COMPMID-970 : Remove QS8 / QS16 support
Removed fixed point related code.
Change-Id: I487acf138dace3b0450e0d72ca7071eaec254566
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/137678
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/core/CL/CLHelpers.cpp b/src/core/CL/CLHelpers.cpp
index df06aff..07f8bd7 100644
--- a/src/core/CL/CLHelpers.cpp
+++ b/src/core/CL/CLHelpers.cpp
@@ -38,8 +38,6 @@
{
case DataType::U8:
return "uchar";
- case DataType::QS8:
- return "qs8";
case DataType::S8:
return "char";
case DataType::QASYMM8:
@@ -48,8 +46,6 @@
return "ushort";
case DataType::S16:
return "short";
- case DataType::QS16:
- return "qs16";
case DataType::U32:
return "uint";
case DataType::S32:
@@ -75,13 +71,11 @@
switch(dt)
{
case DataType::U8:
- case DataType::QS8:
case DataType::S8:
case DataType::QASYMM8:
return "8";
case DataType::U16:
case DataType::S16:
- case DataType::QS16:
case DataType::F16:
return "16";
case DataType::U32:
@@ -101,10 +95,6 @@
{
switch(dt)
{
- case DataType::QS8:
- return "char";
- case DataType::QS16:
- return "short";
case DataType::QS32:
return "int";
default:
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index db4b344..42cf213 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -231,22 +231,16 @@
{ "gemm_interleave4x4", "gemm.cl" },
{ "gemm_ma_f16", "gemm.cl" },
{ "gemm_ma_f32", "gemm.cl" },
- { "gemm_ma_qs8", "gemm.cl" },
- { "gemm_ma_qs16", "gemm.cl" },
{ "gemm_mv", "gemv.cl" },
{ "gemm_mv_quantized", "gemv.cl" },
{ "gemm_mm_interleaved_transposed_f16", "gemm.cl" },
{ "gemm_mm_interleaved_transposed_f16_bifrost", "gemm.cl" },
{ "gemm_mm_interleaved_transposed_f32", "gemm.cl" },
{ "gemm_mm_interleaved_transposed_f32_bifrost", "gemm.cl" },
- { "gemm_mm_interleaved_transposed_qs8", "gemm.cl" },
- { "gemm_mm_interleaved_transposed_qs16", "gemm.cl" },
{ "gemm_mm_floating_point", "gemm.cl" },
{ "gemm_mm_floating_point_f16_bifrost", "gemm.cl" },
{ "gemm_mm_floating_point_f32_bifrost", "gemm.cl" },
{ "gemm_mm_floating_point_f32_bifrost_1000", "gemm.cl" },
- { "gemm_mm_qs8", "gemm.cl" },
- { "gemm_mm_qs16", "gemm.cl" },
{ "gemm_lc_vm_f32", "gemm.cl" },
{ "gemm_transpose1xW", "gemm.cl" },
{ "gemmlowp_matrix_a_reduction", "gemmlowp.cl" },
@@ -557,10 +551,6 @@
#include "./cl_kernels/fill_border.clembed"
},
{
- "fixed_point.h",
-#include "./cl_kernels/fixed_point.hembed"
- },
- {
"floor.cl",
#include "./cl_kernels/floor.clembed"
},
diff --git a/src/core/CL/cl_kernels/activation_layer.cl b/src/core/CL/cl_kernels/activation_layer.cl
index a8ea738..373406a 100644
--- a/src/core/CL/cl_kernels/activation_layer.cl
+++ b/src/core/CL/cl_kernels/activation_layer.cl
@@ -25,23 +25,6 @@
#define TYPE VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
-#if defined(FIXED_POINT_POSITION)
-#include "fixed_point.h"
-
-#define CONST_ONE (1 << FIXED_POINT_POSITION)
-#define ABS_OP(a) ABS_SAT_OP_EXPAND((a), DATA_TYPE, VEC_SIZE)
-#define ADD_OP(a, b) ADD_SAT_OP_EXPAND((a), (b), DATA_TYPE, VEC_SIZE)
-#define SUB_OP(a, b) SUB_SAT_OP_EXPAND((a), (b), DATA_TYPE, VEC_SIZE)
-#define MUL_OP(a, b) MUL_SAT_OP_EXPAND((a), (b), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
-#define MLA_OP(a, b, c) MLA_SAT_OP_EXPAND((a), (b), (c), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
-#define DIV_OP(a, b) DIV_SAT_OP_VEC_EXPAND((a), (b), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
-#define EXP_OP(a) EXP_OP_EXPAND((a), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
-#define LOG_OP(a) LOG_OP_EXPAND((a), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
-#define SQRT_OP(a) DIV_OP(CONST_ONE, INVSQRT_OP_EXPAND((a), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION))
-#define TANH_OP(a) TANH_OP_EXPAND((a), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
-
-#else /* FIXED_POINT_POSITION */
-
#define CONST_ONE 1.f
#define ABS_OP(a) fabs((a))
#define ADD_OP(a, b) ((a) + (b))
@@ -54,8 +37,6 @@
#define SQRT_OP(a) sqrt((a))
#define TANH_OP(a) tanh((a))
-#endif /* FIXED_POINT_POSITION */
-
// Logistic Activation
inline TYPE logistic_op(TYPE x)
{
@@ -125,9 +106,8 @@
* @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16
* @note Activation function should be given as a preprocessor argument using -DACT=name. e.g. -DACT=TANH
* @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively.
- * @note In case of fixed point calculations the fixed point position is passed using -DFIXED_POINT_POSITION=position. e.g. -DFIXED_POINT_POSITION=3.
*
- * @param[in] input_ptr Pointer to the source image. Supported data types: QS8/QS16/F16/F32
+ * @param[in] input_ptr Pointer to the source image. Supported data types: F16/F32
* @param[in] input_stride_x Stride of the source image in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the source image in Y dimension (in bytes)
diff --git a/src/core/CL/cl_kernels/arithmetic_op.cl b/src/core/CL/cl_kernels/arithmetic_op.cl
index 8bd2823..9efb71b 100644
--- a/src/core/CL/cl_kernels/arithmetic_op.cl
+++ b/src/core/CL/cl_kernels/arithmetic_op.cl
@@ -23,10 +23,6 @@
*/
#include "helpers.h"
-#if defined(FIXED_POINT_POSITION)
-#include "fixed_point.h"
-#endif /* FIXED_POINT_POSITION */
-
#ifdef SATURATE
#define ADD(x, y) add_sat((x), (y))
#define SUB(x, y) sub_sat((x), (y))
@@ -43,7 +39,7 @@
* e.g. -DDATA_TYPE_IN1=uchar -DDATA_TYPE_IN2=uchar -DDATA_TYPE_OUT=short
* @attention To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used.
*
- * @param[in] in1_ptr Pointer to the source tensor. Supported data types: U8/QS8/QS16/S16/F16/F32
+ * @param[in] in1_ptr Pointer to the source tensor. Supported data types: U8/S16/F16/F32
* @param[in] in1_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] in1_step_x in1_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] in1_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -51,7 +47,7 @@
* @param[in] in1_stride_z Stride of the source tensor in Z dimension (in bytes)
* @param[in] in1_step_z in1_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] in1_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[in] in2_ptr Pointer to the source tensor. Supported data types: U8/QS8 (only if @p in1_ptr is QS8), QS16 (only if @p in1_ptr is QS16), S16/F16/F32
+ * @param[in] in2_ptr Pointer to the source tensor. Supported data types: U8/S16/F16/F32
* @param[in] in2_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] in2_step_x in2_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] in2_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -59,7 +55,7 @@
* @param[in] in2_stride_z Stride of the source tensor in Z dimension (in bytes)
* @param[in] in2_step_z in2_stride_z * number of elements along Z processed per workitem(in bytes)
* @param[in] in2_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] out_ptr Pointer to the destination tensor. Supported data types: U8 (only if both inputs are U8), QS8 (only if both inputs are QS8), QS16 (only if both inputs are QS16), S16/F16/F32
+ * @param[out] out_ptr Pointer to the destination tensor. Supported data types: U8 (only if both inputs are U8), S16/F16/F32
* @param[in] out_stride_x Stride of the destination tensor in X dimension (in bytes)
* @param[in] out_step_x out_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] out_stride_y Stride of the destination tensor in Y dimension (in bytes)
diff --git a/src/core/CL/cl_kernels/batchnormalization_layer.cl b/src/core/CL/cl_kernels/batchnormalization_layer.cl
index 9c980da..5352af3 100644
--- a/src/core/CL/cl_kernels/batchnormalization_layer.cl
+++ b/src/core/CL/cl_kernels/batchnormalization_layer.cl
@@ -25,25 +25,12 @@
#if defined(VEC_SIZE) && defined(DATA_TYPE)
-#if defined(FIXED_POINT_POSITION)
-#include "fixed_point.h"
-
-#define ADD_OP(a, b) ADD_SAT_OP_EXPAND((a), (b), DATA_TYPE, VEC_SIZE)
-#define SUB_OP(a, b) SUB_SAT_OP_EXPAND((a), (b), DATA_TYPE, VEC_SIZE)
-#define MUL_OP(a, b) MUL_SAT_OP_EXPAND((a), (b), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
-#define INVSQRT_OP(a) INVSQRT_OP_EXPAND((a), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
-#define SQCVT_SAT(a) SQCVT_SAT_OP_EXPAND((a), DATA_TYPE, FIXED_POINT_POSITION)
-
-#else /* FIXED_POINT_POSITION */
-
#define ADD_OP(a, b) ((a) + (b))
#define SUB_OP(a, b) ((a) - (b))
#define MUL_OP(a, b) ((a) * (b))
#define INVSQRT_OP(a) rsqrt((a))
#define SQCVT_SAT(a) (a)
-#endif /* FIXED_POINT_POSITION */
-
#if defined(FUSED_ACTIVATION)
#include "activation_layer.cl"
#define ACTIVATION_FUNC(x) ACTIVATION_OP(FUSED_ACTIVATION, x)
@@ -53,7 +40,7 @@
/** Apply batch normalization.
*
- * @param[in] input_ptr Pointer to the first source tensor. Supported data types: QS8/QS16/F16/F32
+ * @param[in] input_ptr Pointer to the first source tensor. Supported data types: F16/F32
* @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the first source tensor in Y dimension (in bytes)
@@ -163,7 +150,7 @@
/** Apply batch normalization on tensors with NHWC format.
*
- * @param[in] input_ptr Pointer to the first source tensor. Supported data types: QS8/QS16/F16/F32
+ * @param[in] input_ptr Pointer to the first source tensor. Supported data types: F16/F32
* @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the first source tensor in Y dimension (in bytes)
diff --git a/src/core/CL/cl_kernels/channel_shuffle.cl b/src/core/CL/cl_kernels/channel_shuffle.cl
index 26cee9c..23962e1 100644
--- a/src/core/CL/cl_kernels/channel_shuffle.cl
+++ b/src/core/CL/cl_kernels/channel_shuffle.cl
@@ -38,7 +38,7 @@
* @note The number of channels in each group should be given as a preprocessor argument using -DK=num. e.g. -DK=1
* K is equal to num_channels / num_groups.
*
- * @param[in] src_ptr Pointer to the source matrix. Supported data types: U8/S8/QS8/QASYMM8/U16/S16/QS16/F16/U32/S32/F32
+ * @param[in] src_ptr Pointer to the source matrix. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32
* @param[in] src_stride_x Stride of the first source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the first source tensor in Y dimension (in bytes)
diff --git a/src/core/CL/cl_kernels/col2im.cl b/src/core/CL/cl_kernels/col2im.cl
index 6e491f3..98bf8d1 100644
--- a/src/core/CL/cl_kernels/col2im.cl
+++ b/src/core/CL/cl_kernels/col2im.cl
@@ -23,12 +23,7 @@
*/
#include "helpers.h"
-#if defined(FIXED_POINT_POSITION)
-#include "fixed_point.h"
-#endif // FIXED_POINT_POSITION
-
#if defined(DATA_TYPE) && defined(WIDTH_OUTPUT) && defined(ELEMENT_SIZE) && defined(WIDTH_INPUT)
-#if !defined(FIXED_POINT_POSITION)
#if ELEMENT_SIZE == 1
#define COND_DATA_TYPE char
@@ -100,41 +95,4 @@
*((__global DATA_TYPE *)(output_ptr + idx + x_clamped.s6 * dst_stride_z)) = data.s6;
*((__global DATA_TYPE *)(output_ptr + idx + x_clamped.s7 * dst_stride_z)) = data.s7;
}
-#else // !defined(FIXED_POINT_POSITION)
-/** This kernel performs a reshaping of the output of the convolution layer.
- *
- * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=qs8
- * @note The width of the output tensor must be passed at compile time using -DWIDTH_OUTPUT: e.g. -DWIDTH_OUTPUT=320
- *
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16
- * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
- * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
- * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr
- * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)
- * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
- * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
- * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
- * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes)
- */
-__kernel void col2im(
- IMAGE_DECLARATION(src),
- TENSOR3D_DECLARATION(dst),
- uint dst_stride_w)
-{
- Image src = CONVERT_TO_IMAGE_STRUCT(src);
- Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(dst);
-
- // Compute output offset
- int idx = get_global_id(0) * dst.stride_z + (get_global_id(1) / WIDTH_OUTPUT) * dst_stride_y + (get_global_id(1) % WIDTH_OUTPUT) * dst_stride_x + get_global_id(2) * dst_stride_w;
-
- // Store value
- *((__global DATA_TYPE *)(dst.ptr + idx)) = *((__global DATA_TYPE *)(src.ptr));
-}
-#endif // !defined(FIXED_POINT_POSITION)
#endif // defined(DATA_TYPE) && defined(WIDTH_OUTPUT) && defined(ELEMENT_SIZE) && defined(WIDTH_INPUT)
\ No newline at end of file
diff --git a/src/core/CL/cl_kernels/concatenate.cl b/src/core/CL/cl_kernels/concatenate.cl
index f97ae13..6ec8383 100644
--- a/src/core/CL/cl_kernels/concatenate.cl
+++ b/src/core/CL/cl_kernels/concatenate.cl
@@ -25,7 +25,7 @@
/** This kernel concatenates the input tensor into the output tensor along the first dimension
*
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8, QASYMM8, QS16, F16, F32
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8, F16, F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -60,7 +60,7 @@
/** This kernel concatenates the input tensor into the output tensor along the third dimension
*
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8, QS16, F16, F32
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16, F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
diff --git a/src/core/CL/cl_kernels/convert_fc_weights.cl b/src/core/CL/cl_kernels/convert_fc_weights.cl
index 3c3e8b0..5aadfb3 100644
--- a/src/core/CL/cl_kernels/convert_fc_weights.cl
+++ b/src/core/CL/cl_kernels/convert_fc_weights.cl
@@ -32,7 +32,7 @@
* @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float
* @attention Original input tensor width*height and depth should be given as a preprocessor argument using -DFACTOR_1=size and -DFACTOR_2=size for NCHW and vice versa for NHWC. e.g. -DFACTOR_1=256 and -DFACTOR_2=128
*
- * @param[in] src_ptr Pointer to the source image. Supported data types: U8, S8, QS8, QASYMM8, U16, S16, QS16, U32, S32, QS32, F16, F32
+ * @param[in] src_ptr Pointer to the source image. Supported data types: U8, S8, QASYMM8, U16, S16, U32, S32, QS32, F16, F32
* @param[in] src_stride_x Stride of the source image in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source image in Y dimension (in bytes)
diff --git a/src/core/CL/cl_kernels/convolution_layer.cl b/src/core/CL/cl_kernels/convolution_layer.cl
index 6a70b00..2b83e5a 100644
--- a/src/core/CL/cl_kernels/convolution_layer.cl
+++ b/src/core/CL/cl_kernels/convolution_layer.cl
@@ -23,10 +23,6 @@
*/
#include "helpers.h"
-#if defined(FIXED_POINT_POSITION)
-#include "fixed_point.h"
-#endif // FIXED_POINT_POSITION
-
#if defined(DATA_TYPE)
/** This kernel reshapes the tensor's low three dimensions to single column
*
diff --git a/src/core/CL/cl_kernels/depth_convert.cl b/src/core/CL/cl_kernels/depth_convert.cl
index a9b7284..01491ec 100644
--- a/src/core/CL/cl_kernels/depth_convert.cl
+++ b/src/core/CL/cl_kernels/depth_convert.cl
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -23,23 +23,6 @@
*/
#include "helpers.h"
-#if defined(FIXED_POINT_POSITION)
-
-#include "fixed_point.h"
-
-#ifdef SATURATE
-#define CONVERT_DOWN(x, in_type, out_type, fixed_point_position) CONVERT_DOWN1_SAT(x, in_type, out_type, fixed_point_position)
-#define CONVERT_DOWN1_SAT(x, in_type, out_type, fixed_point_position) convert_##out_type##_##in_type##_sat(x, fixed_point_position)
-#else /* SATURATE */
-#define CONVERT_DOWN(x, in_type, out_type, fixed_point_position) CONVERT_DOWN1(x, in_type, out_type, fixed_point_position)
-#define CONVERT_DOWN1(x, in_type, out_type, fixed_point_position) convert_##out_type##_##in_type(x, fixed_point_position)
-#endif /* SATURATE */
-
-#define CONVERT_UP(x, in_type, out_type, fixed_point_position) CONVERT_UP1(x, in_type, out_type, fixed_point_position)
-#define CONVERT_UP1(x, in_type, out_type, fixed_point_position) convert_##out_type##_##in_type(x, fixed_point_position)
-
-#else /* FIXED_POINT_POSITION */
-
#ifdef SATURATE
#define CONVERT_DOWN(x, type) CONVERT_SAT(x, type)
#else /* SATURATE */
@@ -48,22 +31,18 @@
#define CONVERT_UP(x, type) CONVERT(x, type)
-#endif /* FIXED_POINT_POSITION */
-
/** This function performs a down-scaling depth conversion.
*
* @attention The input and output data_types need to be passed at compile time using -DDATA_TYPE_IN and -DDATA_TYPE_OUT:
* e.g. -DDATA_TYPE_IN=uchar -DDATA_TYPE_OUT=short
*
- * @note In case of fixed-point operation -DFIXED_POINT_POSITION=fixed_point_position must be provided: e.g. -DFIXED_POINT_POSITION=3
- *
* @param[in] in_ptr Pointer to the source image. Supported data types: U8, U16, S16, U32, S32, F16, F32
* @param[in] in_stride_x Stride of the source image in X dimension (in bytes)
* @param[in] in_step_x in_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] in_stride_y Stride of the source image in Y dimension (in bytes)
* @param[in] in_step_y in_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] in_offset_first_element_in_bytes The offset of the first element in the source image
- * @param[out] out_ptr Pointer to the destination image. Supported data types: QS8, U8, QS16, U16, S16, U32, S32
+ * @param[out] out_ptr Pointer to the destination image. Supported data types: U8, U16, S16, U32, S32
* @param[in] out_stride_x Stride of the destination image in X dimension (in bytes)
* @param[in] out_step_x out_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] out_stride_y Stride of the destination image in Y dimension (in bytes)
@@ -84,11 +63,7 @@
VEC_DATA_TYPE(DATA_TYPE_IN, 16)
in_data = vload16(0, (__global DATA_TYPE_IN *)in.ptr);
-#if defined(FIXED_POINT_POSITION)
- vstore16(CONVERT_DOWN(in_data, VEC_DATA_TYPE(DATA_TYPE_IN, 16), VEC_DATA_TYPE(DATA_TYPE_OUT, 16), FIXED_POINT_POSITION), 0, (__global DATA_TYPE_OUT *)out.ptr);
-#else /* FIXED_POINT_POSITION */
vstore16(CONVERT_DOWN(in_data >> shift, VEC_DATA_TYPE(DATA_TYPE_OUT, 16)), 0, (__global DATA_TYPE_OUT *)out.ptr);
-#endif /* FIXED_POINT_POSITION */
}
/** This function performs a up-scaling depth conversion.
@@ -96,9 +71,7 @@
* @attention The input and output data_types need to be passed at compile time using -DDATA_TYPE_IN and -DDATA_TYPE_OUT:
* e.g. -DDATA_TYPE_IN=uchar -DDATA_TYPE_OUT=short
*
- * @note In case of fixed-point operation -DFIXED_POINT_POSITION=fixed_point_position must be provided: e.g. -DFIXED_POINT_POSITION=3
- *
- * @param[in] in_ptr Pointer to the source image. Supported data types: U8, QS8, U16, S16, QS16, U32 or S32
+ * @param[in] in_ptr Pointer to the source image. Supported data types: U8, U16, S16, U32 or S32
* @param[in] in_stride_x Stride of the source image in X dimension (in bytes)
* @param[in] in_step_x in_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] in_stride_y Stride of the source image in Y dimension (in bytes)
@@ -125,9 +98,5 @@
VEC_DATA_TYPE(DATA_TYPE_IN, 16)
in_data = vload16(0, (__global DATA_TYPE_IN *)in.ptr);
-#if defined(FIXED_POINT_POSITION)
- vstore16(CONVERT_UP(in_data, VEC_DATA_TYPE(DATA_TYPE_IN, 16), VEC_DATA_TYPE(DATA_TYPE_OUT, 16), FIXED_POINT_POSITION), 0, (__global DATA_TYPE_OUT *)out.ptr);
-#else /* FIXED_POINT_POSITION */
vstore16(CONVERT_UP(in_data, VEC_DATA_TYPE(DATA_TYPE_OUT, 16)) << shift, 0, (__global DATA_TYPE_OUT *)out.ptr);
-#endif /* FIXED_POINT_POSITION */
}
diff --git a/src/core/CL/cl_kernels/depthwise_convolution.cl b/src/core/CL/cl_kernels/depthwise_convolution.cl
index f3aa0d6..9a8b57e 100644
--- a/src/core/CL/cl_kernels/depthwise_convolution.cl
+++ b/src/core/CL/cl_kernels/depthwise_convolution.cl
@@ -527,7 +527,7 @@
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The convolution information must be passed at compile time using -DSTRIDE_X, -DSTRIDE_Y, -DPAD_LEFT, -DPAD_TOP, -DPAD_RIGHT, -DPAD_BOTTOM, -DKERNEL_WIDHT, -DKERNEL_HEIGHT, -DSRC_WIDTH, -DSRC_HEIGHT, -DDEPTH_MULTIPLIER
*
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -587,7 +587,7 @@
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The convolution information must be passed at compile time using -DCONV_WIDTH, -DCONV_HEIGHT, e.g -DCONV_WIDTH=32, -DCONV_HEIGHT=42
*
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
diff --git a/src/core/CL/cl_kernels/dequantization_layer.cl b/src/core/CL/cl_kernels/dequantization_layer.cl
index 21e9c87..4908bb0 100644
--- a/src/core/CL/cl_kernels/dequantization_layer.cl
+++ b/src/core/CL/cl_kernels/dequantization_layer.cl
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -25,7 +25,7 @@
/** This performs the dequantization of 8-bit unsigned integers to floating point.
