace", { false })))
+{
+ // Validate output
+ validate(Accessor(_target), _reference);
+}
+
+TEST_SUITE_END() // U8U8toS16
+
TEST_SUITE(U8toU8)
TEST_SUITE(Scale255)
-PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToU8Fixture<uint8_t>, ALL, SmallShapes(), U8, U8, scale_255, TO_NEAREST_UP, InPlaceDataSet, WRAP_VALIDATE(uint8_t, 1))
+PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToU8Fixture<uint8_t>, ALL, SmallShapes(), U8, U8, U8, scale_255, TO_NEAREST_UP, InPlaceDataSet, WRAP_VALIDATE(uint8_t, 1))
TEST_SUITE_END() // Scale255
TEST_SUITE(ScaleUnity)
-PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToU8Fixture<uint8_t>, ALL, SmallShapes(), U8, U8, scale_unity, TO_ZERO, InPlaceDataSet, DEFAULT_VALIDATE)
+PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToU8Fixture<uint8_t>, ALL, SmallShapes(), U8, U8, U8, scale_unity, TO_ZERO, InPlaceDataSet, DEFAULT_VALIDATE)
TEST_SUITE_END() // ScaleUnity
TEST_SUITE(ScaleOther)
-PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToU8Fixture<uint8_t>, ALL, SmallShapes(), U8, U8, scale_other, TO_ZERO, InPlaceDataSet, DEFAULT_VALIDATE)
+PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToU8Fixture<uint8_t>, ALL, SmallShapes(), U8, U8, U8, scale_other, TO_ZERO, InPlaceDataSet, DEFAULT_VALIDATE)
TEST_SUITE_END() // ScaleOther
TEST_SUITE_END() // U8toU8
@@ -395,16 +427,18 @@
TEST_SUITE(U8toS16)
TEST_SUITE(Scale255)
-PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToS16Fixture<uint8_t>, ALL, SmallShapes(), U8, S16, scale_255, TO_NEAREST_UP, framework::dataset::make("InPlace", { false }),
+PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToS16Fixture<uint8_t>, ALL, SmallShapes(), U8, S16, S16, scale_255, TO_NEAREST_UP, framework::dataset::make("InPlace", { false }),
WRAP_VALIDATE(int16_t, 2))
TEST_SUITE_END() // Scale255
TEST_SUITE(ScaleUnity)
-PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToS16Fixture<uint8_t>, ALL, SmallShapes(), U8, S16, scale_unity, TO_ZERO, framework::dataset::make("InPlace", { false }), DEFAULT_VALIDATE)
+PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToS16Fixture<uint8_t>, ALL, SmallShapes(), U8, S16, S16, scale_unity, TO_ZERO, framework::dataset::make("InPlace", { false }),
+ DEFAULT_VALIDATE)
TEST_SUITE_END() // ScaleUnity
TEST_SUITE(ScaleOther)
-PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToS16Fixture<uint8_t>, ALL, SmallShapes(), U8, S16, scale_other, TO_ZERO, framework::dataset::make("InPlace", { false }), DEFAULT_VALIDATE)
+PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToS16Fixture<uint8_t>, ALL, SmallShapes(), U8, S16, S16, scale_other, TO_ZERO, framework::dataset::make("InPlace", { false }),
+ DEFAULT_VALIDATE)
TEST_SUITE_END() // ScaleOther
TEST_SUITE_END() // U8toS16
@@ -412,15 +446,15 @@
TEST_SUITE(S16toS16)
TEST_SUITE(Scale255)
-PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToS16Fixture<int16_t>, ALL, SmallShapes(), S16, S16, scale_255, TO_NEAREST_UP, InPlaceDataSet, WRAP_VALIDATE(int16_t, 2))
+PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToS16Fixture<int16_t>, ALL, SmallShapes(), S16, S16, S16, scale_255, TO_NEAREST_UP, InPlaceDataSet, WRAP_VALIDATE(int16_t, 2))
TEST_SUITE_END() // Scale255
TEST_SUITE(ScaleUnity)
-PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToS16Fixture<int16_t>, ALL, SmallShapes(), S16, S16, scale_unity, TO_ZERO, InPlaceDataSet, DEFAULT_VALIDATE)
+PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToS16Fixture<int16_t>, ALL, SmallShapes(), S16, S16, S16, scale_unity, TO_ZERO, InPlaceDataSet, DEFAULT_VALIDATE)
TEST_SUITE_END() // ScaleUnity
TEST_SUITE(ScaleOther)
-PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToS16Fixture<int16_t>, ALL, SmallShapes(), S16, S16, scale_other, TO_ZERO, InPlaceDataSet, DEFAULT_VALIDATE)
+PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToS16Fixture<int16_t>, ALL, SmallShapes(), S16, S16, S16, scale_other, TO_ZERO, InPlaceDataSet, DEFAULT_VALIDATE)
TEST_SUITE_END() // ScaleOther
TEST_SUITE_END() // S16toS16
@@ -429,7 +463,7 @@
TEST_SUITE(F16toF16)
TEST_SUITE(Scale255)
-PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToF16Fixture<half_float::half>, ALL, SmallShapes(), F16, F16, scale_255, TO_NEAREST_UP, InPlaceDataSet, VALIDATE(float, 1.f))
+PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToF16Fixture<half_float::half>, ALL, SmallShapes(), F16, F16, F16, scale_255, TO_NEAREST_UP, InPlaceDataSet, VALIDATE(float, 1.f))
TEST_SUITE_END() // Scale255
TEST_SUITE_END() // F16toF16
@@ -438,21 +472,21 @@
TEST_SUITE(F32toF32)
TEST_SUITE(Scale255)
-PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToF32Fixture<float>, ALL, SmallShapes(), F32, F32, scale_255, TO_NEAREST_UP, InPlaceDataSet, VALIDATE(float, 1.f))
+PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToF32Fixture<float>, ALL, SmallShapes(), F32, F32, F32, scale_255, TO_NEAREST_UP, InPlaceDataSet, VALIDATE(float, 1.f))
TEST_SUITE_END() // Scale255
TEST_SUITE(ScaleUnity)
-PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToF32Fixture<float>, ALL, SmallShapes(), F32, F32, scale_unity, TO_ZERO, InPlaceDataSet, DEFAULT_VALIDATE)
+PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToF32Fixture<float>, ALL, SmallShapes(), F32, F32, F32, scale_unity, TO_ZERO, InPlaceDataSet, DEFAULT_VALIDATE)
TEST_SUITE_END() // ScaleUnity
TEST_SUITE(ScaleOther)
-PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToF32Fixture<float>, ALL, SmallShapes(), F32, F32, scale_other, TO_ZERO, InPlaceDataSet, DEFAULT_VALIDATE)
+PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, ToF32Fixture<float>, ALL, SmallShapes(), F32, F32, F32, scale_other, TO_ZERO, InPlaceDataSet, DEFAULT_VALIDATE)
TEST_SUITE_END() // ScaleOther
TEST_SUITE_END() // F32toF32
TEST_SUITE(Broadcast)
-PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, BroadcastFixture<float>, ALL, SmallShapesBroadcast(), F32, F32, scale_255, TO_NEAREST_UP, framework::dataset::make("InPlace", { false }),
+PIXEL_WISE_MULTIPLICATION_FIXTURE_DATA_TEST_CASE(RunSmall, BroadcastFixture<float>, ALL, SmallShapesBroadcast(), F32, F32, F32, scale_255, TO_NEAREST_UP, framework::dataset::make("InPlace", { false }),
VALIDATE(float, 1.f))
TEST_SUITE_END() // Broadcast
diff --git a/tests/validation/fixtures/PixelWiseMultiplicationFixture.h b/tests/validation/fixtures/PixelWiseMultiplicationFixture.h
index 315c840..3869c35 100644
--- a/tests/validation/fixtures/PixelWiseMultiplicationFixture.h
+++ b/tests/validation/fixtures/PixelWiseMultiplicationFixture.h
@@ -139,27 +139,28 @@
bool _is_inplace{ false };
};
-template <typename TensorType, typename AccessorType, typename FunctionType, typename T1, typename T2>
-class PixelWiseMultiplicationValidationFixture : public PixelWiseMultiplicationGenericValidationFixture<TensorType, AccessorType, FunctionType, T1, T2>
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T1, typename T2, typename T3 = T2>
+class PixelWiseMultiplicationValidationFixture : public PixelWiseMultiplicationGenericValidationFixture<TensorType, AccessorType, FunctionType, T1, T2, T3>
{
public:
template <typename...>
- void setup(const TensorShape &shape, DataType dt_in1, DataType dt_in2, float scale, ConvertPolicy convert_policy, RoundingPolicy rounding_policy, bool is_inplace)
+ void setup(const TensorShape &shape, DataType dt_in1, DataType dt_in2, DataType dt_out, float scale, ConvertPolicy convert_policy, RoundingPolicy rounding_policy, bool is_inplace)
{
- PixelWiseMultiplicationGenericValidationFixture<TensorType, AccessorType, FunctionType, T1, T2>::setup(shape, shape, dt_in1, dt_in2, dt_in2, scale, convert_policy, rounding_policy,
- QuantizationInfo(), QuantizationInfo(), QuantizationInfo(), ActivationLayerInfo(), is_inplace);
+ PixelWiseMultiplicationGenericValidationFixture<TensorType, AccessorType, FunctionType, T1, T2, T3>::setup(shape, shape, dt_in1, dt_in2, dt_out, scale, convert_policy, rounding_policy,
+ QuantizationInfo(), QuantizationInfo(), QuantizationInfo(), ActivationLayerInfo(), is_inplace);
}
};
-template <typename TensorType, typename AccessorType, typename FunctionType, typename T1, typename T2>
-class PixelWiseMultiplicationBroadcastValidationFixture : public PixelWiseMultiplicationGenericValidationFixture<TensorType, AccessorType, FunctionType, T1, T2>