*
- * @param[in] input_ptr Pointer to the source image. Supported data types: QS8/QS16/F16/F32
+ * @param[in] input_ptr Pointer to the source image. Supported data types: F16/F32
* @param[in] input_stride_x Stride of the source image in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the source image in Y dimension (in bytes)
diff --git a/src/core/CL/cl_kernels/direct_convolution1x1.cl b/src/core/CL/cl_kernels/direct_convolution1x1.cl
index 817c261..7a308c9 100644
--- a/src/core/CL/cl_kernels/direct_convolution1x1.cl
+++ b/src/core/CL/cl_kernels/direct_convolution1x1.cl
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -23,24 +23,12 @@
*/
#include "helpers.h"
-#if defined(FIXED_POINT_POSITION)
-#include "fixed_point.h"
-
-#define ADD_OP(a, b) ADD_SAT_OP_EXPAND((a), (b), DATA_TYPE_PROMOTED, 8)
-#define MUL_OP(a, b) MUL_SAT_OP_EXPAND(CONVERT((a), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)), CONVERT((b), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)), DATA_TYPE_PROMOTED, 8, FIXED_POINT_POSITION)
-
-// There is no need to have a larger intermediate type for qs32 because all the arguments are already promoted
-MULQ_SAT_IMPL(qs32x8, qs32x8)
-
-#else /* FIXED_POINT_POSITION */
#undef CONVERT_SAT
#define ADD_OP(a, b) ((a) + (b))
#define MUL_OP(a, b) ((a) * (b))
#define CONVERT_SAT(a, b) ((a))
-#endif /* FIXED_POINT_POSITION */
-
#if defined(DATA_TYPE) && defined(DATA_SIZE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
#if STRIDE_X == 3
diff --git a/src/core/CL/cl_kernels/direct_convolution3x3.cl b/src/core/CL/cl_kernels/direct_convolution3x3.cl
index a7abc9f..824306f 100644
--- a/src/core/CL/cl_kernels/direct_convolution3x3.cl
+++ b/src/core/CL/cl_kernels/direct_convolution3x3.cl
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -23,25 +23,12 @@
*/
#include "helpers.h"
-#if defined(FIXED_POINT_POSITION)
-#include "fixed_point.h"
-
-#define ADD_OP(a, b) ADD_SAT_OP_EXPAND((a), (b), DATA_TYPE_PROMOTED, 8)
-#define MUL_OP(a, b) MUL_SAT_OP_EXPAND(CONVERT((a), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)), CONVERT((b), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 8)), DATA_TYPE_PROMOTED, 8, FIXED_POINT_POSITION)
-
-// There is no need to have a larger intermediate type for qs32 because all the arguments are already promoted
-MULQ_SAT_IMPL(qs32x8, qs32x8)
-
-#else /* FIXED_POINT_POSITION */
-
#undef CONVERT_SAT
#define ADD_OP(a, b) ((a) + (b))
#define MUL_OP(a, b) ((a) * (b))
#define CONVERT_SAT(a, b) ((a))
-#endif /* FIXED_POINT_POSITION */
-
#if defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
#if STRIDE_X == 1
@@ -86,7 +73,7 @@
* @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
* @note If biases are used then -DHAS_BIAS has to be passed at compile time
*
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
diff --git a/src/core/CL/cl_kernels/fill_border.cl b/src/core/CL/cl_kernels/fill_border.cl
index 33a9495..9d6a2b8 100644
--- a/src/core/CL/cl_kernels/fill_border.cl
+++ b/src/core/CL/cl_kernels/fill_border.cl
@@ -23,10 +23,6 @@
*/
#include "helpers.h"
-#if defined(FIXED_POINT_POSITION)
-#include "fixed_point.h"
-#endif /* FIXED_POINT_POSITION */
-
/** Fill N pixel of the padding edge of a single channel image by replicating the closest valid pixel.
*
* @attention The DATA_TYPE needs to be passed at the compile time.
diff --git a/src/core/CL/cl_kernels/fixed_point.h b/src/core/CL/cl_kernels/fixed_point.h
deleted file mode 100644
index 46fa645..0000000
--- a/src/core/CL/cl_kernels/fixed_point.h
+++ /dev/null
@@ -1,518 +0,0 @@
-/*
- * Copyright (c) 2017-2018 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_FIXED_POINT_H
-#define ARM_COMPUTE_FIXED_POINT_H
-
-#define TYPE_ALIAS(type, alias) \
- typedef type alias; \
- typedef type alias##x##1; \
- typedef type##2 alias##x##2; \
- typedef type##3 alias##x##3; \
- typedef type##4 alias##x##4; \
- typedef type##8 alias##x##8; \
- typedef type##16 alias##x##16;
-
-TYPE_ALIAS(char, qs8)
-TYPE_ALIAS(short, qs16)
-TYPE_ALIAS(int, qs32)
-
-#define qs8_MIN ((char)CHAR_MIN)
-#define qs8_MAX ((char)CHAR_MAX)
-#define qs16_MIN ((short)SHRT_MIN)
-#define qs16_MAX ((short)SHRT_MAX)
-#define qs32_MIN ((int)INT_MIN)
-#define qs32_MAX ((int)INT_MAX)
-
-#define qu8_MIN ((uchar)0)
-#define qu8_MAX ((uchar)UCHAR_MAX)
-#define qu16_MIN ((ushort)0)
-#define qu16_MAX ((ushort)USHRT_MAX)
-#define qu32_MIN ((uint)0)
-#define qu32_MAX ((uint)UINT_MAX)
-
-#define qs8_TYPE char
-#define qs8x1_TYPE char
-#define qs8x2_TYPE char2
-#define qs8x3_TYPE char3
-#define qs8x4_TYPE char4
-#define qs8x8_TYPE char8
-#define qs8x16_TYPE char16
-
-#define qs16_TYPE short
-#define qs16x1_TYPE short
-#define qs16x2_TYPE short2
-#define qs16x3_TYPE short3
-#define qs16x4_TYPE short4
-#define qs16x8_TYPE short8
-#define qs16x16_TYPE short16
-
-#define qs32_TYPE int
-#define qs32x1_TYPE int
-#define qs32x2_TYPE int2
-#define qs32x3_TYPE int3
-#define qs32x4_TYPE int4
-#define qs32x8_TYPE int8
-#define qs32x16_TYPE int16
-
-/* All internal constants are represented in the maximum supported fixed point format (QS16),
- * thus we define an additional shift parameter required to convert the constant
- * from the maximum supported format to the require one.
- */
-#define qs8_SHIFT 8
-#define qs16_SHIFT 0
-
-#undef VEC_DATA_TYPE_STR
-#undef VEC_DATA_TYPE
-#undef CONVERT_STR
-#undef CONVERT
-#undef CONVERT_SAT_STR
-#undef CONVERT_SAT
-
-#define VEC_DATA_TYPE_STR(type, size) type##x##size
-#define VEC_DATA_TYPE(type, size) VEC_DATA_TYPE_STR(type, size)
-
-#define CONVERT_STR3(x, type, rtype) (convert_##rtype((x)))
-#define CONVERT_STR2(x, type, rtype) CONVERT_STR3(x, type, rtype)
-#define CONVERT_STR(x, type) CONVERT_STR2(x, type, type##_TYPE)
-#define CONVERT(x, type) CONVERT_STR(x, type)
-
-#define CONVERT_SAT_STR3(x, type, rtype) (convert_##rtype##_sat((x)))
-#define CONVERT_SAT_STR2(x, type, rtype) CONVERT_SAT_STR3(x, type, rtype)
-#define CONVERT_SAT_STR(x, type) CONVERT_SAT_STR2(x, type, type##_TYPE)
-#define CONVERT_SAT(x, type) CONVERT_SAT_STR(x, type)
-
-/** Computes saturating absolute value of fixed point vector.
- *
- * @param[in] type the actual data type.
- *
- * @return The result of the fixed point absolute value.
- */
-#define ABSQ_SAT_IMPL(type) \
- inline type abs_##type##_sat(type VopA) \
- { \
- return CONVERT_SAT(abs(VopA), type); \
- }
-
-ABSQ_SAT_IMPL(qs8x16)
-ABSQ_SAT_IMPL(qs16x8)
-
-#define ABS_SAT_OP_EXPAND_STR(a, type, size) abs_##type##x##size##_sat((a))
-#define ABS_SAT_OP_EXPAND(a, type, size) ABS_SAT_OP_EXPAND_STR(a, type, size)
-
-/** Computes max of fixed point types.
- *
- * @param[in] type the actual data type.
- *
- * @return The result of the fixed point maximum.
- */
-#define MAXQ_IMPL(type) \
- inline type max_##type(type VopA, type VopB) \
- { \
- return max(VopA, VopB); \
- }
-
-MAXQ_IMPL(qs8x1)
-MAXQ_IMPL(qs8x2)
-MAXQ_IMPL(qs8x4)
-MAXQ_IMPL(qs8x8)
-MAXQ_IMPL(qs8x16)
-MAXQ_IMPL(qs16x1)
-MAXQ_IMPL(qs16x2)
-MAXQ_IMPL(qs16x4)
-MAXQ_IMPL(qs16x8)
-MAXQ_IMPL(qs16x16)
-
-#define MAX_OP_EXPAND_STR(a, b, type, size) max_##type##x##size((a), (b))
-#define MAX_OP_EXPAND(a, b, type, size) MAX_OP_EXPAND_STR(a, b, type, size)
-
-/** Computes saturated addition of fixed point types.
- *
- * @param[in] type the actual data type.
- *
- * @return The result of the fixed point addition. The result is saturated in case of overflow
- */
-#define ADDQ_SAT_IMPL(type) \
- inline type add_sat_##type(type VopA, type VopB) \
- { \
- return add_sat(VopA, VopB); \
- }
-
-ADDQ_SAT_IMPL(qs8x1)
-ADDQ_SAT_IMPL(qs8x2)
-ADDQ_SAT_IMPL(qs8x4)
-ADDQ_SAT_IMPL(qs8x8)
-ADDQ_SAT_IMPL(qs8x16)
-ADDQ_SAT_IMPL(qs16x1)
-ADDQ_SAT_IMPL(qs16x2)
-ADDQ_SAT_IMPL(qs16x4)
-ADDQ_SAT_IMPL(qs16x8)
-ADDQ_SAT_IMPL(qs16x16)
-ADDQ_SAT_IMPL(qs32x1)
-ADDQ_SAT_IMPL(qs32x2)
-ADDQ_SAT_IMPL(qs32x4)
-ADDQ_SAT_IMPL(qs32x8)
-ADDQ_SAT_IMPL(qs32x16)
-
-#define ADD_SAT_OP_EXPAND_STR(a, b, type, size) add_sat_##type##x##size((a), (b))
-#define ADD_SAT_OP_EXPAND(a, b, type, size) ADD_SAT_OP_EXPAND_STR(a, b, type, size)
-
-/** Computes saturated subtraction of fixed point types.
- *
- * @param[in] type the actual data type.
- *
- * @return The result of the fixed point subtraction. The result is saturated in case of overflow
- */
-#define SUBQ_SAT_IMPL(type) \
- inline type sub_sat_##type(type VopA, type VopB) \
- { \
- return sub_sat(VopA, VopB); \
- }
-
-SUBQ_SAT_IMPL(qs8x1)
-SUBQ_SAT_IMPL(qs8x2)
-SUBQ_SAT_IMPL(qs8x4)
-SUBQ_SAT_IMPL(qs8x8)
-SUBQ_SAT_IMPL(qs8x16)
-SUBQ_SAT_IMPL(qs16x1)
-SUBQ_SAT_IMPL(qs16x2)
-SUBQ_SAT_IMPL(qs16x4)
-SUBQ_SAT_IMPL(qs16x8)
-SUBQ_SAT_IMPL(qs16x16)
-
-#define SUB_SAT_OP_EXPAND_STR(a, b, type, size) sub_sat_##type##x##size((a), (b))
-#define SUB_SAT_OP_EXPAND(a, b, type, size) SUB_SAT_OP_EXPAND_STR(a, b, type, size)
-
-/* Multiply of two fixed point numbers
- *
- * @param[in] type the actual data type.
- * @param[in] itype the intermediate data type.
- *
- * @return The result of the fixed point multiplication.
- */
-#define MULQ_IMPL(type, itype) \
- inline type mul_##type(type VopA, type VopB, int fixed_point_position) \
- { \
- itype round_val = (itype)(1 << (fixed_point_position - 1)); \
- itype res = CONVERT((VopA), itype) * CONVERT((VopB), itype) + round_val; \
- return CONVERT((res >> (itype)fixed_point_position), type); \
- }
-
-MULQ_IMPL(qs8x8, qs16x8)
-MULQ_IMPL(qs16x8, qs32x8)
-MULQ_IMPL(qs8x16, qs16x16)
-MULQ_IMPL(qs16x16, qs32x16)
-
-#define MUL_OP_EXPAND_STR(a, b, type, size, position) mul_##type##x##size((a), (b), (position))
-#define MUL_OP_EXPAND(a, b, type, size, position) MUL_OP_EXPAND_STR(a, b, type, size, position)
-
-/* Saturate multiply of two fixed point numbers
- *
- * @param[in] type the actual data type.
- * @param[in] itype the intermediate data type.
- *
- * @return The result of the fixed point multiplication. The result is saturated in case of overflow
- */
-#define MULQ_SAT_IMPL(type, itype) \
- inline type mul_sat_##type(type VopA, type VopB, int fixed_point_position) \
- { \
- itype round_val = (itype)(1 << (fixed_point_position - 1)); \
- itype res = mad_sat(CONVERT((VopA), itype), CONVERT((VopB), itype), round_val); \
- return CONVERT_SAT((res >> (itype)fixed_point_position), type); \
- }
-
-MULQ_SAT_IMPL(qs8x1, qs16x1)
-MULQ_SAT_IMPL(qs8x2, qs16x2)
-MULQ_SAT_IMPL(qs8x3, qs16x3)
-MULQ_SAT_IMPL(qs8x4, qs16x4)
-MULQ_SAT_IMPL(qs8x8, qs16x8)
-MULQ_SAT_IMPL(qs8x16, qs16x16)
-MULQ_SAT_IMPL(qs16x1, qs32x1)
-MULQ_SAT_IMPL(qs16x2, qs32x2)
-MULQ_SAT_IMPL(qs16x3, qs32x3)
-MULQ_SAT_IMPL(qs16x4, qs32x4)
-MULQ_SAT_IMPL(qs16x8, qs32x8)
-MULQ_SAT_IMPL(qs16x16, qs32x16)
-
-#define MUL_SAT_OP_EXPAND_STR(a, b, type, size, position) mul_sat_##type##x##size((a), (b), (position))
-#define MUL_SAT_OP_EXPAND(a, b, type, size, position) MUL_SAT_OP_EXPAND_STR(a, b, type, size, position)
-
-/** Saturate multiply-accumulate
- *
- * @param[in] type the actual data type.
- * @param[in] itype the intermediate data type.
- *
- * @return The result of the fixed point multiply-accumulate. The result is saturated in case of overflow
- */
-#define MLAQ_SAT_IMPL(type, itype) \
- type mla_sat_##type(type VopA, type VopB, type VopC, int fixed_point_position) \
- { \
- itype res = mad_sat(CONVERT(VopB, itype), CONVERT(VopC, itype), (itype)(1 << (fixed_point_position - 1))); \
- return add_sat(VopA, CONVERT_SAT(res >> (itype)fixed_point_position, type)); \
- }
-
-MLAQ_SAT_IMPL(qs8x8, qs16x8)
-MLAQ_SAT_IMPL(qs8x16, qs16x16)
-MLAQ_SAT_IMPL(qs16x8, qs32x8)
-
-#define MLA_SAT_OP_EXPAND_STR(a, b, c, type, size, position) mla_sat_##type##x##size((a), (b), (c), (position))
-#define MLA_SAT_OP_EXPAND(a, b, c, type, size, position) MLA_SAT_OP_EXPAND_STR(a, b, c, type, size, position)
-
-/** Saturate multiply-accumulate long
- *
- * @param[in] type the actual data type.
- * @param[in] itype the intermediate data type.
- *
- * @return The result of the fixed point multiply-accumulate long. The result is saturated in case of overflow
- */
-#define MLALQ_SAT_IMPL(type, itype) \
- itype mlal_sat_##type(itype VopA, type VopB, type VopC, int fixed_point_position) \
- { \
- itype res = mad_sat(CONVERT(VopB, itype), CONVERT(VopC, itype), (itype)(1 << (fixed_point_position - 1))); \
- return add_sat(VopA, res >> (itype)fixed_point_position); \
- }
-
-MLALQ_SAT_IMPL(qs8x8, qs16x8)
-MLALQ_SAT_IMPL(qs16x8, qs32x8)
-
-#define MLAL_SAT_OP_EXPAND_STR(a, b, c, type, size, position) mlal_sat_##type##x##size((a), (b), (c), (position))
-#define MLAL_SAT_OP_EXPAND(a, b, c, type, size, position) MLAL_SAT_OP_EXPAND_STR(a, b, c, type, size, position)
-
-/** Saturate division of two fixed point vectors
- *
- * @param[in] stype the actual scalar data type.
- * @param[in] type the actual data type.
- * @param[in] itype the intermediate data type.
- *
- * @return The result of the fixed point division. The result is saturated in case of overflow
- */
-#define DIVQ_SAT_IMPL(stype, type, itype) \
- inline type div_sat_##type(type VopA, type VopB, int fixed_point_position) \
- { \
- itype conv_a = CONVERT((VopA), itype); \
- itype denominator = CONVERT((VopB), itype); \
- itype numerator = conv_a << (itype)(fixed_point_position); \
- itype res = select((itype)(numerator / denominator), select((itype)stype##_MAX, (itype)stype##_MIN, (itype)(conv_a < (itype)0)), (itype)(denominator == (itype)0)); \
- return CONVERT_SAT((res), type); \
- }
-
-DIVQ_SAT_IMPL(qs8, qs8x16, qs16x16)
-DIVQ_SAT_IMPL(qs16, qs16x8, qs32x8)
-DIVQ_SAT_IMPL(qs16, qs16x16, qs32x16)
-DIVQ_SAT_IMPL(qs8, qs8, qs16)
-DIVQ_SAT_IMPL(qs16, qs16, qs32)
-
-#define DIV_SAT_OP_EXPAND_STR(a, b, type, position) div_sat_##type((a), (b), (position))
-#define DIV_SAT_OP_EXPAND(a, b, type, position) DIV_SAT_OP_EXPAND_STR(a, b, type, position)
-
-#define DIV_SAT_OP_VEC_EXPAND_STR(a, b, type, size, position) div_sat_##type##x##size((a), (b), (position))
-#define DIV_SAT_OP_VEC_EXPAND(a, b, type, size, position) DIV_SAT_OP_VEC_EXPAND_STR(a, b, type, size, position)
-
-/** Saturate exponential of a fixed point vector
- *
- * @note Implemented approach uses taylor polynomial to approximate the exponential function.
- *
- * @param[in] stype the actual scalar data type.
- * @param[in] type the actual data type.
- * @param[in] size the number of the calculated elements.
- *
- * @return The result of the fixed point exponential. The result is saturated in case of overflow
- */
-#define EXPQ_IMPL(stype, type, size) \
- inline type exp_sat_##type(type VopA, int fixed_point_position) \
- { \
- type const_one = (type)(1 << (fixed_point_position)); \
- type ln2 = (type)((((0x58B9 >> (14 - fixed_point_position))) + 1) >> 1); \
- type inv_ln2 = (type)((((0x38AA >> (14 - fixed_point_position)) + 1) >> 1)) | const_one; \
- type A = (type)(((0x7FBA >> (14 - fixed_point_position)) + 1) >> 1); \
- type B = (type)(((0x3FE9 >> (14 - fixed_point_position)) + 1) >> 1); \
- type C = (type)(((0x1693 >> (14 - fixed_point_position)) + 1) >> 1); \
- type D = (type)(((0x0592 >> (14 - fixed_point_position)) + 1) >> 1); \
- type m = MUL_SAT_OP_EXPAND(VopA, inv_ln2, stype, size, fixed_point_position); \
- type dec_m = m >> (type)fixed_point_position; \
- type alpha = MUL_SAT_OP_EXPAND(dec_m << (type)fixed_point_position, ln2, stype, size, fixed_point_position); \
- alpha = CONVERT(abs_diff(VopA, alpha), type); \
- type sum = add_sat(MUL_SAT_OP_EXPAND(alpha, D, stype, size, fixed_point_position), C); \
- sum = add_sat(MUL_SAT_OP_EXPAND(alpha, sum, stype, size, fixed_point_position), B); \
- sum = add_sat(MUL_SAT_OP_EXPAND(alpha, sum, stype, size, fixed_point_position), A); \
- sum = add_sat(MUL_SAT_OP_EXPAND(alpha, sum, stype, size, fixed_point_position), const_one); \
- return select((type)stype##_MAX, select(sum << dec_m, sum >> -dec_m, dec_m < (type)0), clz(sum) > dec_m); /* Saturate result if needed */ \
- }
-
-EXPQ_IMPL(qs8, qs8x2, 2)
-EXPQ_IMPL(qs8, qs8x4, 4)
-EXPQ_IMPL(qs8, qs8x8, 8)
-EXPQ_IMPL(qs8, qs8x16, 16)
-EXPQ_IMPL(qs16, qs16x2, 2)
-EXPQ_IMPL(qs16, qs16x4, 4)
-EXPQ_IMPL(qs16, qs16x8, 8)
-EXPQ_IMPL(qs16, qs16x16, 16)
-
-#define EXP_OP_EXPAND_STR(a, type, size, position) exp_sat_##type##x##size((a), (position))
-#define EXP_OP_EXPAND(a, type, size, position) EXP_OP_EXPAND_STR(a, type, size, position)
-
-/** Saturate logarithm of a fixed point vector
- *
- * @note Implemented approach uses taylor polynomial to approximate the logarithm function.
- *
- * @param[in] stype the actual scalar data type.
- * @param[in] type the actual data type.
- * @param[in] size the number of the calculated elements.
- *
- * @return The result of the fixed point logarithm. The result is saturated in case of overflow
- */
-#define LOGQ_IMPL(stype, type, size) \
- inline type log_sat_##type(type VopA, int fixed_point_position) \
- { \
- type const_one = (type)(1 << (fixed_point_position)); \
- type ln2 = (type)(0x58B9 >> (15 - fixed_point_position)); /* 1.4384189 */ \
- type A = (type)(0x5C0F >> (14 - fixed_point_position)); /* 1.4384189 */ \
- type B = -(type)(0x56AE >> (15 - fixed_point_position)); /* -0.6771900 */ \
- type C = (type)(0x2933 >> (15 - fixed_point_position)); /* 0.3218538 */ \
- type D = -(type)(0x0AA7 >> (15 - fixed_point_position)); /* -0.0832229 */ \
- type inter_a = select(VopA, DIV_SAT_OP_VEC_EXPAND(const_one, VopA, stype, size, fixed_point_position), VopA < const_one); \
- type shift_val = (type)(15 - stype##_SHIFT) - clz(inter_a >> (type)fixed_point_position); \
- inter_a = inter_a >> shift_val; \
- inter_a = sub_sat(inter_a, const_one); \
- type sum = add_sat(MUL_SAT_OP_EXPAND(inter_a, D, stype, size, fixed_point_position), C); \
- sum = add_sat(MUL_SAT_OP_EXPAND(inter_a, sum, stype, size, fixed_point_position), B); \
- sum = add_sat(MUL_SAT_OP_EXPAND(inter_a, sum, stype, size, fixed_point_position), A); \
- sum = MUL_SAT_OP_EXPAND(inter_a, sum, stype, size, fixed_point_position); \
- sum = MUL_SAT_OP_EXPAND(add_sat(sum, shift_val << (type)fixed_point_position), ln2, stype, size, fixed_point_position); \
- return select(select(sum, -sum, VopA < const_one), (type)0, VopA < (type)0); /* Saturate result if needed */ \
- }
-
-LOGQ_IMPL(qs8, qs8x16, 16)
-LOGQ_IMPL(qs16, qs16x8, 8)
-LOGQ_IMPL(qs16, qs16x16, 16)
-
-#define LOG_OP_EXPAND_STR(a, type, size, position) log_sat_##type##x##size((a), (position))
-#define LOG_OP_EXPAND(a, type, size, position) LOG_OP_EXPAND_STR(a, type, size, position)
-
-/** Saturate inverse square root of a fixed point vector
- *
- * @note Implemented approach uses Newton's method to approximate the inverse square root function.
- *
- * @param[in] stype the actual scalar data type.
- * @param[in] type the actual data type.
- * @param[in] size the number of the calculated elements.
- *
- * @return The result of the fixed point inverse square root. The result is saturated in case of overflow
- */
-#define INVSQRTQ_IMPL(stype, type, size) \
- inline type invsqrt_sat_##type(type VopA, int fixed_point_position) \
- { \
- type const_three = (type)(3 << (fixed_point_position)); \
- type shift_value = (type)(16 - stype##_SHIFT) - (clz(VopA) + (type)fixed_point_position); \
- type temp = select((type)(VopA >> shift_value), select((type)stype##_MAX, (type)(VopA << (-shift_value)), (type)(clz(VopA) > (-shift_value))), (type)(shift_value < (type)0)); \
- type x = temp; \
- x = MUL_SAT_OP_EXPAND(x, sub_sat(const_three, MUL_SAT_OP_EXPAND(MUL_SAT_OP_EXPAND(x, x, stype, size, fixed_point_position), temp, stype, size, fixed_point_position)), stype, size, fixed_point_position) >> 1; \
- x = MUL_SAT_OP_EXPAND(x, sub_sat(const_three, MUL_SAT_OP_EXPAND(MUL_SAT_OP_EXPAND(x, x, stype, size, fixed_point_position), temp, stype, size, fixed_point_position)), stype, size, fixed_point_position) >> 1; \
- x = MUL_SAT_OP_EXPAND(x, sub_sat(const_three, MUL_SAT_OP_EXPAND(MUL_SAT_OP_EXPAND(x, x, stype, size, fixed_point_position), temp, stype, size, fixed_point_position)), stype, size, fixed_point_position) >> 1; \
- if(sizeof((stype)(1)) > 1) /* Perform more iterations if datatype is QS16 */ \
- { \
- x = MUL_SAT_OP_EXPAND(x, sub_sat(const_three, MUL_SAT_OP_EXPAND(MUL_SAT_OP_EXPAND(x, x, stype, size, fixed_point_position), temp, stype, size, fixed_point_position)), stype, size, fixed_point_position) >> 1; \
- x = MUL_SAT_OP_EXPAND(x, sub_sat(const_three, MUL_SAT_OP_EXPAND(MUL_SAT_OP_EXPAND(x, x, stype, size, fixed_point_position), temp, stype, size, fixed_point_position)), stype, size, fixed_point_position) >> 1; \
- } \
- type shift_value2 = select(shift_value >> 1, (-shift_value) >> 1, shift_value < (type)0); \
- return select((type)(x >> shift_value2), select((type)stype##_MAX, (type)(x << shift_value2), (type)(clz(x) > shift_value2)), (type)(shift_value < (type)0)); /* Saturate result if needed */ \
- }
-
-INVSQRTQ_IMPL(qs8, qs8x1, 1)
-INVSQRTQ_IMPL(qs16, qs16x1, 1)
-INVSQRTQ_IMPL(qs8, qs8x16, 16)
-INVSQRTQ_IMPL(qs16, qs16x8, 8)
-
-#define INVSQRT_OP_EXPAND_STR(a, type, size, position) invsqrt_sat_##type##x##size((a), (position))
-#define INVSQRT_OP_EXPAND(a, type, size, position) INVSQRT_OP_EXPAND_STR(a, type, size, position)
-
-/** Saturate hyperbolic tangent of a fixed point vector
- *
- * tanh(x) = (e^2x - 1)/(e^2x + 1)
- *
- * @param[in] stype the actual scalar data type.
- * @param[in] type the actual data type.
- * @param[in] size the number of the calculated elements.
- *
- * @return The result of the fixed point hyperbolic tangent. The result is saturated in case of overflow
- */
-#define TANHQ_IMPL(stype, type, size) \
- inline type tanh_sat_##type(type VopA, int fixed_point_position) \
- { \
- type const_one = (type)(1 << (fixed_point_position)); \
- type const_two = (type)(2 << (fixed_point_position)); \
- type exp2x = EXP_OP_EXPAND(MUL_SAT_OP_EXPAND(const_two, VopA, stype, size, fixed_point_position), stype, size, fixed_point_position); \
- type num = SUB_SAT_OP_EXPAND(exp2x, const_one, stype, size); \
- type den = ADD_SAT_OP_EXPAND(exp2x, const_one, stype, size); \
- return DIV_SAT_OP_VEC_EXPAND(num, den, stype, size, fixed_point_position); \
- }
-
-TANHQ_IMPL(qs8, qs8x16, 16)
-TANHQ_IMPL(qs16, qs16x8, 8)
-
-#define TANH_OP_EXPAND_STR(a, type, size, position) tanh_sat_##type##x##size((a), (position))
-#define TANH_OP_EXPAND(a, type, size, position) TANH_OP_EXPAND_STR(a, type, size, position)
-
-#define floatx16 float16
-#define float16_TYPE float16
-
-#define CONVERTQ_DOWN_IMPL(in_type, out_type) \
- inline out_type convert_##out_type##_##in_type(in_type a, int fixed_point_position) \
- { \
- return CONVERT(a * (1 << fixed_point_position) + select((in_type)-0.5f, (in_type)0.5f, isgreater(a, (in_type)0)), out_type); \
- }
-
-CONVERTQ_DOWN_IMPL(float16, qs8x16)
-CONVERTQ_DOWN_IMPL(float16, qs16x16)
-
-#define CONVERTQ_DOWN_SAT_IMPL(in_type, out_type) \
- inline out_type convert_##out_type##_##in_type##_sat(in_type a, int fixed_point_position) \
- { \
- return CONVERT_SAT(a * (1 << fixed_point_position) + select((in_type)-0.5f, (in_type)0.5f, isgreater(a, (in_type)0)), out_type); \
- }
-
-CONVERTQ_DOWN_SAT_IMPL(float16, qs8x16)
-CONVERTQ_DOWN_SAT_IMPL(float16, qs16x16)
-
-#define CONVERTQ_UP_IMPL(in_type, out_type) \
- inline out_type convert_##out_type##_##in_type(in_type a, int fixed_point_position) \
- { \
- return CONVERT(a, out_type) / (1 << fixed_point_position); \
- }
-
-CONVERTQ_UP_IMPL(qs8x16, float16)
-CONVERTQ_UP_IMPL(qs16x16, float16)
-
-#define SQCVT_SAT_IMPL(type) \
- inline type sqcvt_##type##_sat(float a, int fixed_point_position) \
- { \
- return CONVERT_SAT((a * (1 << fixed_point_position) + ((a < 0) ? -0.5f : 0.5f)), type); \
- }
-
-SQCVT_SAT_IMPL(qs8)
-SQCVT_SAT_IMPL(qs16)
-
-#define SQCVT_SAT_OP_EXPAND_STR(a, type, position) sqcvt_##type##_sat((a), (position))
-#define SQCVT_SAT_OP_EXPAND(a, type, position) SQCVT_SAT_OP_EXPAND_STR((a), type, position)
-
-#endif // ARM_COMPUTE_FIXED_POINT_H
diff --git a/src/core/CL/cl_kernels/gemm.cl b/src/core/CL/cl_kernels/gemm.cl
index e969e84..f75161c 100644
--- a/src/core/CL/cl_kernels/gemm.cl
+++ b/src/core/CL/cl_kernels/gemm.cl
@@ -23,10 +23,6 @@
*/
#include "helpers.h"
-#ifdef FIXED_POINT_POSITION
-#include "fixed_point.h"
-#endif // FIXED_POINT_POSITION
-
#if defined(TRANSPOSE_W) && defined(MULT_TRANSPOSE1XW_WIDTH)
#if ELEMENT_SIZE == 1
@@ -44,7 +40,7 @@
* @note The transposition width must be passed at compile time using -DTRANSPOSE_W (i.e. -DTRANSPOSE_W)
* @note The multiplication factor for the transposition width (mult_transpose1xW_width) must be passed at compile time using -DMULT_TRANSPOSE1XW_WIDTH (i.e. -DMULT_TRANSPOSE1XW_WIDTH=2)
*
- * @param[in] src_ptr Pointer to the source matrix. Supported data types: U8/S8/QS8/QASYMM8/U16/S16/QS16/F16/U32/S32/F32
+ * @param[in] src_ptr Pointer to the source matrix. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32
* @param[in] src_stride_x Stride of the source matrix in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source matrix in Y dimension (in bytes)
@@ -93,7 +89,7 @@
* @note The data type must be passed at compile time using -DDATA_TYPE (i.e. -DDATA_TYPE=float)
* @note The multiplication factor for the height of the 4x4 interleaved block must be passed at compile time using -DMULT_INTERLEAVE4X4_HEIGHT (i.e. -DMULT_INTERLEAVE4X4_HEIGHT=2)
*
- * @param[in] src_ptr Pointer to the source matrix. Supported data types: U8/S8/QS8/QASYMM8/U16/S16/QS16/F16/U32/S32/F32
+ * @param[in] src_ptr Pointer to the source matrix. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32
* @param[in] src_stride_x Stride of the source matrix in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source matrix in Y dimension (in bytes)
@@ -1085,248 +1081,6 @@
#endif // defined(ARM_COMPUTE_OPENCL_FP16_ENABLED)
-#if defined(FIXED_POINT_POSITION)
-/** This OpenCL kernel computes the matrix multiplication between matrix A (src0) and matrix B (src1) in 8 bit fixed point precision
- * Matrix A and matrix B must be reshaped respectively with @ref gemm_interleave4x4_8bit and @ref gemm_transpose1x16 before running the matrix multiplication
- *
- * @note The number of columns of matrix B and the optional alpha's value need to be passed at compile time using -DCOLS_B and -DALPHA
- * @note The multiplication factor for the transposition width (mult_transpose1xW_width) must be passed at compile time using -DMULT_TRANSPOSE1XW_WIDTH (i.e. -DMULT_TRANSPOSE1XW_WIDTH=2)
- * @note The multiplication factor for the height of the 4x4 interleaved block must be passed at compile time using -DMULT_INTERLEAVE4X4_HEIGHT (i.e. -DMULT_INTERLEAVE4X4_HEIGHT=2)
- * @note In case the matrix B has 3 dimensions and the matrix A more than 3, in order to avoid out-of-bounds reads, the number of channels of matrix B must be passed at compile time using MATRIX_B_DEPTH (i.e. -DMATRIX_B_DEPTH=16)
- * This case can happen when GEMM is used to perform the element-wise multiplication through a batched matrix multiplication (2D Winograd) and we have multiple inputs (i.e. a = [K, M, 16, Batches], b = [N, K, 16])
- * @note:ALPHA must be passed in 8 bit fixed point format
- *
- * @param[in] src0_ptr Pointer to the source matrix. Supported data types: QS8
- * @param[in] src0_stride_x Stride of the source matrix in X dimension (in bytes)
- * @param[in] src0_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src0_stride_y Stride of the source matrix in Y dimension (in bytes)
- * @param[in] src0_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src0_offset_first_element_in_bytes The offset of the first element in the source matrix
- * @param[in] src1_ptr Pointer to the source matrix. Supported data types: same as @p src0_ptr
- * @param[in] src1_stride_x Stride of the source matrix in X dimension (in bytes)
- * @param[in] src1_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src1_stride_y Stride of the source matrix in Y dimension (in bytes)
- * @param[in] src1_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src1_offset_first_element_in_bytes The offset of the first element in the source matrix
- * @param[out] dst_ptr Pointer to the destination matrix Supported data types: same as @p src0_ptr
- * @param[in] dst_stride_x Stride of the destination matrix in X dimension (in bytes)
- * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination matrix in Y dimension (in bytes)
- * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination matrix
- * @param[in] src0_stride_z Stride of the source matrix in Z dimension (in bytes)
- * @param[in] src1_stride_z Stride of the source matrix in Z dimension (in bytes)
- * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
- */
-__kernel void gemm_mm_interleaved_transposed_qs8(IMAGE_DECLARATION(src0),
- IMAGE_DECLARATION(src1),
- IMAGE_DECLARATION(dst),
- uint src0_stride_z,
- uint src1_stride_z,
- uint dst_stride_z)
-{
- int x = get_global_id(0) / MULT_TRANSPOSE1XW_WIDTH;
- int y = get_global_id(1) / MULT_INTERLEAVE4X4_HEIGHT;
- int z = get_global_id(2);
-
- // Offset
- const int offset_row_a = (get_global_id(1) % MULT_INTERLEAVE4X4_HEIGHT) * 4;
- const int offset_row_b = (get_global_id(0) % MULT_TRANSPOSE1XW_WIDTH) * 16;
-
- // src_addr_a = address of matrix A
- // src_addr_b = address of matrix B
- int src0_addr_in_bytes = z * src0_stride_z + y * src0_stride_y + src0_offset_first_element_in_bytes;
- int src1_addr_in_bytes = x * src1_stride_y + src1_offset_first_element_in_bytes;
-
-#if defined(MATRIX_B_DEPTH)
- // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
- src1_addr_in_bytes += (z % MATRIX_B_DEPTH) * src1_stride_z;
-#else // defined(MATRIX_B_DEPTH)
- src1_addr_in_bytes += z * src1_stride_z;
-#endif // defined(MATRIX_B_DEPTH)
-
- __global char *src_addr_a = (__global char *)(src0_ptr + src0_addr_in_bytes);
- __global char *src_addr_b = (__global char *)(src1_ptr + src1_addr_in_bytes);
-
- // Compute end row address for matrix B
- __global char *src_end_addr_b = src_addr_b + COLS_B;
-
- src_addr_a += offset_row_a;
- src_addr_b += offset_row_b;
-
- // Reset accumulators
- short8 c00 = 0.0f;
- short8 c10 = 0.0f;
- short8 c20 = 0.0f;
- short8 c30 = 0.0f;
- short8 c01 = 0.0f;
- short8 c11 = 0.0f;
- short8 c21 = 0.0f;
- short8 c31 = 0.0f;
-
- // This for loop performs 1 accumulation for each iteration
- for(; src_addr_b < src_end_addr_b; src_addr_a += 4 * MULT_INTERLEAVE4X4_HEIGHT, src_addr_b += 16 * MULT_TRANSPOSE1XW_WIDTH)
- {
- // Load values from matrix A (interleaved) and matrix B (transposed)
- char4 a0 = vload4(0, src_addr_a);
- char16 b0 = vload16(0, src_addr_b);
-
- c00 = mlal_sat_qs8x8(c00, (char8)a0.s0, b0.s01234567, FIXED_POINT_POSITION);
- c10 = mlal_sat_qs8x8(c10, (char8)a0.s1, b0.s01234567, FIXED_POINT_POSITION);
- c20 = mlal_sat_qs8x8(c20, (char8)a0.s2, b0.s01234567, FIXED_POINT_POSITION);
- c30 = mlal_sat_qs8x8(c30, (char8)a0.s3, b0.s01234567, FIXED_POINT_POSITION);
-
- c01 = mlal_sat_qs8x8(c01, (char8)a0.s0, b0.s89ABCDEF, FIXED_POINT_POSITION);
- c11 = mlal_sat_qs8x8(c11, (char8)a0.s1, b0.s89ABCDEF, FIXED_POINT_POSITION);
- c21 = mlal_sat_qs8x8(c21, (char8)a0.s2, b0.s89ABCDEF, FIXED_POINT_POSITION);
- c31 = mlal_sat_qs8x8(c31, (char8)a0.s3, b0.s89ABCDEF, FIXED_POINT_POSITION);
- }
-
- // Compute destination address
- Image dst = CONVERT_TO_IMAGE_STRUCT(dst);
-
- // Multiply by the weight of matrix product
- char16 c00_qs8 = convert_char16_sat((short16)(c00, c01));
- char16 c10_qs8 = convert_char16_sat((short16)(c10, c11));
- char16 c20_qs8 = convert_char16_sat((short16)(c20, c21));
- char16 c30_qs8 = convert_char16_sat((short16)(c30, c31));
-
-#if defined(ALPHA)
- c00_qs8 = mul_sat_qs8x16(c00_qs8, (char16)ALPHA, FIXED_POINT_POSITION);
- c10_qs8 = mul_sat_qs8x16(c10_qs8, (char16)ALPHA, FIXED_POINT_POSITION);
- c20_qs8 = mul_sat_qs8x16(c20_qs8, (char16)ALPHA, FIXED_POINT_POSITION);
- c30_qs8 = mul_sat_qs8x16(c30_qs8, (char16)ALPHA, FIXED_POINT_POSITION);
-#endif // defined(ALPHA)
-
- // Compute dst address
- __global uchar *dst_addr = offset(&dst, 0, 0);
-
- // Add offset for batched GEMM
- dst_addr += z * dst_stride_z;
-
- // Store 16x4 block
- vstore16(c00_qs8, 0, (__global char *)(dst_addr + 0 * dst_stride_y));
- vstore16(c10_qs8, 0, (__global char *)(dst_addr + 1 * dst_stride_y));
- vstore16(c20_qs8, 0, (__global char *)(dst_addr + 2 * dst_stride_y));
- vstore16(c30_qs8, 0, (__global char *)(dst_addr + 3 * dst_stride_y));
-}
-
-/** This OpenCL kernel computes the matrix multiplication between matrix A (src0) and matrix B (src1) in 16 bit fixed point precision
- * Matrix A and matrix B must be reshaped respectively with @ref gemm_interleave4x4_16bit and @ref gemm_transpose1x8 before running the matrix multiplication
- *
- * @note The number of columns of matrix B and the optional alpha's value need to be passed at compile time using -DCOLS_B and -DALPHA
- * @note The multiplication factor for the transposition width (mult_transpose1xW_width) must be passed at compile time using -DMULT_TRANSPOSE1XW_WIDTH (i.e. -DMULT_TRANSPOSE1XW_WIDTH=2)
- * @note The multiplication factor for the height of the 4x4 interleaved block must be passed at compile time using -DMULT_INTERLEAVE4X4_HEIGHT (i.e. -DMULT_INTERLEAVE4X4_HEIGHT=2)
- * @note In case the matrix B has 3 dimensions and the matrix A more than 3, in order to avoid out-of-bounds reads, the number of channels of matrix B must be passed at compile time using MATRIX_B_DEPTH (i.e. -DMATRIX_B_DEPTH=16)
- * This case can happen when GEMM is used to perform the element-wise multiplication through a batched matrix multiplication (2D Winograd) and we have multiple inputs (i.e. a = [K, M, 16, Batches], b = [N, K, 16])
- * @note:ALPHA must be passed in 16 bit fixed point format
- *
- * @param[in] src0_ptr Pointer to the source matrix. Supported data types: QS16
- * @param[in] src0_stride_x Stride of the source matrix in X dimension (in bytes)
- * @param[in] src0_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src0_stride_y Stride of the source matrix in Y dimension (in bytes)
- * @param[in] src0_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src0_offset_first_element_in_bytes The offset of the first element in the source matrix
- * @param[in] src1_ptr Pointer to the source matrix. Supported data types: same as @p src0_ptr
- * @param[in] src1_stride_x Stride of the source matrix in X dimension (in bytes)
- * @param[in] src1_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src1_stride_y Stride of the source matrix in Y dimension (in bytes)
- * @param[in] src1_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src1_offset_first_element_in_bytes The offset of the first element in the source matrix
- * @param[out] dst_ptr Pointer to the destination matrix Supported data types: same as @p src0_ptr
- * @param[in] dst_stride_x Stride of the destination matrix in X dimension (in bytes)
- * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination matrix in Y dimension (in bytes)
- * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination matrix
- * @param[in] src0_stride_z Stride of the source matrix in Z dimension (in bytes)
- * @param[in] src1_stride_z Stride of the source matrix in Z dimension (in bytes)
- * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
- */
-__kernel void gemm_mm_interleaved_transposed_qs16(IMAGE_DECLARATION(src0),
- IMAGE_DECLARATION(src1),
- IMAGE_DECLARATION(dst),
- uint src0_stride_z,
- uint src1_stride_z,
- uint dst_stride_z)
-{
- int x = get_global_id(0) / MULT_TRANSPOSE1XW_WIDTH;
- int y = get_global_id(1) / MULT_INTERLEAVE4X4_HEIGHT;
- int z = get_global_id(2);
-
- // Offset
- const int offset_row_a = (get_global_id(1) % MULT_INTERLEAVE4X4_HEIGHT) * 4;
- const int offset_row_b = (get_global_id(0) % MULT_TRANSPOSE1XW_WIDTH) * 8;
-
- // src_addr_a = address of matrix A
- // src_addr_b = address of matrix B
- int src0_addr_in_bytes = z * src0_stride_z + y * src0_stride_y + src0_offset_first_element_in_bytes;
- int src1_addr_in_bytes = x * src1_stride_y + src1_offset_first_element_in_bytes;
-
-#if defined(MATRIX_B_DEPTH)
- // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
- src1_addr_in_bytes += (z % MATRIX_B_DEPTH) * src1_stride_z;
-#else // defined(MATRIX_B_DEPTH)
- src1_addr_in_bytes += z * src1_stride_z;
-#endif // defined(MATRIX_B_DEPTH)
-
- __global short *src_addr_a = (__global short *)(src0_ptr + src0_addr_in_bytes);
- __global short *src_addr_b = (__global short *)(src1_ptr + src1_addr_in_bytes);
-
- // Compute end row address for matrix B
- __global short *src_end_addr_b = src_addr_b + COLS_B;
-
- src_addr_a += offset_row_a;
- src_addr_b += offset_row_b;
-
- // Reset accumulators
- int8 c00 = 0.0f;
- int8 c10 = 0.0f;
- int8 c20 = 0.0f;
- int8 c30 = 0.0f;
-
- // This for loop performs 1 accumulation for each iteration
- for(; src_addr_b < src_end_addr_b; src_addr_a += 4 * MULT_INTERLEAVE4X4_HEIGHT, src_addr_b += 8 * MULT_TRANSPOSE1XW_WIDTH)
- {
- /* Load values from matrix A (interleaved) and matrix B (transposed) */
- short4 a0 = vload4(0, src_addr_a);
- short8 b0 = vload8(0, src_addr_b);
-
- c00 = mlal_sat_qs16x8(c00, (short8)a0.s0, b0, FIXED_POINT_POSITION);
- c10 = mlal_sat_qs16x8(c10, (short8)a0.s1, b0, FIXED_POINT_POSITION);
- c20 = mlal_sat_qs16x8(c20, (short8)a0.s2, b0, FIXED_POINT_POSITION);
- c30 = mlal_sat_qs16x8(c30, (short8)a0.s3, b0, FIXED_POINT_POSITION);
- }
-
- // Compute destination address
- Image dst = CONVERT_TO_IMAGE_STRUCT(dst);
-
- // Multiply by the weight of matrix product
- short8 c00_qs16 = convert_short8_sat(c00);
- short8 c10_qs16 = convert_short8_sat(c10);
- short8 c20_qs16 = convert_short8_sat(c20);
- short8 c30_qs16 = convert_short8_sat(c30);
-
-#if defined(ALPHA)
- c00_qs16 = mul_sat_qs16x8(c00_qs16, (short8)ALPHA, FIXED_POINT_POSITION);
- c10_qs16 = mul_sat_qs16x8(c10_qs16, (short8)ALPHA, FIXED_POINT_POSITION);
- c20_qs16 = mul_sat_qs16x8(c20_qs16, (short8)ALPHA, FIXED_POINT_POSITION);
- c30_qs16 = mul_sat_qs16x8(c30_qs16, (short8)ALPHA, FIXED_POINT_POSITION);
-#endif // defined(ALPHA)
-
- // Compute dst address
- __global uchar *dst_addr = offset(&dst, 0, 0);
-
- // Add offset for batched GEMM
- dst_addr += z * dst_stride_z;
-
- // Store 8x4 block
- vstore8(c00_qs16, 0, (__global short *)(dst_addr + 0 * dst_stride_y));
- vstore8(c10_qs16, 0, (__global short *)(dst_addr + 1 * dst_stride_y));
- vstore8(c20_qs16, 0, (__global short *)(dst_addr + 2 * dst_stride_y));
- vstore8(c30_qs16, 0, (__global short *)(dst_addr + 3 * dst_stride_y));
-}
-#endif // defined(FIXED_POINT_POSITION)
#endif // defined(COLS_B) && defined(MULT_TRANSPOSE1XW_WIDTH) && defined(MULT_INTERLEAVE4X4_HEIGHT)
#if defined(COLS_A) && defined(NUM_ELEMS_PROCESSED_PER_THREAD_X) && (NUM_ELEMS_PROCESSED_PER_THREAD_Y)
@@ -2543,365 +2297,6 @@
}
#endif // defined(ARM_COMPUTE_OPENCL_FP16_ENABLED)
-#if defined(FIXED_POINT_POSITION)
-/** This OpenCL kernel computes the matrix by matrix multiplication between the matrix A (src0) and matrix B (src1) in case both matrices have not beed reshaped
- *
- * @note This OpenCL kernel works with fixed point data types QS8
- * @note The number of elements processed along the x and y directions must be passed at compile time using -DNUM_ELEMS_PROCESSED_PER_THREAD_X and -DNUM_ELEMS_PROCESSED_PER_THREAD_Y
- * @note The number matrix A columns, the number of elements processed per thread along the Y direction and the alpha's value need to be passed at compile time using -DCOLS_A, -DNUM_ELEMS_PROCESSED_PER_THREAD_Y and -DALPHA
- * @note The fixed point position need to be passed at compile time using -DFIXED_POINT_POSITION
- * @note The optional alpha value must be passed in 8 bit fixed point format using -DALPHA
- * @note In case the matrix B has 3 dimensions and the matrix A more than 3, in order to avoid out-of-bounds reads, the number of channels of matrix B must be passed at compile time using MATRIX_B_DEPTH (i.e. -DMATRIX_B_DEPTH=16)
- * This case can happen when GEMM is used to perform the element-wise multiplication through a batched matrix multiplication (2D Winograd) and we have multiple inputs (i.e. a = [K, M, 16, Batches], b = [N, K, 16])
- *
- * @param[in] src0_ptr Pointer to the source matrix. Supported data types: QS8/QS16