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T1, typename T2, typename T3 = T2>
+class PixelWiseMultiplicationBroadcastValidationFixture : public PixelWiseMultiplicationGenericValidationFixture<TensorType, AccessorType, FunctionType, T1, T2, T3>
{
public:
template <typename...>
- void setup(const TensorShape &shape0, const TensorShape &shape1, DataType dt_in1, DataType dt_in2, float scale, ConvertPolicy convert_policy, RoundingPolicy rounding_policy, bool is_inplace)
+ void setup(const TensorShape &shape0, const TensorShape &shape1, DataType dt_in1, DataType dt_in2, DataType dt_out, float scale, ConvertPolicy convert_policy, RoundingPolicy rounding_policy,
+ bool is_inplace)
{
- PixelWiseMultiplicationGenericValidationFixture<TensorType, AccessorType, FunctionType, T1, T2>::setup(shape0, shape1, dt_in1, dt_in2, dt_in2, scale, convert_policy, rounding_policy,
- QuantizationInfo(), QuantizationInfo(), QuantizationInfo(), ActivationLayerInfo(), is_inplace);
+ PixelWiseMultiplicationGenericValidationFixture<TensorType, AccessorType, FunctionType, T1, T2, T3>::setup(shape0, shape1, dt_in1, dt_in2, dt_out, scale, convert_policy, rounding_policy,
+ QuantizationInfo(), QuantizationInfo(), QuantizationInfo(), ActivationLayerInfo(), is_inplace);
}
};
diff --git a/tests/validation/reference/PixelWiseMultiplication.cpp b/tests/validation/reference/PixelWiseMultiplication.cpp
index 3e21fca..0ee8dee 100644
--- a/tests/validation/reference/PixelWiseMultiplication.cpp
+++ b/tests/validation/reference/PixelWiseMultiplication.cpp
@@ -178,6 +178,34 @@
}
template <>
+SimpleTensor<int16_t> pixel_wise_multiplication(const SimpleTensor<uint8_t> &src1, const SimpleTensor<uint8_t> &src2, float scale, ConvertPolicy convert_policy, RoundingPolicy rounding_policy,
+ DataType dt_out, const QuantizationInfo &qout)
+{
+ SimpleTensor<int16_t> dst(TensorShape::broadcast_shape(src1.shape(), src2.shape()), dt_out, 1, qout);
+
+ if(src1.data_type() == DataType::QASYMM8 && src2.data_type() == DataType::QASYMM8)
+ {
+ SimpleTensor<float> src1_tmp = convert_from_asymmetric(src1);
+ SimpleTensor<float> src2_tmp = convert_from_asymmetric(src2);
+ SimpleTensor<float> dst_tmp = pixel_wise_multiplication<float, float, float>(src1_tmp, src2_tmp, scale, convert_policy, rounding_policy, DataType::F32, qout);
+ dst = convert_to_symmetric<int16_t>(dst_tmp, qout);
+ }
+ else
+ {
+ if(scale < 0)
+ {
+ ARM_COMPUTE_ERROR("Scale of pixel-wise multiplication must be non-negative");
+ }
+
+ Coordinates id_src1{};
+ Coordinates id_src2{};
+ Coordinates id_dst{};
+ BroadcastUnroll<Coordinates::num_max_dimensions>::unroll(src1, src2, dst, scale, convert_policy, rounding_policy, id_src1, id_src2, id_dst);
+ }
+ return dst;
+}
+
+template <>
SimpleTensor<int8_t> pixel_wise_multiplication(const SimpleTensor<int8_t> &src1, const SimpleTensor<int8_t> &src2, float scale, ConvertPolicy convert_policy, RoundingPolicy rounding_policy,
DataType dt_out, const QuantizationInfo &qout)
{
@@ -234,6 +262,7 @@
}
// *INDENT-OFF*
// clang-format off
+template SimpleTensor<int16_t> pixel_wise_multiplication(const SimpleTensor<uint8_t> &src1, const SimpleTensor<uint8_t> &src2, float scale, ConvertPolicy convert_policy, RoundingPolicy rounding_policy, DataType dt_out, const QuantizationInfo &qout);
template SimpleTensor<int16_t> pixel_wise_multiplication(const SimpleTensor<uint8_t> &src1, const SimpleTensor<int16_t> &src2, float scale, ConvertPolicy convert_policy, RoundingPolicy rounding_policy, DataType dt_out, const QuantizationInfo &qout);
template SimpleTensor<int32_t> pixel_wise_multiplication(const SimpleTensor<int16_t> &src1, const SimpleTensor<int16_t> &src2, float scale, ConvertPolicy convert_policy, RoundingPolicy rounding_policy, DataType dt_out, const QuantizationInfo &qout);
template SimpleTensor<float> pixel_wise_multiplication(const SimpleTensor<float> &src1, const SimpleTensor<float> &src2, float scale, ConvertPolicy convert_policy, RoundingPolicy rounding_policy, DataType dt_out, const QuantizationInfo &qout);