- * @param[in] src0_stride_x Stride of the source matrix in X dimension (in bytes)
- * @param[in] src0_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src0_stride_y Stride of the source matrix in Y dimension (in bytes)
- * @param[in] src0_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src0_offset_first_element_in_bytes The offset of the first element in the source matrix
- * @param[in] src1_ptr Pointer to the source matrix. Supported data types: same as @p src0_ptr
- * @param[in] src1_stride_x Stride of the source matrix in X dimension (in bytes)
- * @param[in] src1_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src1_stride_y Stride of the source matrix in Y dimension (in bytes)
- * @param[in] src1_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src1_offset_first_element_in_bytes The offset of the first element in the source matrix
- * @param[out] dst_ptr Pointer to the destination matrix Supported data types: same as @p src0_ptr
- * @param[in] dst_stride_x Stride of the destination matrix in X dimension (in bytes)
- * @param[in] dst_step_x dst_gx_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination matrix in Y dimension (in bytes)
- * @param[in] dst_step_y dst_gx_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination matrix
- * @param[in] src0_stride_z Stride of the source matrix in Z dimension (in bytes)
- * @param[in] src1_stride_z Stride of the source matrix in Z dimension (in bytes)
- * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
- */
-__kernel void gemm_mm_qs8(IMAGE_DECLARATION(src0),
- IMAGE_DECLARATION(src1),
- IMAGE_DECLARATION(dst),
- uint src0_stride_z,
- uint src1_stride_z,
- uint dst_stride_z)
-{
- int idx = get_global_id(0) * NUM_ELEMS_PROCESSED_PER_THREAD_X;
-
- // Compute starting address for matrix A and Matrix B
- int2 src_addr = ((int2)(src0_offset_first_element_in_bytes, src1_offset_first_element_in_bytes));
-
- // Update address for the matrix A
- src_addr.s0 += get_global_id(1) * src0_stride_y * NUM_ELEMS_PROCESSED_PER_THREAD_Y;
-
- // Update address for the matrix B
- src_addr.s1 += idx * sizeof(char);
-
- // Add offset for batched GEMM
- src_addr.s0 += get_global_id(2) * src0_stride_z;
-
-#if defined(MATRIX_B_DEPTH)
- // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
- src_addr.s1 += (get_global_id(2) % MATRIX_B_DEPTH) * src1_stride_z;
-#else // defined(MATRIX_B_DEPTH)
- src_addr.s1 += get_global_id(2) * src1_stride_z;
-#endif // defined(MATRIX_B_DEPTH)
-
- int end_row_vec_a = src_addr.s0 + (COLS_A * sizeof(char));
-
- short8 acc00 = 0;
- short8 acc01 = 0;
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
- short8 acc10 = 0;
- short8 acc11 = 0;
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
- short8 acc20 = 0;
- short8 acc21 = 0;
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- short8 acc30 = 0;
- short8 acc31 = 0;
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
-
- // This for loop performs 4 accumulations per iteration
- for(; src_addr.s0 <= (end_row_vec_a - 2); src_addr += (int2)(2, 2 * src1_stride_y))
- {
- char2 a0 = vload2(0, (__global char *)(src0_ptr + src_addr.s0 + 0 * src0_stride_y));
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
- char2 a1 = vload2(0, (__global char *)(src0_ptr + src_addr.s0 + 1 * src0_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
- char2 a2 = vload2(0, (__global char *)(src0_ptr + src_addr.s0 + 2 * src0_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- char2 a3 = vload2(0, (__global char *)(src0_ptr + src_addr.s0 + 3 * src0_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- char16 b0 = vload16(0, (__global char *)(src1_ptr + src_addr.s1 + 0 * src1_stride_y));
- char16 b1 = vload16(0, (__global char *)(src1_ptr + src_addr.s1 + 1 * src1_stride_y));
-
- acc00 = mlal_sat_qs8x8(acc00, (char8)a0.s0, b0.s01234567, FIXED_POINT_POSITION);
- acc00 = mlal_sat_qs8x8(acc00, (char8)a0.s1, b1.s01234567, FIXED_POINT_POSITION);
- acc01 = mlal_sat_qs8x8(acc01, (char8)a0.s0, b0.s89ABCDEF, FIXED_POINT_POSITION);
- acc01 = mlal_sat_qs8x8(acc01, (char8)a0.s1, b1.s89ABCDEF, FIXED_POINT_POSITION);
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
- acc10 = mlal_sat_qs8x8(acc10, (char8)a1.s0, b0.s01234567, FIXED_POINT_POSITION);
- acc10 = mlal_sat_qs8x8(acc10, (char8)a1.s1, b1.s01234567, FIXED_POINT_POSITION);
- acc11 = mlal_sat_qs8x8(acc11, (char8)a1.s0, b0.s89ABCDEF, FIXED_POINT_POSITION);
- acc11 = mlal_sat_qs8x8(acc11, (char8)a1.s1, b1.s89ABCDEF, FIXED_POINT_POSITION);
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
- acc20 = mlal_sat_qs8x8(acc20, (char8)a2.s0, b0.s01234567, FIXED_POINT_POSITION);
- acc20 = mlal_sat_qs8x8(acc20, (char8)a2.s1, b1.s01234567, FIXED_POINT_POSITION);
- acc21 = mlal_sat_qs8x8(acc21, (char8)a2.s0, b0.s89ABCDEF, FIXED_POINT_POSITION);
- acc21 = mlal_sat_qs8x8(acc21, (char8)a2.s1, b1.s89ABCDEF, FIXED_POINT_POSITION);
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- acc30 = mlal_sat_qs8x8(acc30, (char8)a3.s0, b0.s01234567, FIXED_POINT_POSITION);
- acc30 = mlal_sat_qs8x8(acc30, (char8)a3.s1, b1.s01234567, FIXED_POINT_POSITION);
- acc31 = mlal_sat_qs8x8(acc31, (char8)a3.s0, b0.s89ABCDEF, FIXED_POINT_POSITION);
- acc31 = mlal_sat_qs8x8(acc31, (char8)a3.s1, b1.s89ABCDEF, FIXED_POINT_POSITION);
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- }
-
- // Left-over accumulations
- for(; src_addr.s0 < end_row_vec_a; src_addr += (int2)(1, src1_stride_y))
- {
- char a0 = *((__global char *)(src0_ptr + src_addr.s0 + 0 * src0_stride_y));
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
- char a1 = *((__global char *)(src0_ptr + src_addr.s0 + 1 * src0_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
- char a2 = *((__global char *)(src0_ptr + src_addr.s0 + 2 * src0_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- char a3 = *((__global char *)(src0_ptr + src_addr.s0 + 3 * src0_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- char16 b0 = vload16(0, (__global char *)(src1_ptr + src_addr.s1));
-
- acc00 = mlal_sat_qs8x8(acc00, (char8)a0, b0.s01234567, FIXED_POINT_POSITION);
- acc01 = mlal_sat_qs8x8(acc01, (char8)a0, b0.s89ABCDEF, FIXED_POINT_POSITION);
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
- acc10 = mlal_sat_qs8x8(acc10, (char8)a1, b0.s01234567, FIXED_POINT_POSITION);
- acc11 = mlal_sat_qs8x8(acc11, (char8)a1, b0.s89ABCDEF, FIXED_POINT_POSITION);
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
- acc20 = mlal_sat_qs8x8(acc20, (char8)a2, b0.s01234567, FIXED_POINT_POSITION);
- acc21 = mlal_sat_qs8x8(acc21, (char8)a2, b0.s89ABCDEF, FIXED_POINT_POSITION);
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- acc30 = mlal_sat_qs8x8(acc30, (char8)a3, b0.s01234567, FIXED_POINT_POSITION);
- acc31 = mlal_sat_qs8x8(acc31, (char8)a3, b0.s89ABCDEF, FIXED_POINT_POSITION);
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- }
-
- // Compute destination address
- Image dst = CONVERT_TO_IMAGE_STRUCT(dst);
-
- // Compute dst address
- __global uchar *dst_addr = offset(&dst, 0, 0);
-
- // Add offset for batched GEMM
- dst_addr += get_global_id(2) * dst_stride_z;
-
- // Multiply by the weight of matrix product and store the result
- char16 acc_qs8;
- acc_qs8 = convert_char16_sat((short16)(acc00, acc01));
-#if defined(ALPHA)
- acc_qs8 = mul_sat_qs8x16(acc_qs8, (char16)ALPHA, FIXED_POINT_POSITION);
-#endif // defined(ALPHA)
- vstore16(acc_qs8, 0, (__global char *)(dst_addr + 0 * dst_stride_y));
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
- acc_qs8 = convert_char16_sat((short16)(acc10, acc11));
-#if defined(ALPHA)
- acc_qs8 = mul_sat_qs8x16(acc_qs8, (char16)ALPHA, FIXED_POINT_POSITION);
-#endif // defined(ALPHA)
- vstore16(acc_qs8, 0, (__global char *)(dst_addr + 1 * dst_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
- acc_qs8 = convert_char16_sat((short16)(acc20, acc21));
-#if defined(ALPHA)
- acc_qs8 = mul_sat_qs8x16(acc_qs8, (char16)ALPHA, FIXED_POINT_POSITION);
-#endif // defined(ALPHA)
- vstore16(acc_qs8, 0, (__global char *)(dst_addr + 2 * dst_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- acc_qs8 = convert_char16_sat((short16)(acc30, acc31));
-#if defined(ALPHA)
- acc_qs8 = mul_sat_qs8x16(acc_qs8, (char16)ALPHA, FIXED_POINT_POSITION);
-#endif // defined(ALPHA)
- vstore16(acc_qs8, 0, (__global char *)(dst_addr + 3 * dst_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
-}
-
-/** This OpenCL kernel computes the matrix by matrix multiplication between the matrix A (src0) and matrix B (src1) in case both matrices have not beed reshaped
- *
- * @note This OpenCL kernel works with fixed point data types QS16
- * @note The number of elements processed along the x and y directions must be passed at compile time using -DNUM_ELEMS_PROCESSED_PER_THREAD_X and -DNUM_ELEMS_PROCESSED_PER_THREAD_Y
- * @note The number of matrix A columns, the number of elements processed per thread along the Y direction and the alpha's value need to be passed at compile time using -DCOLS_A, -DNUM_ELEMS_PROCESSED_PER_THREAD_Y and -DALPHA
- * @note The fixed point position need to be passed at compile time using -DFIXED_POINT_POSITION
- * @note The optional alpha value must be passed in 16 bit fixed point format using -DALPHA
- * @note In case the matrix B has 3 dimensions and the matrix A more than 3, in order to avoid out-of-bounds reads, the number of channels of matrix B must be passed at compile time using MATRIX_B_DEPTH (i.e. -DMATRIX_B_DEPTH=16)
- * This case can happen when GEMM is used to perform the element-wise multiplication through a batched matrix multiplication (2D Winograd) and we have multiple inputs (i.e. a = [K, M, 16, Batches], b = [N, K, 16])
- *
- * @param[in] src0_ptr Pointer to the source matrix. Supported data types: QS8/QS16
- * @param[in] src0_stride_x Stride of the source matrix in X dimension (in bytes)
- * @param[in] src0_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src0_stride_y Stride of the source matrix in Y dimension (in bytes)
- * @param[in] src0_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src0_offset_first_element_in_bytes The offset of the first element in the source matrix
- * @param[in] src1_ptr Pointer to the source matrix. Supported data types: same as @p src0_ptr
- * @param[in] src1_stride_x Stride of the source matrix in X dimension (in bytes)
- * @param[in] src1_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src1_stride_y Stride of the source matrix in Y dimension (in bytes)
- * @param[in] src1_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src1_offset_first_element_in_bytes The offset of the first element in the source matrix
- * @param[out] dst_ptr Pointer to the destination matrix Supported data types: same as @p src0_ptr
- * @param[in] dst_stride_x Stride of the destination matrix in X dimension (in bytes)
- * @param[in] dst_step_x dst_gx_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination matrix in Y dimension (in bytes)
- * @param[in] dst_step_y dst_gx_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination matrix
- * @param[in] src0_stride_z Stride of the source matrix in Z dimension (in bytes)
- * @param[in] src1_stride_z Stride of the source matrix in Z dimension (in bytes)
- * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
- */
-__kernel void gemm_mm_qs16(IMAGE_DECLARATION(src0),
- IMAGE_DECLARATION(src1),
- IMAGE_DECLARATION(dst),
- uint src0_stride_z,
- uint src1_stride_z,
- uint dst_stride_z)
-{
- int idx = get_global_id(0) * NUM_ELEMS_PROCESSED_PER_THREAD_X;
-
- // Compute starting address for matrix A and Matrix B
- int2 src_addr = ((int2)(src0_offset_first_element_in_bytes, src1_offset_first_element_in_bytes));
-
- // Update address for the matrix A
- src_addr.s0 += get_global_id(1) * src0_stride_y * NUM_ELEMS_PROCESSED_PER_THREAD_Y;
-
- // Update address for the matrix B
- src_addr.s1 += idx * sizeof(short);
-
- // Add offset for batched GEMM
- src_addr.s0 += get_global_id(2) * src0_stride_z;
-
-#if defined(MATRIX_B_DEPTH)
- // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
- src_addr.s1 += (get_global_id(2) % MATRIX_B_DEPTH) * src1_stride_z;
-#else // defined(MATRIX_B_DEPTH)
- src_addr.s1 += get_global_id(2) * src1_stride_z;
-#endif // defined(MATRIX_B_DEPTH)
-
- int end_row_vec_a = src_addr.s0 + (COLS_A * sizeof(short));
-
- int8 acc0 = 0;
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
- int8 acc1 = 0;
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
- int8 acc2 = 0;
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- int8 acc3 = 0;
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
-
- // This for loop performs 4 accumulations per iteration
- for(; src_addr.s0 <= (end_row_vec_a - 2 * (int)sizeof(short)); src_addr += (int2)(2 * sizeof(short), 2 * src1_stride_y))
- {
- short2 a0 = vload2(0, (__global short *)(src0_ptr + src_addr.s0 + 0 * src0_stride_y));
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
- short2 a1 = vload2(0, (__global short *)(src0_ptr + src_addr.s0 + 1 * src0_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
- short2 a2 = vload2(0, (__global short *)(src0_ptr + src_addr.s0 + 2 * src0_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- short2 a3 = vload2(0, (__global short *)(src0_ptr + src_addr.s0 + 3 * src0_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- short8 b0 = vload8(0, (__global short *)(src1_ptr + src_addr.s1 + 0 * src1_stride_y));
- short8 b1 = vload8(0, (__global short *)(src1_ptr + src_addr.s1 + 1 * src1_stride_y));
-
- acc0 = mlal_sat_qs16x8(acc0, (short8)a0.s0, b0, FIXED_POINT_POSITION);
- acc0 = mlal_sat_qs16x8(acc0, (short8)a0.s1, b1, FIXED_POINT_POSITION);
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
- acc1 = mlal_sat_qs16x8(acc1, (short8)a1.s0, b0, FIXED_POINT_POSITION);
- acc1 = mlal_sat_qs16x8(acc1, (short8)a1.s1, b1, FIXED_POINT_POSITION);
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
- acc2 = mlal_sat_qs16x8(acc2, (short8)a2.s0, b0, FIXED_POINT_POSITION);
- acc2 = mlal_sat_qs16x8(acc2, (short8)a2.s1, b1, FIXED_POINT_POSITION);
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- acc3 = mlal_sat_qs16x8(acc3, (short8)a3.s0, b0, FIXED_POINT_POSITION);
- acc3 = mlal_sat_qs16x8(acc3, (short8)a3.s1, b1, FIXED_POINT_POSITION);
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- }
-
- // Left-over accumulations
- for(; src_addr.s0 < end_row_vec_a; src_addr += (int2)(sizeof(short), src1_stride_y))
- {
- short a0 = *((__global short *)(src0_ptr + src_addr.s0 + 0 * src0_stride_y));
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
- short a1 = *((__global short *)(src0_ptr + src_addr.s0 + 1 * src0_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
- short a2 = *((__global short *)(src0_ptr + src_addr.s0 + 2 * src0_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- short a3 = *((__global short *)(src0_ptr + src_addr.s0 + 3 * src0_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- short8 b0 = vload8(0, (__global short *)(src1_ptr + src_addr.s1));
-
- acc0 = mlal_sat_qs16x8(acc0, (short8)a0, b0, FIXED_POINT_POSITION);
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
- acc1 = mlal_sat_qs16x8(acc1, (short8)a1, b0, FIXED_POINT_POSITION);
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
- acc2 = mlal_sat_qs16x8(acc2, (short8)a2, b0, FIXED_POINT_POSITION);
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- acc3 = mlal_sat_qs16x8(acc3, (short8)a3, b0, FIXED_POINT_POSITION);
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- }
-
- // Compute destination address
- Image dst = CONVERT_TO_IMAGE_STRUCT(dst);
-
- // Compute dst address
- __global uchar *dst_addr = offset(&dst, 0, 0);
-
- // Add offset for batched GEMM
- dst_addr += get_global_id(2) * dst_stride_z;
-
- // Multiply by the weight of matrix product and store the result
- short8 acc_qs16;
- acc_qs16 = convert_short8_sat(acc0);
-#if defined(ALPHA)
- acc_qs16 = mul_sat_qs16x8(acc_qs16, (short8)ALPHA, FIXED_POINT_POSITION);
-#endif // defined(ALPHA)
- vstore8(acc_qs16, 0, (__global short *)(dst_addr + 0 * dst_stride_y));
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
- acc_qs16 = convert_short8_sat(acc1);
-#if defined(ALPHA)
- acc_qs16 = mul_sat_qs16x8(acc_qs16, (short8)ALPHA, FIXED_POINT_POSITION);
-#endif // defined(ALPHA)
- vstore8(acc_qs16, 0, (__global short *)(dst_addr + 1 * dst_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 1
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
- acc_qs16 = convert_short8_sat(acc2);
-#if defined(ALPHA)
- acc_qs16 = mul_sat_qs16x8(acc_qs16, (short8)ALPHA, FIXED_POINT_POSITION);
-#endif // defined(ALPHA)
- vstore8(acc_qs16, 0, (__global short *)(dst_addr + 2 * dst_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 2
-#if NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
- acc_qs16 = convert_short8_sat(acc3);
-#if defined(ALPHA)
- acc_qs16 = mul_sat_qs16x8(acc_qs16, (short8)ALPHA, FIXED_POINT_POSITION);
-#endif // defined(ALPHA)
- vstore8(acc_qs16, 0, (__global short *)(dst_addr + 3 * dst_stride_y));
-#endif // NUM_ELEMS_PROCESSED_PER_THREAD_Y > 3
-}
-#endif // defined(FIXED_POINT_POSITION)
#endif // defined(COLS_A) && defined(NUM_ELEMS_PROCESSED_PER_THREAD_X) && (NUM_ELEMS_PROCESSED_PER_THREAD_Y)
#if defined(BETA)
@@ -2988,94 +2383,6 @@
vstore8(out, 0, (__global half *)dst.ptr);
}
#endif // defined(ARM_COMPUTE_OPENCL_FP16_ENABLED)
-
-#if defined(FIXED_POINT_POSITION)
-/** This OpenCL kernel performs the in-place matrix addition between 2 matrices in 8 bit fixed point taking into account that the second matrix might be weighted by a scalar value beta:
- *
- * @note The beta's value and the fixed point position need to be passed at compile time using -DBETA and -DFIXED_POINT_POSITION
- *
- * @note: BETA must be passed in 8 bit fixed point format
- *
- * @param[in] src_ptr Pointer to the source matrix. Supported data types: QS8
- * @param[in] src_stride_x Stride of the source matrix in X dimension (in bytes)
- * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src_stride_y Stride of the source matrix in Y dimension (in bytes)
- * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the destination tensor in Z dimension (in bytes)
- * @param[in] src_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source matrix
- * @param[out] dst_ptr Pointer to the destination matrix Supported data types: same as @p src_ptr
- * @param[in] dst_stride_x Stride of the destination matrix in X dimension (in bytes)
- * @param[in] dst_step_x dst_gx_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination matrix in Y dimension (in bytes)
- * @param[in] dst_step_y dst_gx_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
- * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination matrix
- */
-__kernel void gemm_ma_qs8(TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst))
-{
- // Compute source and destination addresses
- Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
- Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
-
- // Load values from A x B
- char16 alpha_ab = vload16(0, (__global char *)dst.ptr);
-
- // Load values from Matrix C
- char16 c = vload16(0, (__global char *)src.ptr);
-
- // Computes alpha * axb + beta * c
- char16 out = mla_sat_qs8x16(alpha_ab, (char16)BETA, c, FIXED_POINT_POSITION);
-
- // Store final result in axb matrix
- vstore16(out, 0, (__global char *)dst.ptr);
-}
-
-/** This OpenCL kernel performs the in-place matrix addition between 2 matrices in 16 bit fixed point taking into account that the second matrix might be weighted by a scalar value beta:
- *
- * @note The beta's value and the fixed point position need to be passed at compile time using -DBETA and -DFIXED_POINT_POSITION
- *
- * @note: BETA must be passed in 16 bit fixed point format
- *
- * @param[in] src_ptr Pointer to the source matrix. Supported data types: QS16
- * @param[in] src_stride_x Stride of the source matrix in X dimension (in bytes)
- * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] src_stride_y Stride of the source matrix in Y dimension (in bytes)
- * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] src_stride_z Stride of the destination tensor in Z dimension (in bytes)
- * @param[in] src_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source matrix
- * @param[out] dst_ptr Pointer to the destination matrix Supported data types: same as @p src_ptr
- * @param[in] dst_stride_x Stride of the destination matrix in X dimension (in bytes)
- * @param[in] dst_step_x dst_gx_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] dst_stride_y Stride of the destination matrix in Y dimension (in bytes)
- * @param[in] dst_step_y dst_gx_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
- * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination matrix
- */
-__kernel void gemm_ma_qs16(TENSOR3D_DECLARATION(src),
- TENSOR3D_DECLARATION(dst))
-{
- // Compute source and destination addresses
- Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
- Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
-
- // Load values from A x B
- short8 alpha_ab = vload8(0, (__global short *)dst.ptr);
-
- // Load values from Matrix C
- short8 c = vload8(0, (__global short *)src.ptr);
-
- // Computes alpha * axb + beta * c
- short8 out = mla_sat_qs16x8(alpha_ab, (short8)BETA, c, FIXED_POINT_POSITION);
-
- // Store final result in axb matrix
- vstore8(out, 0, (__global short *)dst.ptr);
-}
-#endif // defined(FIXED_POINT_POSITION)
#endif // defined(BETA)
#if defined(WIDTH_VECTOR_A)
@@ -3151,7 +2458,7 @@
* @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=short.
* @note The vector size must be passed at compile time using -DVECTOR_SIZE e.g. -DVECTOR_SIZE=16.
*
- * @param[in, out] accum_ptr Pointer to the accumulate tensor. Supported data type: U8/S8/QS8/U16/S16/F16/U32/S32/F32
+ * @param[in, out] accum_ptr Pointer to the accumulate tensor. Supported data type: U8/S8/U16/S16/F16/U32/S32/F32
* @param[in] accum_stride_x Stride of the accmulate tensor in X dimension (in bytes)
* @param[in] accum_step_x accum_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] accum_stride_y Stride of the accumlulate tensor in Y dimension (in bytes)
@@ -3175,11 +2482,7 @@
accum_value = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)accum.ptr);
VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE)
biases_value = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)biases.ptr);
-#ifdef FIXED_POINT_POSITION
- accum_value = ADD_SAT_OP_EXPAND(biases_value, accum_value, DATA_TYPE, VECTOR_SIZE);
-#else // FIXED_POINT_POSITION
- accum_value = biases_value + accum_value;
-#endif // FIXED_POINT_POSITION
+ accum_value = biases_value + accum_value;
// Store result in the accumulate buffer
VSTORE(VECTOR_SIZE)
(accum_value, 0, (__global DATA_TYPE *)accum.ptr);
diff --git a/src/core/CL/cl_kernels/im2col.cl b/src/core/CL/cl_kernels/im2col.cl
index 6f25ad4..d034b30 100644
--- a/src/core/CL/cl_kernels/im2col.cl
+++ b/src/core/CL/cl_kernels/im2col.cl
@@ -23,12 +23,7 @@
*/
#include "helpers.h"
-#if defined(FIXED_POINT_POSITION)
-#include "fixed_point.h"
-#endif // FIXED_POINT_POSITION
-
#if defined(DATA_TYPE) && defined(ELEMENT_SIZE)
-#if !defined(FIXED_POINT_POSITION)
#if ELEMENT_SIZE == 1
#define COND_DATA_TYPE char
@@ -50,7 +45,7 @@
* @note The stride along the Y direction must be passed at compile time using -DSTRIDE_Y: e.g. -DSTRIDE_Y=1
* @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
*
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -139,7 +134,7 @@
* @note The dilation_x and dilation_y must be passed at compile time using -DDILATION_X and -DDILATION_Y: e.g. -DDILATION_X=1, -DDILATION_Y=1
* @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
*
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -232,7 +227,7 @@
* @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
* @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
*
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -338,7 +333,7 @@
* @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
* @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
*
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -425,7 +420,7 @@
* @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
* @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
*
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -598,7 +593,7 @@
* @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1
* @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
*
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -788,7 +783,6 @@
#endif // HAS_BIAS
}
#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_DEPTH)
-#endif // !defined(FIXED_POINT_POSITION)
#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE)
/** This kernel reshapes the input tensor to a tensor used to perform convolution using GEMM when
@@ -799,7 +793,7 @@
* @note The width modulo vector size must be passed at compile time using -DWIDTH_MOD_VECTOR_SIZE e.g. -DWIDTH_MOD_VECTOR_SIZE=3.
* @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
*
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -863,11 +857,7 @@
#ifdef HAS_BIAS
if(ch == (KERNEL_DEPTH - 1))
{
-#ifdef FIXED_POINT_POSITION
- *output_ptr = (DATA_TYPE)(1 << FIXED_POINT_POSITION);
-#else // FIXED_POINT_POSITION
*output_ptr = 1.0f;
-#endif // FIXED_POINT_POSITION
}
#endif // HAS_BIAS
}
@@ -886,7 +876,7 @@
* @note The dilation_x and dilation_y must be passed at compile time using -DDILATION_X and -DDILATION_Y: e.g. -DDILATION_X=1, -DDILATION_Y=1
* @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
*
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -950,11 +940,7 @@
#ifdef HAS_BIAS
if(ch == (KERNEL_DEPTH - 1))
{
-#ifdef FIXED_POINT_POSITION
- *output_ptr = (DATA_TYPE)(1 << FIXED_POINT_POSITION);
-#else // FIXED_POINT_POSITION
*output_ptr = 1.0f;
-#endif // FIXED_POINT_POSITION
}
#endif // HAS_BIAS
}
@@ -966,7 +952,7 @@
* @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float
* @note In case biases will be added in late stage, -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.
*
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -999,11 +985,7 @@
if(get_global_id(0) == (get_global_size(0) - 1) && get_global_id(1) == (get_global_size(1) - 1) && get_global_id(2) == (get_global_size(2) - 1))
{
tmp_out_ptr += dst_stride_x;
-#ifdef FIXED_POINT_POSITION
- *((__global DATA_TYPE *)tmp_out_ptr) = (DATA_TYPE)(1 << FIXED_POINT_POSITION);
-#else // FIXED_POINT_POSITION
*((__global DATA_TYPE *)tmp_out_ptr) = (DATA_TYPE)1.0f;
-#endif // FIXED_POINT_POSITION
}
#endif // HAS_BIAS
}
diff --git a/src/core/CL/cl_kernels/l2_normalize.cl b/src/core/CL/cl_kernels/l2_normalize.cl
index 8d47631..f58e98b 100644
--- a/src/core/CL/cl_kernels/l2_normalize.cl
+++ b/src/core/CL/cl_kernels/l2_normalize.cl
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -28,11 +28,11 @@
* @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
* @note The data size must be passed at compile time using -DDATA_SIZE e.g. -DDATA_SIZE=32
*
- * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[in] sum_ptr Pointer to the source tensor. Supported data types: QS8/F16/F32
+ * @param[in] sum_ptr Pointer to the source tensor. Supported data types: F16/F32
* @param[in] sum_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] sum_step_x sum_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] sum_offset_first_element_in_bytes The offset of the first element in the source tensor
diff --git a/src/core/CL/cl_kernels/normalization_layer.cl b/src/core/CL/cl_kernels/normalization_layer.cl
index bc00252..dbdad27 100644
--- a/src/core/CL/cl_kernels/normalization_layer.cl
+++ b/src/core/CL/cl_kernels/normalization_layer.cl
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -23,22 +23,6 @@
*/
#include "helpers.h"
-#if defined(FIXED_POINT_POSITION)
-
-#include "fixed_point.h"
-#define MUL_OP(x, y) MUL_SAT_OP_EXPAND((x), (y), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
-#define ADD_OP(x, y) ADD_SAT_OP_EXPAND((x), (y), DATA_TYPE, VEC_SIZE)
-#define DIV_OP(x, y) DIV_SAT_OP_VEC_EXPAND((x), (y), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
-#define EXP_OP(x) EXP_OP_EXPAND((x), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
-#define LOG_OP(x) LOG_OP_EXPAND((x), DATA_TYPE, VEC_SIZE, FIXED_POINT_POSITION)
-#define POW_OP(x, y) EXP_OP(MUL_OP(LOG_OP((x)), (y)))
-#define SQCVT_SAT(a) SQCVT_SAT_OP_EXPAND((a), DATA_TYPE, FIXED_POINT_POSITION)
-
-#define LOAD_OP(offset, ptr) vload16(offset, ptr)
-#define STORE_OP(data, offset, ptr) vstore16(data, offset, ptr)
-
-#else // FIXED_POINT_POSITION
-
#define MUL_OP(x, y) ((x) * (y))
#define ADD_OP(x, y) ((x) + (y))
#define DIV_OP(x, y) ((x) / (y))
@@ -48,18 +32,15 @@
#define LOAD_OP(offset, ptr) vload4(offset, ptr)
#define STORE_OP(data, offset, ptr) vstore4(data, offset, ptr)
-#endif // FIXED_POINT_POSITION
-
/** Apply cross-map normalization.
*
* @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short
* @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size, e.g. -DVEC_SIZE=16
* @note The radius should be given as a preprocessor argument using -DRADIUS=size. e.g. -DRADIUS=5
* @note The number of slices should be given as a preprocessor argument using -DNUM_SLICES=size. e.g. -DNUM_SLICES=192
- * @note In case of fixed-point operation -DFIXED_POINT_POSITION=fixed_point_position must be provided: e.g. -DFIXED_POINT_POSITION=3
* @note Scaling coefficient (= alpha/norm_size), beta and kappa need to be passed at compile time using -DCOEFF, -DALPHA and -DKAPPA
*
- * @param[in] input_ptr Pointer to the first source tensor. Supported data types: QS8/QS16/F16/F32
+ * @param[in] input_ptr Pointer to the first source tensor. Supported data types: F16/F32
* @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the first source tensor in Y dimension (in bytes)
@@ -116,10 +97,9 @@
* @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short
* @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size, e.g. -DVEC_SIZE=16
* @note The radius should be given as a preprocessor argument using -DRADIUS=size. e.g. -DRADIUS=5
- * @note In case of fixed-point operation -DFIXED_POINT_POSITION=fixed_point_position must be provided: e.g. -DFIXED_POINT_POSITION=3
* @note Scaling coefficient (= alpha/norm_size), beta and kappa need to be passed at compile time using -DCOEFF, -DALPHA and -DKAPPA
*
- * @param[in] input_ptr Pointer to the first source tensor. Supported data types: QS8/F16/F32
+ * @param[in] input_ptr Pointer to the first source tensor. Supported data types: F16/F32
* @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the first source tensor in Y dimension (in bytes)
diff --git a/src/core/CL/cl_kernels/permute.cl b/src/core/CL/cl_kernels/permute.cl
index 6f978c9..03fc15e 100644
--- a/src/core/CL/cl_kernels/permute.cl
+++ b/src/core/CL/cl_kernels/permute.cl
@@ -29,7 +29,7 @@
* @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float
* @attention Input tensor depth should be given as a preprocessor argument using -DDEPTH_IN=size. e.g. -DDEPTH_IN=16
*
- * @param[in] input_ptr Pointer to the source image. Supported data types: U8/S8/QS8/QASYMM8/U16/S16/QS16/F16/U32/S32/F32
+ * @param[in] input_ptr Pointer to the source image. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32
* @param[in] input_stride_x Stride of the source image in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the source image in Y dimension (in bytes)
@@ -63,7 +63,7 @@
* @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float
* @attention Input tensor depth should be given as a preprocessor argument using -DDEPTH_IN=size. e.g. -DDEPTH_IN=16
*
- * @param[in] input_ptr Pointer to the source image. Supported data types: U8/S8/QS8/QASYMM8/U16/S16/QS16/F16/U32/S32/F32
+ * @param[in] input_ptr Pointer to the source image. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32
* @param[in] input_stride_x Stride of the source image in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the source image in Y dimension (in bytes)
@@ -97,7 +97,7 @@
* @attention Data type can be passed using the -DDATA_TYPE compile flag, e.g. -DDATA_TYPE=float
* @attention Input tensor depth should be given as a preprocessor argument using -DDEPTH_IN=size. e.g. -DDEPTH_IN=16
*
- * @param[in] input_ptr Pointer to the source image. Supported data types: U8/S8/QS8/QASYMM8/U16/S16/QS16/F16/U32/S32/F32
+ * @param[in] input_ptr Pointer to the source image. Supported data types: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32
* @param[in] input_stride_x Stride of the source image in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the source image in Y dimension (in bytes)
diff --git a/src/core/CL/cl_kernels/pixelwise_mul_int.cl b/src/core/CL/cl_kernels/pixelwise_mul_int.cl
index b5734a3..c99a08a 100644
--- a/src/core/CL/cl_kernels/pixelwise_mul_int.cl
+++ b/src/core/CL/cl_kernels/pixelwise_mul_int.cl
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -23,18 +23,6 @@
*/
#include "helpers.h"
-#if defined(FIXED_POINT_POSITION)
-
-#include "fixed_point.h"
-
-#if defined(SATURATE)
-#define MUL_OP(x, y, scale, type, size) MUL_SAT_OP_EXPAND((x), (y), type, size, FIXED_POINT_POSITION)
-#else // SATURATE
-#define MUL_OP(x, y, scale, type, size) MUL_OP_EXPAND((x), (y), type, size, FIXED_POINT_POSITION)
-#endif // SATURATE
-
-#else // FIXED_POINT_POSITION
-
#if defined(SATURATE)
#define CONVERT_OP_INT_STR(x, type, size) (convert_##type##size##_sat(x))
#else // SATURATE
@@ -44,17 +32,14 @@
#define MUL_OP(x, y, scale, type, size) CONVERT_OP_INT((x) * (y) >> scale, type, size)
-#endif // FIXED_POINT_POSITION
-
/** Performs a pixelwise multiplication with integer scale of integer inputs.
*
* @attention The inputs and output data types need to be passed at compile time using -DDATA_TYPE_IN1, -DDATA_TYPE_IN2 and -DDATA_TYPE_OUT:
* e.g. -DDATA_TYPE_IN1=uchar -DDATA_TYPE_IN2=ushort -DDATA_TYPE_OUT=short
* @attention The data_type of the intermediate result of the multiplication should passed as well using -DDATA_TYPE_RES.
* e.g. If one of inputs is S16 -DDATA_TYPE_RES=int should be passed else -DDATA_TYPE_RES=short.
- * @note In case of fixed-point operation -DFIXED_POINT_POSITION=fixed_point_position must be provided: e.g. -DFIXED_POINT_POSITION=3
*
- * @param[in] in1_ptr Pointer to the source image. Supported data types: U8/QS8/QS16/S16
+ * @param[in] in1_ptr Pointer to the source image. Supported data types: U8/S16
* @param[in] in1_stride_x Stride of the source image in X dimension (in bytes)
* @param[in] in1_step_x in1_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] in1_stride_y Stride of the source image in Y dimension (in bytes)
@@ -78,7 +63,7 @@
* @param[in] out_stride_z Stride of the destination image in Y dimension (in bytes)
* @param[in] out_step_z out_stride_z * number of elements along Y processed per workitem(in bytes)
* @param[in] out_offset_first_element_in_bytes The offset of the first element in the destination image
- * @param[in] scale Integer scaling factor. Supported data types: S32 (ignored for QS8 and QS16 as the assumption is scale = 1).
+ * @param[in] scale Integer scaling factor. Supported data types: S32.
*/
__kernel void pixelwise_mul_int(
TENSOR3D_DECLARATION(in1),
diff --git a/src/core/CL/cl_kernels/pooling_layer.cl b/src/core/CL/cl_kernels/pooling_layer.cl
index 2c7ddfd..c38a78c 100644
--- a/src/core/CL/cl_kernels/pooling_layer.cl
+++ b/src/core/CL/cl_kernels/pooling_layer.cl
@@ -23,28 +23,6 @@
*/
#include "helpers.h"
-#ifdef FIXED_POINT_POSITION
-
-#include "fixed_point.h"
-
-#if defined(POOL_AVG)
-#define POOL_OP(x, y) add_sat(x, y)
-#else /* POOL_AVG */
-#define POOL_OP(x, y) (max((x), (y)))
-#endif /* POOL_AVG */
-
-#define DIV_OP1(x, y) DIV_SAT_OP_EXPAND((x), (y), DATA_TYPE, FIXED_POINT_POSITION)
-#define DIV_OP(x, y) DIV_OP1(x, y << FIXED_POINT_POSITION)
-#define SQRT_OP(x) DIV_OP1((1 << FIXED_POINT_POSITION), (INVSQRT_OP_EXPAND((x), DATA_TYPE, 1, FIXED_POINT_POSITION)))
-
-#if defined(POOL_L2)
-#define POW2_OP(x, vec_size) MUL_SAT_OP_EXPAND((x), (x), DATA_TYPE, vec_size, FIXED_POINT_POSITION)
-#else /* defined(POOL_L2) */
-#define POW2_OP(x, vec_size) (x)
-#endif /* defined(POOL_L2) */
-
-#else /* FIXED_POINT_POSITION */
-
#if defined(POOL_AVG) || defined(POOL_L2)
#define POOL_OP(x, y) ((x) + (y))
#else /* defined(POOL_AVG) || defined(POOL_L2) */
@@ -60,8 +38,6 @@
#define DIV_OP(x, y) (x * (1.f / y))
#define SQRT_OP(x) sqrt((x))
-#endif /* FIXED_POINT_POSITION */
-
#define DIV_OP_NHWC(x, y) (x * (VEC_DATA_TYPE(DATA_TYPE, 8))(1.f / y))
#if STRIDE_X == 1
@@ -201,14 +177,14 @@
/** Performs a pooling function of pool size equal to 2.
*
- * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are QS8/QS16/F16/F32;
+ * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32;
* @note In case of average pooling the following information must be passed at compile time:
* -DPOOL_AVG or -DPOOL_L2 must be provided otherwise max pooling will be performed.
* -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad)
* -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions
* -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension
*
- * @param[in] input_ptr Pointer to the source image. Supported data types: QS8/QS16/F16/F32
+ * @param[in] input_ptr Pointer to the source image. Supported data types: F16/F32
* @param[in] input_stride_x Stride of the source image in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the source image in Y dimension (in bytes)
@@ -265,14 +241,14 @@
/** Performs a pooling function of pool size equal to 3
*
- * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are QS8/QS16/F16/F32;
+ * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32;
* @note In case of average pooling the following information must be passed at compile time:
* -DPOOL_AVG or -DPOOL_L2 must be provided otherwise max pooling will be performed.
* -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad)
* -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions
* -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension
*
- * @param[in] input_ptr Pointer to the source image. Supported data types: QS8/QS16/F16/F32
+ * @param[in] input_ptr Pointer to the source image. Supported data types: F16/F32
* @param[in] input_stride_x Stride of the source image in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the source image in Y dimension (in bytes)
@@ -331,7 +307,7 @@
*(__global DATA_TYPE *)output.ptr = res;
}
-#if defined(POOLING3x3) && !defined(FIXED_POINT_POSITION)
+#if defined(POOLING3x3)
#define CONVERT_OP(data_type) convert_##data_type##4
#define CONVERT_VECTOR4(data_type) CONVERT_OP(data_type)
@@ -353,7 +329,7 @@
/** Performs an optimized pooling function of pool size equal to 3 when the stride_x is less equal than 3
*
- * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are QS8/QS16/F16/F32;
+ * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32;
* @note In case of average pooling the following information must be passed at compile time:
* -DPOOL_AVG or -DPOOL_L2 must be provided otherwise max pooling will be performed.
* -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad)
@@ -403,7 +379,7 @@
vstore4(res, 0, (__global DATA_TYPE *)output.ptr);
}
-#endif // defined(POOLING3x3) && !defined(FIXED_POINT_POSITION)
+#endif // defined(POOLING3x3)
#if defined(POOL_SIZE_X) && defined(POOL_SIZE_Y)
@@ -411,23 +387,17 @@
#if defined(POOL_AVG) || defined(POOL_L2)
#define INITIAL_VALUE 0
#else /* defined(POOL_AVG) || defined(POOL_L2) */
-#ifdef FIXED_POINT_POSITION
-#define MIN_VAL_EXPAND(type) type##_MIN
-#define MIN_VAL(type) MIN_VAL_EXPAND(type)
-#define INITIAL_VALUE MIN_VAL(DATA_TYPE)
-#else // FIXED_POINT_POSITION
#if FP16
#define INITIAL_VALUE -HALF_MAX
#else // FP16
#define INITIAL_VALUE -FLT_MAX
#endif // FP16
-#endif // FIXED_POINT_POSITION
#endif // POOL_AVG
/** Performs a pooling function of pool size equal to N (NCHW)
*
- * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are QS8/QS16/F16/F32;
+ * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32;
* @note -DFP16 must be passed at compile time if half float data type is used
* @note Pool sizes must be passed using -DPOOL_SIZE_X and -DPOOL_SIZE_Y e.g. -DPOOL_SIZE_X=13;
* @note In case of average pooling the following information must be passed at compile time:
@@ -436,7 +406,7 @@
* -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions
* -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension
*
- * @param[in] input_ptr Pointer to the source image. Supported data types: QS8/QS16/F16/F32
+ * @param[in] input_ptr Pointer to the source image. Supported data types: F16/F32
* @param[in] input_stride_x Stride of the source image in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the source image in Y dimension (in bytes)
diff --git a/src/core/CL/cl_kernels/reshape_layer.cl b/src/core/CL/cl_kernels/reshape_layer.cl
index 23eccbf..11393d2 100644
--- a/src/core/CL/cl_kernels/reshape_layer.cl
+++ b/src/core/CL/cl_kernels/reshape_layer.cl
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -27,7 +27,7 @@
*
* @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short
*
- * @param[in] input_ptr Pointer to the first source tensor. Supported data types: U8/S8/QS8/U16/S16/QS16/U32/S32/F16/F32
+ * @param[in] input_ptr Pointer to the first source tensor. Supported data types: U8/S8/U16/S16/U32/S32/F16/F32
* @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] input_stride_y Stride of the first source tensor in Y dimension (in bytes)
diff --git a/src/core/CL/cl_kernels/softmax_layer.cl b/src/core/CL/cl_kernels/softmax_layer.cl
index aa1fa01..e549b44 100644
--- a/src/core/CL/cl_kernels/softmax_layer.cl
+++ b/src/core/CL/cl_kernels/softmax_layer.cl
@@ -23,23 +23,6 @@
*/
#include "helpers.h"
-#ifdef FIXED_POINT_POSITION
-
-#include "fixed_point.h"
-#define MAX_OP(x, y, type, size) MAX_OP_EXPAND(x, y, type, size)
-#define ADD_OP(x, y, type, size) ADD_SAT_OP_EXPAND((x), (y), type, size)
-#define SUB_OP(x, y, type, size) SUB_SAT_OP_EXPAND((x), (y), type, size)
-#define MUL_OP(x, y, type, size) MUL_SAT_OP_EXPAND((x), (y), type, size, FIXED_POINT_POSITION)
-#define DIV_OP(x, y, type, size) DIV_SAT_OP_VEC_EXPAND((x), (y), type, size, FIXED_POINT_POSITION)
-#define EXP_OP(x, type, size) EXP_OP_EXPAND((x), type, size, FIXED_POINT_POSITION)
-
-#define MIN_VAL_EXPAND(type) type##_MIN
-#define MIN_VAL(type) MIN_VAL_EXPAND(type)
-#define MINVAL MIN_VAL(DATA_TYPE)
-#define SELECT_DATA_TYPE EXPAND(DATA_TYPE)
-
-#else /* FIXED_POINT_POSITION */
-
#define MAX_OP(x, y, type, size) max((x), (y))
#define ADD_OP(x, y, type, size) ((x) + (y))
#define SUB_OP(x, y, type, size) ((x) - (y))
@@ -55,8 +38,6 @@
#define SELECT_DATA_TYPE int
#endif /* USE_F16 */
-#endif /* FIXED_POINT_POSITION */
-
/* Number of workitems in dimension 0. */
#if !defined(GRID_SIZE)
#define GRID_SIZE 1
@@ -91,9 +72,8 @@
/** Divides all the values of the input tensor by the sum calculated from softmax_layer_shift_exp_sum kernel.
*
* @note Datatype must be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short
- * @note Fixed point position must be given as a preprocessor argument using -DFIXED_POINT_POSITION=pos. e.g. DFIXED_POINT_POSITION=4
*
- * @param[in] src_ptr Pointer to the source tensor slice. Supported data types: QS8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor slice. Supported data types: F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -138,11 +118,10 @@
* then gets the exponent of each element as sums all elements across each row.
*
* @note Datatype must be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short
- * @note Fixed point position must be given as a preprocessor argument using -DFIXED_POINT_POSITION=pos. e.g. DFIXED_POINT_POSITION=4
* @note In case the input is not a multiple of VECTOR_SIZE (2,4,8,16) -DNON_MULTIPLE_OF_VECTOR_SIZE must be passed.
* @note Beta can be optionally passed at compile time using -DBETA (by default, it is 1.0).
*
- * @param[in] src_ptr Pointer to the source tensor slice. Supported data types: QS8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor slice. Supported data types: F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -288,11 +267,10 @@
* then gets the exponent of each element as sums all elements across each row.
*
* @note Datatype must be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short
- * @note Fixed point position must be given as a preprocessor argument using -DFIXED_POINT_POSITION=pos. e.g. DFIXED_POINT_POSITION=4
* @note In case the input is not a multiple of VECTOR_SIZE (2,4,8,16) -DNON_MULTIPLE_OF_VECTOR_SIZE must be passed.
* @note Beta can be optionally passed at compile time using -DBETA (by default, it is 1.0).
*
- * @param[in] src_ptr Pointer to the source tensor slice. Supported data types: QS8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor slice. Supported data types: F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
diff --git a/src/core/CL/cl_kernels/softmax_layer_quantized.cl b/src/core/CL/cl_kernels/softmax_layer_quantized.cl
index c055381..95d6d4b 100644
--- a/src/core/CL/cl_kernels/softmax_layer_quantized.cl
+++ b/src/core/CL/cl_kernels/softmax_layer_quantized.cl
@@ -230,10 +230,9 @@
* then gets the exponent of each element as sums all elements across each row.
*
* @note Datatype must be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short
- * @note Fixed point position must be given as a preprocessor argument using -DFIXED_POINT_POSITION=pos. e.g. DFIXED_POINT_POSITION=4
* @note In case the input is not a multiple of VECTOR_SIZE (2,4,8,16) -DNON_MULTIPLE_OF_VECTOR_SIZE must be passed.
*
- * @param[in] src_ptr Pointer to the source tensor slice. Supported data types: QS8/QS16/F16/F32
+ * @param[in] src_ptr Pointer to the source tensor slice. Supported data types: F16/F32
* @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)
* @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)
@@ -519,7 +518,6 @@
/** Divides all the values of the input tensor by the sum calculated from softmax_layer_shift_exp_sum kernel.
*
- * @note Fixed point position must be given as a preprocessor argument using -DFIXED_POINT_POSITION=pos. e.g. DFIXED_POINT_POSITION=4
* @note Quantized beta can be optionally passed at compile time using -DINPUT_BETA_MULTIPLIER and -DINPUT_BETA_LEFT_SHIFT (if undefined, assume beta equals 1.0)
* @note -DDIFF_MIN must be passed at compile time. It is threshold difference between maximum value of input data and current processed value, it defines whether the value will be taken into account or not.
*
diff --git a/src/core/CL/kernels/CLActivationLayerKernel.cpp b/src/core/CL/kernels/CLActivationLayerKernel.cpp
index 3d8824a..1ae1032 100644
--- a/src/core/CL/kernels/CLActivationLayerKernel.cpp
+++ b/src/core/CL/kernels/CLActivationLayerKernel.cpp
@@ -27,7 +27,6 @@
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/CLValidate.h"
#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/FixedPoint.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/IAccessWindow.h"
#include "arm_compute/core/TensorInfo.h"
@@ -47,7 +46,7 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG((input->data_type() == DataType::QASYMM8) && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU)
&& (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU)
&& (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU),
@@ -58,7 +57,6 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
@@ -118,7 +116,6 @@
const unsigned int num_elems_processed_per_iteration = 16 / input->info()->element_size();
const DataType dt = input->info()->data_type();
- const int fixed_point_position = input->info()->fixed_point_position();
float a_const = act_info.a();
float b_const = act_info.b();
int a_const_int = 0;
@@ -127,16 +124,8 @@
// Create quantized version of constants a, b if needed
if(is_data_type_quantized(dt))
{
- if(is_data_type_fixed_point(dt))
- {
- a_const_int = static_cast<int>(lround(a_const * (1 << fixed_point_position)));
- b_const_int = static_cast<int>(lround(b_const * (1 << fixed_point_position)));
- }
- else
- {
- a_const_int = input->info()->quantization_info().quantize(a_const, RoundingPolicy::TO_NEAREST_UP);
- b_const_int = input->info()->quantization_info().quantize(b_const, RoundingPolicy::TO_NEAREST_UP);
- }
+ a_const_int = input->info()->quantization_info().quantize(a_const, RoundingPolicy::TO_NEAREST_UP);
+ b_const_int = input->info()->quantization_info().quantize(b_const, RoundingPolicy::TO_NEAREST_UP);
}
// Set build options
@@ -177,10 +166,6 @@
}
build_opts.emplace((_run_in_place) ? "-DIN_PLACE" : "");
- if(is_data_type_fixed_point(dt))
- {
- build_opts.emplace(("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(fixed_point_position)));
- }
// Create kernel
std::string kernel_name = is_data_type_quantized_asymmetric(dt) ? std::string("activation_layer_qa8") : std::string("activation_layer");
diff --git a/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp b/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp
index 011807a..78651f8 100644
--- a/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp
+++ b/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp
@@ -37,9 +37,9 @@
{
ARM_COMPUTE_UNUSED(policy);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input2);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
const bool is_qasymm = is_data_type_quantized_asymmetric(input1.data_type()) || is_data_type_quantized_asymmetric(input2.data_type());
if(is_qasymm)
@@ -50,18 +50,16 @@
const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());
ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(&input1, &input2);
// Validate in case of configured output
if(output.total_size() > 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&output);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG((output.data_type() == DataType::U8) && ((input1.data_type() != DataType::U8) || (input2.data_type() != DataType::U8)),
"Output can only be U8 if both inputs are U8");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
"Wrong shape for output");
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(&input1, &output);
if(is_qasymm)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output);
@@ -142,11 +140,7 @@
build_opts.emplace("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type()));
build_opts.emplace("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type()));
build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type()));
- if(is_data_type_fixed_point(input1->info()->data_type()))
- {
- build_opts.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input1->info()->fixed_point_position()));
- }
- else if(is_data_type_quantized_asymmetric(input1->info()->data_type()))
+ if(is_data_type_quantized_asymmetric(input1->info()->data_type()))
{
build_opts.emplace("-DOFFSET=" + support::cpp11::to_string(input1->info()->quantization_info().offset));
kernel_name += "_quantized";
diff --git a/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp b/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp
index db91bc0..aeee602 100644
--- a/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp
+++ b/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -44,21 +44,19 @@
{
ARM_COMPUTE_UNUSED(policy);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input1);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input2);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, input2);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input1, input2);
// Validate in case of configured output
if((output != nullptr) && (output->total_size() != 0))
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(output);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() == DataType::U8 && (input1->data_type() != DataType::U8 || input2->data_type() != DataType::U8),
"Output can only be U8 if both inputs are U8");
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input1, output);
}
return Status{};
@@ -122,10 +120,6 @@
build_opts.emplace("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type()));
build_opts.emplace("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type()));
build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type()));
- if(is_data_type_fixed_point(input1->info()->data_type()))
- {
- build_opts.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input1->info()->fixed_point_position()));
- }
// Create kernel
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("arithmetic_sub", build_opts));
diff --git a/src/core/CL/kernels/CLBatchNormalizationLayerKernel.cpp b/src/core/CL/kernels/CLBatchNormalizationLayerKernel.cpp
index 391baef..5999c66 100644
--- a/src/core/CL/kernels/CLBatchNormalizationLayerKernel.cpp
+++ b/src/core/CL/kernels/CLBatchNormalizationLayerKernel.cpp
@@ -27,7 +27,6 @@
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/CLValidate.h"
#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/FixedPoint.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Utils.h"
@@ -46,22 +45,19 @@
{
ARM_COMPUTE_UNUSED(epsilon);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, var);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, mean, var);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, mean, var);
ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL)) != mean->dimension(0));
if(beta != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, beta);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, beta);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, beta);
}
if(gamma != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, gamma);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, gamma);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, gamma);
}
if(act_info.enabled())
@@ -78,7 +74,6 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
@@ -168,7 +163,6 @@
build_opts.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a()));
build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b()));
build_opts.add_option_if(_run_in_place, "-DIN_PLACE");
- build_opts.add_option_if(is_data_type_fixed_point(input->info()->data_type()), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
build_opts.add_option_if(beta == nullptr, "-DUSE_DEFAULT_BETA");
build_opts.add_option_if(gamma == nullptr, "-DUSE_DEFAULT_GAMMA");
diff --git a/src/core/CL/kernels/CLChannelShuffleLayerKernel.cpp b/src/core/CL/kernels/CLChannelShuffleLayerKernel.cpp
index 1de9872..5f0f0ae 100644
--- a/src/core/CL/kernels/CLChannelShuffleLayerKernel.cpp
+++ b/src/core/CL/kernels/CLChannelShuffleLayerKernel.cpp
@@ -39,8 +39,8 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, unsigned int num_groups)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8,
- DataType::U16, DataType::S16, DataType::QS16,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8,
+ DataType::U16, DataType::S16,
DataType::U32, DataType::S32,
DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(num_groups < 2, "Channel shuffling with less than 2 groups would be inefficient");
diff --git a/src/core/CL/kernels/CLCol2ImKernel.cpp b/src/core/CL/kernels/CLCol2ImKernel.cpp
index 64e6a0b..6274c90 100644
--- a/src/core/CL/kernels/CLCol2ImKernel.cpp
+++ b/src/core/CL/kernels/CLCol2ImKernel.cpp
@@ -44,14 +44,13 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
// Checks performed when output is configured
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), compute_col2im_shape(*input, convolved_dims));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output);
}
@@ -64,7 +63,7 @@
// Output auto inizialitation if not yet initialized
auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_col2im_shape(*input, convolved_dims)));
- const unsigned int num_elems_read_per_iteration = is_data_type_fixed_point(input->data_type()) ? 1 : 8;
+ const unsigned int num_elems_read_per_iteration = 8;
// Configure window
Window win = calculate_max_window(*input, Steps(num_elems_read_per_iteration));
@@ -106,7 +105,6 @@
build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(input->info()->element_size()));
build_opts.add_option("-DWIDTH_INPUT=" + support::cpp11::to_string(input->info()->dimension(0)));
build_opts.add_option("-DWIDTH_OUTPUT=" + support::cpp11::to_string(_convolved_dims.first));
- build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("col2im", build_opts.options()));
diff --git a/src/core/CL/kernels/CLConvertFullyConnectedWeightsKernel.cpp b/src/core/CL/kernels/CLConvertFullyConnectedWeightsKernel.cpp
index c3cd494..a39d1f4 100644
--- a/src/core/CL/kernels/CLConvertFullyConnectedWeightsKernel.cpp
+++ b/src/core/CL/kernels/CLConvertFullyConnectedWeightsKernel.cpp
@@ -75,7 +75,7 @@
DataLayout data_layout)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8, DataType::U16, DataType::S16, DataType::QS16, DataType::U32, DataType::S32,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8, DataType::U16, DataType::S16, DataType::U32, DataType::S32,
DataType::QS32, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
diff --git a/src/core/CL/kernels/CLDepthConcatenateLayerKernel.cpp b/src/core/CL/kernels/CLDepthConcatenateLayerKernel.cpp
index 204f9ae..72dc211 100644
--- a/src/core/CL/kernels/CLDepthConcatenateLayerKernel.cpp
+++ b/src/core/CL/kernels/CLDepthConcatenateLayerKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017, 2018 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -62,9 +62,8 @@
};
ARM_COMPUTE_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output);
ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) + depth_offset > output->info()->dimension(2));
ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) > output->info()->dimension(0));
ARM_COMPUTE_ERROR_ON(input->info()->dimension(1) > output->info()->dimension(1));
diff --git a/src/core/CL/kernels/CLDepthConvertLayerKernel.cpp b/src/core/CL/kernels/CLDepthConvertLayerKernel.cpp
index 83908a1..2f5b246 100644
--- a/src/core/CL/kernels/CLDepthConvertLayerKernel.cpp
+++ b/src/core/CL/kernels/CLDepthConvertLayerKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -40,21 +40,15 @@
void CLDepthConvertLayerKernel::configure(const ICLTensor *input, ICLTensor *output, ConvertPolicy policy, uint32_t shift)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::U8, DataType::S16, DataType::QS16,
- DataType::U16, DataType::U32, DataType::S32, DataType::F32);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::U8, DataType::S16, DataType::QS16,
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S16,
+ DataType::U16, DataType::U32, DataType::S32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16,
DataType::U16, DataType::U32, DataType::S32, DataType::F32);
ARM_COMPUTE_ERROR_ON(input == output);
ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == output->info()->data_type(), "Input and output data types must be different");
ARM_COMPUTE_ERROR_ON(shift >= 8);
// Check if convertion is supported
- ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == DataType::QS8 && output->info()->data_type() != DataType::F32,
- "Only data types supported [in] QS8 -> [out] F32");
- ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == DataType::QS16 && (output->info()->data_type() != DataType::F32),
- "Only data types supported [in] QS16 -> [out] F32");
- ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == DataType::F32 && ((output->info()->data_type() != DataType::QS8) && output->info()->data_type() != DataType::QS16),
- "Only data types supported [in] F32 -> [out] QS8, QS16");
ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == DataType::U8 && (output->info()->data_type() != DataType::U16 && output->info()->data_type() != DataType::S16
&& output->info()->data_type() != DataType::U32 && output->info()->data_type() != DataType::S32),
"Only data types supported [in] U8 -> [out] U16, S16, U32, S32");
@@ -99,10 +93,6 @@
}
build_opts.emplace("-DDATA_TYPE_IN=" + get_cl_type_from_data_type(input->info()->data_type()));
build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type()));
- if(is_data_type_fixed_point(input->info()->data_type()) || is_data_type_fixed_point(output->info()->data_type()))
- {
- build_opts.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
- }
// Create kernel
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));
diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp
index 1de08aa..9d9c280 100644
--- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp
@@ -146,7 +146,6 @@
output_shape,
1,
input->info()->data_type(),
- input->info()->fixed_point_position(),
input->info()->quantization_info());
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info, depth_multiplier, act_info));
diff --git a/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp b/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
index bef13f9..cab9436 100644
--- a/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
@@ -53,7 +53,6 @@
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input->data_type()) && has_bias);
ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(idx_c) * depth_multiplier) != output->dimension(2));
ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != (kernel_dims.width * kernel_dims.height + ((has_bias) ? 1 : 0)));
diff --git a/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp b/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp
index c97ecaf..e124ee4 100644
--- a/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseVectorToTensorKernel.cpp
@@ -61,7 +61,6 @@
TensorShape output_shape = compute_output_shape(input->tensor_shape(), conv_w, conv_h, output->data_layout());
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
diff --git a/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp b/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp
index fd3b754..c28be3f 100644
--- a/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseWeightsReshapeKernel.cpp
@@ -46,7 +46,6 @@
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input->data_type()) && (biases != nullptr));
ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_c) != output->dimension(1));
ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != (input->dimension(idx_w) * input->dimension(idx_h) + ((biases != nullptr) ? 1 : 0)));
@@ -54,7 +53,6 @@
if(biases != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != input->dimension(idx_c));
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
}
diff --git a/src/core/CL/kernels/CLDequantizationLayerKernel.cpp b/src/core/CL/kernels/CLDequantizationLayerKernel.cpp
index fa982d6..fba721f 100644
--- a/src/core/CL/kernels/CLDequantizationLayerKernel.cpp
+++ b/src/core/CL/kernels/CLDequantizationLayerKernel.cpp
@@ -54,7 +54,7 @@
std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *min_max)
{
// Output tensor auto initialization if not yet initialized
- auto_init_if_empty(*output, input->tensor_shape(), 1, DataType::F32, 0);
+ auto_init_if_empty(*output, input->tensor_shape(), 1, DataType::F32);
constexpr unsigned int num_elems_processed_per_iteration = 4;
diff --git a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
index d2794d7..dcb4ac1 100644
--- a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
+++ b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
@@ -45,7 +45,7 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(0) != weights->dimension(1),
"Weights should have same width as length");
@@ -84,7 +84,6 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(),
misc::shape_calculator::compute_deep_convolution_shape(*input, *weights, conv_info));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
@@ -103,7 +102,6 @@
auto_init_if_empty(*output, output_shape,
1,
input->data_type(),
- input->fixed_point_position(),
input->quantization_info());
unsigned int conv_stride_x = std::get<0>(conv_info.stride());
@@ -265,7 +263,6 @@
output_shape,
1,
input->info()->data_type(),
- input->info()->fixed_point_position(),
input->info()->quantization_info());
// Perform validation step
@@ -302,18 +299,14 @@
}
else
{
- bool is_quantized_fixed_point = is_data_type_fixed_point(data_type);
- bool is_quantized_asymm = is_data_type_quantized_asymmetric(data_type);
- DataType promoted_type = (is_quantized_fixed_point) ? get_promoted_data_type(data_type) : data_type;
+ bool is_quantized_asymm = is_data_type_quantized_asymmetric(data_type);
build_options.add_option_if(is_quantized_asymm, std::string("-DKERNEL_SIZE=" + support::cpp11::to_string(kernel_size)));
build_options.add_option(std::string("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)));
build_options.add_option(std::string("-DDATA_SIZE=" + get_data_size_from_data_type(data_type)));
build_options.add_option(std::string("-DWEIGHTS_DEPTH=" + support::cpp11::to_string(_weights->info()->dimension(2))));
build_options.add_option(std::string("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x)));
- build_options.add_option_if(is_quantized_fixed_point,
- std::string("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())));
- build_options.add_option(std::string("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(promoted_type)));
+ build_options.add_option(std::string("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(data_type)));
// Create kernel
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(is_quantized_asymm ? "direct_convolution_1x1_3x3_5x5_quantized" : kernel_name.str(),
diff --git a/src/core/CL/kernels/CLFillBorderKernel.cpp b/src/core/CL/kernels/CLFillBorderKernel.cpp
index 66504e6..3b1edaf 100644
--- a/src/core/CL/kernels/CLFillBorderKernel.cpp
+++ b/src/core/CL/kernels/CLFillBorderKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -91,10 +91,6 @@
build_opts.emplace(("-DBORDER_SIZE_BOTTOM=" + support::cpp11::to_string(border_size.bottom)));
build_opts.emplace(("-DBORDER_SIZE_LEFT=" + support::cpp11::to_string(border_size.left)));
build_opts.emplace(("-DBORDER_SIZE_RIGHT=" + support::cpp11::to_string(border_size.right)));
- if(is_data_type_fixed_point(tensor->info()->data_type()))
- {
- build_opts.emplace("-DFIXED_POINT_POSITION");
- }
// Create kernel
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));
@@ -125,14 +121,12 @@
case DataType::QASYMM8:
set_constant_border<uint8_t>(idx, constant_border_value);
break;
- case DataType::QS8:
case DataType::S8:
set_constant_border<int8_t>(idx, constant_border_value);
break;
case DataType::U16:
set_constant_border<uint16_t>(idx, constant_border_value);
break;
- case DataType::QS16:
case DataType::S16:
set_constant_border<int16_t>(idx, constant_border_value);
break;
diff --git a/src/core/CL/kernels/CLFloorKernel.cpp b/src/core/CL/kernels/CLFloorKernel.cpp
index 11f8e33..f6b0e82 100644
--- a/src/core/CL/kernels/CLFloorKernel.cpp
+++ b/src/core/CL/kernels/CLFloorKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -45,7 +45,7 @@
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
// Auto initialize output
- auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
+ auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type());
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
diff --git a/src/core/CL/kernels/CLGEMMInterleave4x4Kernel.cpp b/src/core/CL/kernels/CLGEMMInterleave4x4Kernel.cpp
index ba475f5..12a40cd 100644
--- a/src/core/CL/kernels/CLGEMMInterleave4x4Kernel.cpp
+++ b/src/core/CL/kernels/CLGEMMInterleave4x4Kernel.cpp
@@ -44,15 +44,14 @@
{
ARM_COMPUTE_RETURN_ERROR_ON(mult_interleave4x4_height < 1);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::U8, DataType::S8,
- DataType::QS16, DataType::U16, DataType::S16, DataType::U32, DataType::S32,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::U8, DataType::S8,
+ DataType::U16, DataType::S16, DataType::U32, DataType::S32,
DataType::F16, DataType::F32);
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), compute_interleaved_shape(*input, mult_interleave4x4_height));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
diff --git a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp
index 3f705ac..e040122 100644
--- a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.cpp
@@ -172,7 +172,7 @@
tensor_shape.set(0, is_interleaved_transposed ? reshape_info.n() : input1->info()->dimension(0));
tensor_shape.set(1, is_interleaved_transposed ? reshape_info.m() : input0->info()->dimension(1));
- auto_init_if_empty(*output->info(), tensor_shape, 1, DataType::S32, 1, QuantizationInfo());
+ auto_init_if_empty(*output->info(), tensor_shape, 1, DataType::S32, QuantizationInfo());
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info));
diff --git a/src/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.cpp
index 81e455f..04cf627 100644
--- a/src/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.cpp
@@ -41,9 +41,8 @@
Status validate_arguments(const ITensorInfo *accum, const ITensorInfo *biases)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(accum);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(biases, accum);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(biases, accum);
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() != 1);
return Status{};
@@ -95,8 +94,6 @@
CLBuildOptions build_opts;
build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(accum->info()->data_type()));
build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
- build_opts.add_option_if(is_data_type_fixed_point(accum->info()->data_type()),
- "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(accum->info()->fixed_point_position()));
// Create kernel
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemm_accumulate_biases", build_opts.options()));
diff --git a/src/core/CL/kernels/CLGEMMMatrixAdditionKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixAdditionKernel.cpp
index c50ee24..bcc3a01 100644
--- a/src/core/CL/kernels/CLGEMMMatrixAdditionKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMMatrixAdditionKernel.cpp
@@ -29,7 +29,6 @@
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/CL/OpenCL.h"
#include "arm_compute/core/Error.h"
-#include "arm_compute/core/FixedPoint.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Window.h"
@@ -64,7 +63,7 @@
ARM_COMPUTE_UNUSED(input, output, beta);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
@@ -88,19 +87,7 @@
_output = output;
std::ostringstream ma_arguments;
- if(is_data_type_fixed_point(input->info()->data_type()))
- {
- ma_arguments << "-DBETA=" << (input->info()->data_type() == DataType::QS8 ?
- sqcvt_qs8_f32(beta, input->info()->fixed_point_position()) :
- sqcvt_qs16_f32(beta, input->info()->fixed_point_position()))
- << " ";
- ma_arguments << "-DFIXED_POINT_POSITION=" << input->info()->fixed_point_position();
- }
- else
- {
- ma_arguments << "-DBETA=" << beta;
- }
-
+ ma_arguments << "-DBETA=" << beta;
std::set<std::string> build_opts;
build_opts.emplace(ma_arguments.str());
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
index 2c2a92d..814cbb6 100644
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
@@ -31,7 +31,6 @@
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/CL/OpenCL.h"
#include "arm_compute/core/Error.h"
-#include "arm_compute/core/FixedPoint.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
@@ -53,10 +52,8 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input0);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_data_type_fixed_point(input0->data_type()) && (reshape_info.depth_output_gemm3d() != 1), "GEMM3D only supports floating point data types");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the matrix B must be <= 3");
@@ -95,7 +92,6 @@
const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_info));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, output);
}
return Status{};
@@ -219,7 +215,6 @@
_slide_matrix_b = _input1->info()->num_dimensions() >= _input0->info()->num_dimensions();
const DataType data_type = input0->info()->data_type();
- const int fp_pos = input0->info()->fixed_point_position();
// Get target architecture
GPUTarget gpu_target = get_target();
@@ -236,14 +231,11 @@
// Create build options
CLBuildOptions build_opts;
- build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(fp_pos));
// Only define ALPHA when alpha is not 1.0f. This avoids performing unnecessary multiplications.
if(std::abs(1.0f - alpha) > 0.00001f)
{
- build_opts.add_option_if_else(is_data_type_fixed_point(data_type),
- "-DALPHA=" + support::cpp11::to_string((data_type == DataType::QS8 ? sqcvt_qs8_f32(alpha, fp_pos) : sqcvt_qs16_f32(alpha, fp_pos))),
- "-DALPHA=" + float_to_string_with_full_precision(alpha));
+ build_opts.add_option("-DALPHA=" + float_to_string_with_full_precision(alpha));
}
build_opts.add_option_if(_is_gemm3d, "-DREINTERPRET_OUTPUT_AS_3D");
build_opts.add_option_if(_is_gemm3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1)));
@@ -299,10 +291,6 @@
// via exhaustive autotuning over a range of representative layer configurations.
_lws_hint = cl::NDRange(4);
}
- else if(is_data_type_fixed_point(data_type))
- {
- kernel_name = "gemm_mm_" + lower_string(string_from_data_type(data_type));
- }
else // (MIDGARD and F32) or (F16)
{
kernel_name = "gemm_mm_floating_point";
diff --git a/src/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.cpp
index d8ecd50..43a6cf2 100644
--- a/src/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.cpp
@@ -42,7 +42,6 @@
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input0);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output);
ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input0->data_type()) && (output->data_type() != DataType::S32));
ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(2) != input1->dimension(1));
diff --git a/src/core/CL/kernels/CLGEMMTranspose1xWKernel.cpp b/src/core/CL/kernels/CLGEMMTranspose1xWKernel.cpp
index 7a8a1e5..7e44fa7 100644
--- a/src/core/CL/kernels/CLGEMMTranspose1xWKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMTranspose1xWKernel.cpp
@@ -47,8 +47,8 @@
{
ARM_COMPUTE_RETURN_ERROR_ON(mult_transpose1xW_width < 1);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::U8, DataType::S8,
- DataType::QS16, DataType::U16, DataType::S16, DataType::U32, DataType::S32,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::U8, DataType::S8,
+ DataType::U16, DataType::S16, DataType::U32, DataType::S32,
DataType::F16, DataType::F32);
if(output->total_size() != 0)
@@ -56,7 +56,6 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(),
compute_transpose1xW_with_element_size_shape(*input, mult_transpose1xW_width));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
diff --git a/src/core/CL/kernels/CLIm2ColKernel.cpp b/src/core/CL/kernels/CLIm2ColKernel.cpp
index 5d4e039..b54575a 100644
--- a/src/core/CL/kernels/CLIm2ColKernel.cpp
+++ b/src/core/CL/kernels/CLIm2ColKernel.cpp
@@ -48,7 +48,7 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, bool has_bias, const Size2D &dilation)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::QASYMM8 && has_bias);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1));
@@ -58,7 +58,6 @@
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
@@ -136,7 +135,7 @@
if(dilation == Size2D(1U, 1U))
{
- if(squared_im2col && !is_data_type_fixed_point(data_type))
+ if(squared_im2col)
{
// Check if we can run an optimized im2col
switch(kernel_dims.width)
@@ -304,7 +303,6 @@
build_opts.add_option(("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)));
build_opts.add_option("-DELEMENT_SIZE=" + support::cpp11::to_string(input->info()->element_size()));
build_opts.add_option_if(has_bias, "-DHAS_BIAS");
- build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
_num_elems_processed_per_iteration = 1;
diff --git a/src/core/CL/kernels/CLL2NormalizeLayerKernel.cpp b/src/core/CL/kernels/CLL2NormalizeLayerKernel.cpp
index 3d30350..39d9f95 100644
--- a/src/core/CL/kernels/CLL2NormalizeLayerKernel.cpp
+++ b/src/core/CL/kernels/CLL2NormalizeLayerKernel.cpp
@@ -26,7 +26,6 @@
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/FixedPoint.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Utils.h"
@@ -78,7 +77,7 @@
Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
// Output tensor auto initialization if not yet initialized
- auto_init_if_empty(*output, input->tensor_shape(), 1, input->data_type(), input->fixed_point_position());
+ auto_init_if_empty(*output, input->tensor_shape(), 1, input->data_type());
AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
diff --git a/src/core/CL/kernels/CLMinMaxLayerKernel.cpp b/src/core/CL/kernels/CLMinMaxLayerKernel.cpp
index 60dd5e7..9493ddc 100644
--- a/src/core/CL/kernels/CLMinMaxLayerKernel.cpp
+++ b/src/core/CL/kernels/CLMinMaxLayerKernel.cpp
@@ -62,7 +62,7 @@
TensorShape output_shape = compute_min_max_shape(input);
// Output auto initialization if not yet initialized
- auto_init_if_empty(*output, output_shape, 1, input->data_type(), input->fixed_point_position());
+ auto_init_if_empty(*output, output_shape, 1, input->data_type());
const unsigned int num_elems_processed_per_iteration = 1;
diff --git a/src/core/CL/kernels/CLNormalizationLayerKernel.cpp b/src/core/CL/kernels/CLNormalizationLayerKernel.cpp
index 5456876..df01eab 100644
--- a/src/core/CL/kernels/CLNormalizationLayerKernel.cpp
+++ b/src/core/CL/kernels/CLNormalizationLayerKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -27,7 +27,6 @@
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/CLValidate.h"
#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/FixedPoint.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Utils.h"
@@ -40,24 +39,16 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, NormalizationLayerInfo norm_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(norm_info.norm_size() % 2), "Normalization size should be odd");
- if(is_data_type_fixed_point(input->data_type()))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.beta(), input);
- ARM_COMPUTE_RETURN_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.kappa(), input);
- ARM_COMPUTE_RETURN_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.scale_coeff(), input);
- }
-
// Checks performed when output is configured
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
@@ -74,7 +65,7 @@
const unsigned int border_width = is_in_map ? std::min(norm_size / 2, 3U) : 0;
const BorderSize border_size = BorderSize(0, border_width);
- const unsigned int num_elems_processed_per_iteration = (is_data_type_fixed_point(input->data_type())) ? 16 : 4;
+ const unsigned int num_elems_processed_per_iteration = 4;
const unsigned int num_elems_read_per_iteration = is_in_map ? (num_elems_processed_per_iteration + 2 * (norm_size / 2)) : num_elems_processed_per_iteration;
Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
@@ -119,14 +110,12 @@
const unsigned int border_width = _is_in_map ? std::min(norm_info.norm_size() / 2, 3U) : 0;
_border_size = BorderSize(0, border_width);
- const unsigned int num_elems_processed_per_iteration = (is_data_type_fixed_point(input->info()->data_type())) ? 16 : 4;
+ const unsigned int num_elems_processed_per_iteration = 4;
const bool is_in_map_2D = (norm_info.type() == NormType::IN_MAP_2D);
// Set build options
CLBuildOptions build_opts;
build_opts.add_option(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
- build_opts.add_option_if(is_data_type_fixed_point(input->info()->data_type()),
- "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
build_opts.add_option(("-DCOEFF=" + float_to_string_with_full_precision(norm_info.scale_coeff())));
build_opts.add_option(("-DBETA=" + float_to_string_with_full_precision(norm_info.beta())));
build_opts.add_option(("-DKAPPA=" + float_to_string_with_full_precision(norm_info.kappa())));
diff --git a/src/core/CL/kernels/CLPermuteKernel.cpp b/src/core/CL/kernels/CLPermuteKernel.cpp
index 168ab81..7c0c95b 100644
--- a/src/core/CL/kernels/CLPermuteKernel.cpp
+++ b/src/core/CL/kernels/CLPermuteKernel.cpp
@@ -52,8 +52,8 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PermutationVector &perm)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8,
- DataType::U16, DataType::S16, DataType::QS16,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8,
+ DataType::U16, DataType::S16,
DataType::U32, DataType::S32,
DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG((perm != PermutationVector{ 2U, 0U, 1U })
@@ -68,7 +68,6 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
}
diff --git a/src/core/CL/kernels/CLPixelWiseMultiplicationKernel.cpp b/src/core/CL/kernels/CLPixelWiseMultiplicationKernel.cpp
index a9df36d..4ea093f 100644
--- a/src/core/CL/kernels/CLPixelWiseMultiplicationKernel.cpp
+++ b/src/core/CL/kernels/CLPixelWiseMultiplicationKernel.cpp
@@ -51,36 +51,23 @@
ARM_COMPUTE_UNUSED(rounding_policy);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input1);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input2);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(scale < 0, "Scale cannot be negative.");
const TensorShape &out_shape = TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape());
ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input1, input2);
-
- if(is_data_type_fixed_point(input1->data_type()))
- {
- // All data types must be all QS8 or all QS16
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2);
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(scale != 1, "Unsupported scaling factor for QS8/QS16. Scale must be 1.");
- }
// Validate in case of configured output
if(output->total_size() > 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(output);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() == DataType::U8 && (input1->data_type() != DataType::U8 || input2->data_type() != DataType::U8),
"Output can only be U8 if both inputs are U8");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), "Wrong shape for output");
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input1, output);
- if(is_data_type_fixed_point(input1->data_type()))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output);
- }
}
return Status{};
@@ -174,14 +161,6 @@
{
compute_type = "int";
}
- else if(input1->info()->data_type() == DataType::QS8)
- {
- compute_type = "qs8";
- }
- else if(input1->info()->data_type() == DataType::QS16)
- {
- compute_type = "qs16";
- }
else
{
compute_type = "ushort";
@@ -197,10 +176,6 @@
std::set<std::string> build_opts;
build_opts.emplace((overflow_policy == ConvertPolicy::WRAP || is_data_type_float(output->info()->data_type())) ? "-DWRAP" : "-DSATURATE");
build_opts.emplace((rounding_policy == RoundingPolicy::TO_ZERO) ? "-DROUND=_rtz" : "-DROUND=_rte");
- if(is_data_type_fixed_point(input1->info()->data_type()))
- {
- build_opts.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input1->info()->fixed_point_position()));
- }
build_opts.emplace("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type()));
build_opts.emplace("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type()));
build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type()));
diff --git a/src/core/CL/kernels/CLPoolingLayerKernel.cpp b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
index 81c52ed..246ab68 100644
--- a/src/core/CL/kernels/CLPoolingLayerKernel.cpp
+++ b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
@@ -62,7 +62,7 @@
switch(data_layout)
{
case DataLayout::NCHW:
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
break;
case DataLayout::NHWC:
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
@@ -78,8 +78,7 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
- TensorInfo out_info(TensorInfo(compute_pool_shape(*input, pool_info), 1, output->data_type(), output->fixed_point_position()));
+ TensorInfo out_info(TensorInfo(compute_pool_shape(*input, pool_info), 1, output->data_type()));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &out_info);
}
@@ -214,8 +213,6 @@
CLBuildOptions build_opts;
build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
build_opts.add_option("-DPOOL_" + string_from_pooling_type(pool_type));
- build_opts.add_option_if(is_data_type_fixed_point(data_type),
- "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
build_opts.add_option("-DSTRIDE_X=" + support::cpp11::to_string(pool_stride_x));
build_opts.add_option("-DSTRIDE_Y=" + support::cpp11::to_string(pool_stride_y));
build_opts.add_option("-DPAD_X=" + support::cpp11::to_string(pool_pad_left));
@@ -240,7 +237,7 @@
{
// Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenCL kernel where
// each thread computes 4 output elements
- const bool is_pool3x3_stride_le3 = (pool_size_x == 3) && (pool_size_y == 3) && (pool_stride_x <= 3) && !is_data_type_fixed_point(data_type);
+ const bool is_pool3x3_stride_le3 = (pool_size_x == 3) && (pool_size_y == 3) && (pool_stride_x <= 3);
std::string kernel_name = ((is_pool3x3_stride_le3) ? "pooling_layer_optimized_" : "pooling_layer_")
+ support::cpp11::to_string(pool_size_x);
diff --git a/src/core/CL/kernels/CLQuantizationLayerKernel.cpp b/src/core/CL/kernels/CLQuantizationLayerKernel.cpp
index 028e508..af751f4 100644
--- a/src/core/CL/kernels/CLQuantizationLayerKernel.cpp
+++ b/src/core/CL/kernels/CLQuantizationLayerKernel.cpp
@@ -54,7 +54,7 @@
std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *min_max)
{
// Output tensor auto initialization if not yet initialized
- auto_init_if_empty(*output, input->tensor_shape(), 1, DataType::U8, 0);
+ auto_init_if_empty(*output, input->tensor_shape(), 1, DataType::U8);
constexpr unsigned int num_elems_processed_per_iteration = 4;
diff --git a/src/core/CL/kernels/CLROIPoolingLayerKernel.cpp b/src/core/CL/kernels/CLROIPoolingLayerKernel.cpp
index 51873ff..4048e92 100644
--- a/src/core/CL/kernels/CLROIPoolingLayerKernel.cpp
+++ b/src/core/CL/kernels/CLROIPoolingLayerKernel.cpp
@@ -56,7 +56,7 @@
// Output auto inizialitation if not yet initialized
TensorShape output_shape(pool_info.pooled_width(), pool_info.pooled_height(), input->info()->dimension(2), rois->num_values());
- auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+ auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type());
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pool_info.pooled_width()) || (output->info()->dimension(1) != pool_info.pooled_height()));
diff --git a/src/core/CL/kernels/CLReductionOperationKernel.cpp b/src/core/CL/kernels/CLReductionOperationKernel.cpp
index c44fced..d64f0d8 100644
--- a/src/core/CL/kernels/CLReductionOperationKernel.cpp
+++ b/src/core/CL/kernels/CLReductionOperationKernel.cpp
@@ -27,7 +27,6 @@
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/ICLTensor.h"
-#include "arm_compute/core/FixedPoint.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Utils.h"
@@ -65,7 +64,7 @@
// Output tensor auto initialization if not yet initialized
TensorShape output_shape{ input->tensor_shape() };
output_shape.set(axis, 1);
- auto_init_if_empty(*output, output_shape, 1, input->data_type(), input->fixed_point_position());
+ auto_init_if_empty(*output, output_shape, 1, input->data_type());
const unsigned int num_elems_processed_per_iteration = 16;
@@ -118,10 +117,6 @@
std::set<std::string> build_opts;
build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
build_opts.emplace(("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)));
- if(is_data_type_fixed_point(input->info()->data_type()))
- {
- build_opts.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
- }
switch(op)
{
diff --git a/src/core/CL/kernels/CLReshapeLayerKernel.cpp b/src/core/CL/kernels/CLReshapeLayerKernel.cpp
index 15897c9..ce9d7ff 100644
--- a/src/core/CL/kernels/CLReshapeLayerKernel.cpp
+++ b/src/core/CL/kernels/CLReshapeLayerKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -47,12 +47,11 @@
void CLReshapeLayerKernel::configure(const ICLTensor *input, ICLTensor *output)
{
ARM_COMPUTE_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8,
- DataType::U16, DataType::S16, DataType::QS16,
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8,
+ DataType::U16, DataType::S16,
DataType::U32, DataType::S32, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_NULLPTR(output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output);
ARM_COMPUTE_ERROR_ON(input->info()->tensor_shape().total_size() != output->info()->tensor_shape().total_size());
diff --git a/src/core/CL/kernels/CLSoftmaxLayerKernel.cpp b/src/core/CL/kernels/CLSoftmaxLayerKernel.cpp
index 6a18e5f..b9ebdc9 100644
--- a/src/core/CL/kernels/CLSoftmaxLayerKernel.cpp
+++ b/src/core/CL/kernels/CLSoftmaxLayerKernel.cpp
@@ -82,11 +82,10 @@
Status validate_arguments_1DMaxShiftExpSum(const ITensorInfo *input, const ITensorInfo *max, const ITensorInfo *output, const ITensorInfo *sum)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(max, sum, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, max);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, max);
const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(input->data_type());
@@ -102,7 +101,6 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
}
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output);
}
// Checks performed when sum is configured
@@ -117,7 +115,6 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(max, sum);
}
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(max, sum);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(max, sum);
}
return Status{};
@@ -126,10 +123,9 @@
Status validate_arguments_1DNorm(const ITensorInfo *input, const ITensorInfo *sum, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::S32, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(sum, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, sum);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, sum);
// Note: output should always have a scale of 1/256 and offset 0
const QuantizationInfo allowed_quantization_info = QuantizationInfo(1.f / 256, 0);
@@ -139,7 +135,6 @@
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output);
if(!is_quantized_asymmetric)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
@@ -239,15 +234,11 @@
const DataType dt = input->info()->data_type();
const size_t reduction_dim_size = input->info()->dimension(0);
- auto beta_int = static_cast<int>(lround(beta * (1 << input->info()->fixed_point_position())));
// Set build options
CLBuildOptions build_opts;
build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(dt));
- build_opts.add_option_if(is_data_type_fixed_point(dt),
- "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
build_opts.add_option_if(dt == DataType::F16, "-DUSE_F16");
- build_opts.add_option_if(is_data_type_fixed_point(dt) && (beta != 1.0f), "-DBETA=" + support::cpp11::to_string(beta_int));
build_opts.add_option_if(is_data_type_float(dt) && (beta != 1.0f), "-DBETA=" + float_to_string_with_full_precision(beta));
build_opts.add_options_if(is_data_type_quantized_asymmetric(dt), prepare_quantized_softmax_build_options(input->info()->quantization_info().scale, beta).options());
@@ -364,8 +355,6 @@
// Set build options
CLBuildOptions build_opts;
build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
- build_opts.add_option_if(is_data_type_fixed_point(input->info()->data_type()),
- "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
build_opts.add_options_if(is_quantized_asymmetric,
prepare_quantized_softmax_build_options(input->info()->quantization_info().scale, beta).options());
diff --git a/src/core/CL/kernels/CLTransposeKernel.cpp b/src/core/CL/kernels/CLTransposeKernel.cpp
index 8260606..3d58434 100644
--- a/src/core/CL/kernels/CLTransposeKernel.cpp
+++ b/src/core/CL/kernels/CLTransposeKernel.cpp
@@ -57,8 +57,8 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8,
- DataType::U16, DataType::S16, DataType::QS16,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8,
+ DataType::U16, DataType::S16,
DataType::U32, DataType::S32,
DataType::F16, DataType::F32);
@@ -68,7 +68,6 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
diff --git a/src/core/CL/kernels/CLWeightsReshapeKernel.cpp b/src/core/CL/kernels/CLWeightsReshapeKernel.cpp
index b012d58..5243c40 100644
--- a/src/core/CL/kernels/CLWeightsReshapeKernel.cpp
+++ b/src/core/CL/kernels/CLWeightsReshapeKernel.cpp
@@ -42,13 +42,12 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
if(biases != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input->data_type()));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
ARM_COMPUTE_RETURN_ERROR_ON((input->num_dimensions() == 4) && (biases->num_dimensions() != 1));
ARM_COMPUTE_RETURN_ERROR_ON((input->num_dimensions() == 5) && (biases->num_dimensions() != 2));
ARM_COMPUTE_RETURN_ERROR_ON((input->num_dimensions() == 4) && (biases->dimension(0) != input->tensor_shape()[3]));
@@ -60,7 +59,6 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), compute_weights_reshaped_shape(*input, biases != nullptr));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output);
}
@@ -96,7 +94,6 @@
CLBuildOptions build_opts;
build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type));
build_opts.add_option_if(biases != nullptr, "-DHAS_BIAS");
- build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()));
// Create kernel
std::string kernel_name = std::string("reshape_to_columns_") + lower_string(string_from_data_layout(data_layout));
diff --git a/src/core/CL/kernels/CLWidthConcatenateLayerKernel.cpp b/src/core/CL/kernels/CLWidthConcatenateLayerKernel.cpp
index 56d6ec8..587ba69 100644
--- a/src/core/CL/kernels/CLWidthConcatenateLayerKernel.cpp
+++ b/src/core/CL/kernels/CLWidthConcatenateLayerKernel.cpp
@@ -60,10 +60,9 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8, DataType::U16, DataType::S16, DataType::QS16, DataType::F16, DataType::U32,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8, DataType::U16, DataType::S16, DataType::F16, DataType::U32,
DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output);
ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) + width_offset > output->dimension(0));
for(size_t i = 1; i < Coordinates::num_max_dimensions; ++i)
diff --git a/src/core/CPP/kernels/CPPPermuteKernel.cpp b/src/core/CPP/kernels/CPPPermuteKernel.cpp
index 5c93f3e..17eaec2 100644
--- a/src/core/CPP/kernels/CPPPermuteKernel.cpp
+++ b/src/core/CPP/kernels/CPPPermuteKernel.cpp
@@ -40,8 +40,8 @@
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PermutationVector &perm)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8,
- DataType::U16, DataType::S16, DataType::QS16,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8,
+ DataType::U16, DataType::S16,
DataType::U32, DataType::S32,
DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(perm.num_dimensions() > 4, "Only up to 4D permutation vectors are supported");
@@ -53,7 +53,6 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
diff --git a/src/core/GLES_COMPUTE/kernels/GCActivationLayerKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCActivationLayerKernel.cpp
index 8287823..874c336 100644
--- a/src/core/GLES_COMPUTE/kernels/GCActivationLayerKernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCActivationLayerKernel.cpp
@@ -55,11 +55,10 @@
if(output != nullptr)
{
// Output auto inizialitation if not yet initialized
- auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
+ auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type());
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
_output = output;
}
diff --git a/src/core/GLES_COMPUTE/kernels/GCBatchNormalizationLayerKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCBatchNormalizationLayerKernel.cpp
index 9a592df..c745f3f 100644
--- a/src/core/GLES_COMPUTE/kernels/GCBatchNormalizationLayerKernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCBatchNormalizationLayerKernel.cpp
@@ -48,27 +48,23 @@
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, mean, var);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, mean, var);
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(mean, var);
if(output->total_size() != 0)
{
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
if(beta != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, beta);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, beta);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, beta);
}
if(gamma != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, gamma);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, gamma);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, gamma);
}
if(act_info.enabled())
{
@@ -86,7 +82,7 @@
ITensorInfo *beta, ITensorInfo *gamma)
{
// Output tensor auto initialization if not yet initialized
- auto_init_if_empty(*output, input->tensor_shape(), 1, input->data_type(), input->fixed_point_position());
+ auto_init_if_empty(*output, input->tensor_shape(), 1, input->data_type());
unsigned int num_elems_processed_per_iteration = 1;
if(input->data_type() == DataType::F16)
diff --git a/src/core/GLES_COMPUTE/kernels/GCDepthwiseConvolutionLayer3x3Kernel.cpp b/src/core/GLES_COMPUTE/kernels/GCDepthwiseConvolutionLayer3x3Kernel.cpp
index c237409..a0d1876 100644
--- a/src/core/GLES_COMPUTE/kernels/GCDepthwiseConvolutionLayer3x3Kernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCDepthwiseConvolutionLayer3x3Kernel.cpp
@@ -69,8 +69,7 @@
auto_init_if_empty(*output->info(),
output_shape,
1,
- input->info()->data_type(),
- input->info()->fixed_point_position());
+ input->info()->data_type());
ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
ARM_COMPUTE_ERROR_ON(output->info()->dimension(2) != weights->info()->dimension(2));
diff --git a/src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp
index 67a1530..ecff233 100644
--- a/src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCDirectConvolutionLayerKernel.cpp
@@ -80,12 +80,11 @@
output_shape.set(2, weights->info()->dimension(3));
// Output auto inizialitation if not yet initialized
- auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+ auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type());
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
ARM_COMPUTE_ERROR_ON(!conv_info.padding_is_symmetric());
_conv_stride_x = std::get<0>(conv_info.stride());
diff --git a/src/core/GLES_COMPUTE/kernels/GCGEMMInterleave4x4Kernel.cpp b/src/core/GLES_COMPUTE/kernels/GCGEMMInterleave4x4Kernel.cpp
index 171fbad..efd5747 100644
--- a/src/core/GLES_COMPUTE/kernels/GCGEMMInterleave4x4Kernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCGEMMInterleave4x4Kernel.cpp
@@ -51,7 +51,7 @@
output_shape.set(1, std::ceil(input->info()->dimension(1) / 4.0f));
// Output auto inizialitation if not yet initialized
- auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+ auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type());
ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
diff --git a/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp
index d576c30..8ead05a 100644
--- a/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp
@@ -97,7 +97,6 @@
ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != static_cast<size_t>(n));
ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != static_cast<size_t>(m));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, output);
}
}
diff --git a/src/core/GLES_COMPUTE/kernels/GCGEMMTranspose1xWKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCGEMMTranspose1xWKernel.cpp
index 5d9f9c2..dfbd021 100644
--- a/src/core/GLES_COMPUTE/kernels/GCGEMMTranspose1xWKernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCGEMMTranspose1xWKernel.cpp
@@ -49,7 +49,7 @@
output_shape.set(1, static_cast<size_t>(std::ceil((input->info()->dimension(0) / static_cast<float>(transpose_w)))));
// Output tensor auto inizialitation if not yet initialized
- auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+ auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type());
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
diff --git a/src/core/GLES_COMPUTE/kernels/GCIm2ColKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCIm2ColKernel.cpp
index 6c89616..2197190 100644
--- a/src/core/GLES_COMPUTE/kernels/GCIm2ColKernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCIm2ColKernel.cpp
@@ -53,7 +53,6 @@
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
@@ -253,7 +252,7 @@
if(_input->info()->data_type() == DataType::F16)
{
(dynamic_cast<TensorInfo *>(_input->info()))->init(_input->info()->tensor_shape(), _input->info()->num_channels(), _input->info()->data_type(), _input->info()->strides_in_bytes(), 0,
- _input->info()->total_size(), _input->info()->fixed_point_position());
+ _input->info()->total_size());
}
_kernel.use();
diff --git a/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp
index 3a0944c..f225ebd 100644
--- a/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCPoolingLayerKernel.cpp
@@ -75,7 +75,6 @@
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
unsigned int pooled_w = 0;
unsigned int pooled_h = 0;
@@ -118,8 +117,7 @@
auto_init(input, output, pooled_w, pooled_h);
- BorderSize border_size = BorderSize(pool_pad_y, pool_pad_x);
- const DataType data_type = input->data_type();
+ BorderSize border_size = BorderSize(pool_pad_y, pool_pad_x);
const int input_width = input->dimension(0);
const int input_height = input->dimension(1);
@@ -131,7 +129,7 @@
{
// Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenGLES kernel where
// each thread computes 4 output elements
- const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3) && !is_data_type_fixed_point(data_type);
+ const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3);
int num_elems_read_per_iteration = pool_size;
@@ -261,8 +259,6 @@
_output = output;
_pool_info = pool_info;
- const DataType data_type = input->info()->data_type();
-
// Set build options
std::set<std::string> build_opts;
build_opts.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(1));
@@ -293,7 +289,7 @@
{
// Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenGLES kernel where
// each thread computes 4 output elements
- const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3) && !is_data_type_fixed_point(data_type);
+ const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3);
std::string kernel_name = "pooling_layer_" + support::cpp11::to_string(pool_size);
if(is_pool3x3_stride_le3)
diff --git a/src/core/GLES_COMPUTE/kernels/GCSoftmaxLayerKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCSoftmaxLayerKernel.cpp
index 040a663..7ae2fc9 100644
--- a/src/core/GLES_COMPUTE/kernels/GCSoftmaxLayerKernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCSoftmaxLayerKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -49,7 +49,7 @@
output_shape.set(0, 1);
// Output auto initialization if not yet initialized
- auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+ auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type());
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
@@ -110,8 +110,8 @@
ARM_COMPUTE_ERROR_ON_NULLPTR(max, sum, output);
// Output auto initialization if not yet initialized
- auto_init_if_empty(*sum->info(), max->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
- auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
+ auto_init_if_empty(*sum->info(), max->info()->tensor_shape(), 1, input->info()->data_type());
+ auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type());
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, max, sum);
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
@@ -204,10 +204,9 @@
ARM_COMPUTE_ERROR_ON_NULLPTR(sum, output);
// Output auto initialization if not yet initialized
- auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
+ auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type());
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, sum, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, sum, output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
_input = input;
diff --git a/src/core/GLES_COMPUTE/kernels/GCTransposeKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCTransposeKernel.cpp
index bda08e4..7248891 100644
--- a/src/core/GLES_COMPUTE/kernels/GCTransposeKernel.cpp
+++ b/src/core/GLES_COMPUTE/kernels/GCTransposeKernel.cpp
@@ -49,7 +49,7 @@
output_shape.set(1, h_out);
// Output tensor auto inizialitation if not yet initialized
- auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+ auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type());
ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
diff --git a/src/core/NEON/kernels/NEActivationLayerKernel.cpp b/src/core/NEON/kernels/NEActivationLayerKernel.cpp
index ec12515..bdc93ed 100644
--- a/src/core/NEON/kernels/NEActivationLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEActivationLayerKernel.cpp
@@ -23,7 +23,6 @@
*/
#include "arm_compute/core/NEON/kernels/NEActivationLayerKernel.h"
-#include "arm_compute/core/FixedPoint.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/NEAsymm.h"
@@ -46,14 +45,13 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, DataType::F16, DataType::F32);
// Checks performed when output is configured
if((output != nullptr) && (output->total_size() != 0))
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
@@ -146,36 +144,6 @@
};
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC*/
- // Activation functions : QS8
- static std::map<ActivationFunction, ActivationFunctionExecutorPtr> act_map_qs8 =
- {
- { ActivationFunction::ABS, &NEActivationLayerKernel::activation<ActivationFunction::ABS, qint8_t> },
- { ActivationFunction::LINEAR, &NEActivationLayerKernel::activation<ActivationFunction::LINEAR, qint8_t> },
- { ActivationFunction::LOGISTIC, &NEActivationLayerKernel::activation<ActivationFunction::LOGISTIC, qint8_t> },
- { ActivationFunction::RELU, &NEActivationLayerKernel::activation<ActivationFunction::RELU, qint8_t> },
- { ActivationFunction::BOUNDED_RELU, &NEActivationLayerKernel::activation<ActivationFunction::BOUNDED_RELU, qint8_t> },
- { ActivationFunction::LU_BOUNDED_RELU, &NEActivationLayerKernel::activation<ActivationFunction::LU_BOUNDED_RELU, qint8_t> },
- { ActivationFunction::LEAKY_RELU, &NEActivationLayerKernel::activation<ActivationFunction::LEAKY_RELU, qint8_t> },
- { ActivationFunction::SOFT_RELU, &NEActivationLayerKernel::activation<ActivationFunction::SOFT_RELU, qint8_t> },
- { ActivationFunction::SQRT, &NEActivationLayerKernel::activation<ActivationFunction::SQRT, qint8_t> },
- { ActivationFunction::SQUARE, &NEActivationLayerKernel::activation<ActivationFunction::SQUARE, qint8_t> },
- { ActivationFunction::TANH, &NEActivationLayerKernel::activation<ActivationFunction::TANH, qint8_t> },
- };
- // Activation functions : QS16
- static std::map<ActivationFunction, ActivationFunctionExecutorPtr> act_map_qs16 =
- {
- { ActivationFunction::ABS, &NEActivationLayerKernel::activation<ActivationFunction::ABS, qint16_t> },
- { ActivationFunction::LINEAR, &NEActivationLayerKernel::activation<ActivationFunction::LINEAR, qint16_t> },
- { ActivationFunction::LOGISTIC, &NEActivationLayerKernel::activation<ActivationFunction::LOGISTIC, qint16_t> },
- { ActivationFunction::RELU, &NEActivationLayerKernel::activation<ActivationFunction::RELU, qint16_t> },
- { ActivationFunction::BOUNDED_RELU, &NEActivationLayerKernel::activation<ActivationFunction::BOUNDED_RELU, qint16_t> },
- { ActivationFunction::LU_BOUNDED_RELU, &NEActivationLayerKernel::activation<ActivationFunction::LU_BOUNDED_RELU, qint16_t> },
- { ActivationFunction::LEAKY_RELU, &NEActivationLayerKernel::activation<ActivationFunction::LEAKY_RELU, qint16_t> },
- { ActivationFunction::SOFT_RELU, &NEActivationLayerKernel::activation<ActivationFunction::SOFT_RELU, qint16_t> },
- { ActivationFunction::SQRT, &NEActivationLayerKernel::activation<ActivationFunction::SQRT, qint16_t> },
- { ActivationFunction::SQUARE, &NEActivationLayerKernel::activation<ActivationFunction::SQUARE, qint16_t> },
- { ActivationFunction::TANH, &NEActivationLayerKernel::activation<ActivationFunction::TANH, qint16_t> },
- };
// Activation functions : QASYMM8
static std::map<ActivationFunction, ActivationFunctionExecutorPtr> act_map_qasymm8 =
{
@@ -188,12 +156,6 @@
case DataType::QASYMM8:
_func = act_map_qasymm8[activation_info.activation()];
break;
- case DataType::QS8:
- _func = act_map_qs8[activation_info.activation()];
- break;
- case DataType::QS16:
- _func = act_map_qs16[activation_info.activation()];
- break;
case DataType::F32:
_func = act_map_f32[activation_info.activation()];
break;
@@ -508,70 +470,6 @@
}
template <ActivationLayerInfo::ActivationFunction F, typename T>
-typename std::enable_if<std::is_same<T, int8_t>::value, void>::type NEActivationLayerKernel::activation(const Window &window)
-{
- Iterator input(_input, window);
- Iterator output(_output, window);
- const int fixed_point_position = _input->info()->fixed_point_position();
-
- static const qint8x16_t CONST_0 = vdupq_n_qs8(0);
- const qint8x16_t CONST_1 = vdupq_n_qs8(sqcvt_qs8_f32(1.f, fixed_point_position));
- const qint8x16_t a = vdupq_n_qs8(sqcvt_qs8_f32(_act_info.a(), fixed_point_position));
- const qint8x16_t b = vdupq_n_qs8(sqcvt_qs8_f32(_act_info.b(), fixed_point_position));
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const auto input_ptr = reinterpret_cast<const int8_t *>(input.ptr());
- const auto output_ptr = reinterpret_cast<int8_t *>(output.ptr());
-
- const qint8x16_t in = vld1q_qs8(input_ptr);
- qint8x16_t tmp = {};
-
- switch(F)
- {
- case ActivationFunction::ABS:
- tmp = vqabsq_qs8(in);
- break;
- case ActivationFunction::LINEAR:
- tmp = vqmlaq_qs8(b, a, in, fixed_point_position);
- break;
- case ActivationFunction::LOGISTIC:
- tmp = vqrecipq_qs8(vqaddq_qs8(CONST_1, vqexpq_qs8(vnegq_s8(in), fixed_point_position)), fixed_point_position);
- break;
- case ActivationFunction::RELU:
- tmp = vmaxq_qs8(CONST_0, in);
- break;
- case ActivationFunction::BOUNDED_RELU:
- tmp = vminq_qs8(a, vmaxq_qs8(CONST_0, in));
- break;
- case ActivationFunction::LU_BOUNDED_RELU:
- tmp = vminq_qs8(a, vmaxq_qs8(b, in));
- break;
- case ActivationFunction::LEAKY_RELU:
- tmp = vbslq_s8(vcgtq_s8(in, CONST_0), in, vmulq_qs8(a, in, fixed_point_position));
- break;
- case ActivationFunction::SOFT_RELU:
- tmp = vlogq_qs8(vqaddq_qs8(CONST_1, vqexpq_qs8(in, fixed_point_position)), fixed_point_position);
- break;
- case ActivationFunction::SQRT:
- tmp = vqrecipq_qs8(vqinvsqrtq_qs8(in, fixed_point_position), fixed_point_position);
- break;
- case ActivationFunction::SQUARE:
- tmp = vqmulq_qs8(in, in, fixed_point_position);
- break;
- case ActivationFunction::TANH:
- tmp = vqmulq_qs8(a, vqtanhq_qs8(vqmulq_qs8(b, in, fixed_point_position), fixed_point_position), fixed_point_position);
- break;
- default:
- break;
- }
-
- vst1q_qs8(output_ptr, tmp);
- },
- input, output);
-}
-
-template <ActivationLayerInfo::ActivationFunction F, typename T>
typename std::enable_if<std::is_same<T, qasymm8_t>::value, void>::type NEActivationLayerKernel::activation(const Window &window)
{
Iterator input(_input, window);
@@ -620,137 +518,6 @@
input, output);
}
-template <ActivationLayerInfo::ActivationFunction F, typename T>
-typename std::enable_if<std::is_same<T, qint16_t>::value, void>::type NEActivationLayerKernel::activation(const Window &window)
-{
- Iterator input(_input, window);
- Iterator output(_output, window);
- const int fixed_point_position = _input->info()->fixed_point_position();
-
- static const qint16x8_t CONST_0 = vdupq_n_qs16(0);
- const qint16x8_t CONST_1 = vdupq_n_qs16(sqcvt_qs16_f32(1.f, fixed_point_position));
- const qint16x8_t a = vdupq_n_qs16(sqcvt_qs16_f32(_act_info.a(), fixed_point_position));
- const qint16x8_t b = vdupq_n_qs16(sqcvt_qs16_f32(_act_info.b(), fixed_point_position));
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const auto input_ptr = reinterpret_cast<const int16_t *>(input.ptr());
- const auto output_ptr = reinterpret_cast<int16_t *>(output.ptr());
-
- const qint16x8x2_t in = vld2q_s16(input_ptr);
- qint16x8x2_t tmp = { {} };
-
- switch(F)
- {
- case ActivationFunction::ABS:
- tmp =
- {
- {
- vqabsq_qs16(in.val[0]),
- vqabsq_qs16(in.val[1]),
- }
- };
- break;
- case ActivationFunction::LINEAR:
- tmp =
- {
- {
- vqmlaq_qs16(b, a, in.val[0], fixed_point_position),
- vqmlaq_qs16(b, a, in.val[1], fixed_point_position),
- }
- };
- break;
- case ActivationFunction::LOGISTIC:
- tmp =
- {
- {
- vqrecipq_qs16(vqaddq_qs16(CONST_1, vqexpq_qs16(vnegq_s16(in.val[0]), fixed_point_position)), fixed_point_position),
- vqrecipq_qs16(vqaddq_qs16(CONST_1, vqexpq_qs16(vnegq_s16(in.val[1]), fixed_point_position)), fixed_point_position),
- }
- };
- break;
- case ActivationFunction::RELU:
- tmp =
- {
- {
- vmaxq_qs16(CONST_0, in.val[0]),
- vmaxq_qs16(CONST_0, in.val[1]),
- }
- };
- break;
- case ActivationFunction::BOUNDED_RELU:
- tmp =
- {
- {
- vminq_qs16(a, vmaxq_qs16(CONST_0, in.val[0])),
- vminq_qs16(a, vmaxq_qs16(CONST_0, in.val[1])),
- }
- };
- break;
- case ActivationFunction::LU_BOUNDED_RELU:
- tmp =
- {
- {
- vminq_qs16(a, vmaxq_qs16(b, in.val[0])),
- vminq_qs16(a, vmaxq_qs16(b, in.val[1])),
- }
- };
- break;
- case ActivationFunction::LEAKY_RELU:
- tmp =
- {
- {
- vbslq_s16(vcgtq_s16(in.val[0], CONST_0), in.val[0], vmulq_qs16(a, in.val[0], fixed_point_position)),
- vbslq_s16(vcgtq_s16(in.val[1], CONST_0), in.val[1], vmulq_qs16(a, in.val[1], fixed_point_position)),
- }
- };
- break;
- case ActivationFunction::SOFT_RELU:
- tmp =
- {
- {
- vlogq_qs16(vqaddq_qs16(CONST_1, vqexpq_qs16(in.val[0], fixed_point_position)), fixed_point_position),
- vlogq_qs16(vqaddq_qs16(CONST_1, vqexpq_qs16(in.val[1], fixed_point_position)), fixed_point_position),
- }
- };
- break;
- case ActivationFunction::SQRT:
- tmp =
- {
- {
- vqrecipq_qs16(vqinvsqrtq_qs16(in.val[0], fixed_point_position), fixed_point_position),
- vqrecipq_qs16(vqinvsqrtq_qs16(in.val[1], fixed_point_position), fixed_point_position),
- }
- };
- break;
- case ActivationFunction::SQUARE:
- tmp =
- {
- {
- vqmulq_qs16(in.val[0], in.val[0], fixed_point_position),
- vqmulq_qs16(in.val[1], in.val[1], fixed_point_position),
- }
- };
- break;
- case ActivationFunction::TANH:
- tmp =
- {
- {
- vqmulq_qs16(a, vqtanhq_qs16(vqmulq_qs16(b, in.val[0], fixed_point_position), fixed_point_position), fixed_point_position),
- vqmulq_qs16(a, vqtanhq_qs16(vqmulq_qs16(b, in.val[1], fixed_point_position), fixed_point_position), fixed_point_position),
- }
- };
- break;
- default:
- ARM_COMPUTE_ERROR("Function not implemented");
- break;
- }
-
- vst2q_qs16(output_ptr, tmp);
- },
- input, output);
-}
-
Status NEActivationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_UNUSED(act_info);
diff --git a/src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp b/src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp
index a487090..f8e2b6d 100644
--- a/src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp
+++ b/src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp
@@ -48,38 +48,6 @@
{
constexpr unsigned int num_elems_processed_per_iteration = 16;
-void add_wrap_QS8_QS8_QS8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
-{
- 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);
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const qint8x16_t a = vld1q_qs8(reinterpret_cast<const qint8_t *>(input1.ptr()));
- const qint8x16_t b = vld1q_qs8(reinterpret_cast<const qint8_t *>(input2.ptr()));
-
- vst1q_qs8(reinterpret_cast<qint8_t *>(output.ptr()), vaddq_qs8(a, b));
- },
- input1, input2, output);
-}
-
-void add_saturate_QS8_QS8_QS8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
-{
- 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);
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const qint8x16_t a = vld1q_qs8(reinterpret_cast<const qint8_t *>(input1.ptr()));
- const qint8x16_t b = vld1q_qs8(reinterpret_cast<const qint8_t *>(input2.ptr()));
-
- vst1q_qs8(reinterpret_cast<qint8_t *>(output.ptr()), vqaddq_qs8(a, b));
- },
- input1, input2, output);
-}
-
void add_wrap_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
{
Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()));
@@ -362,28 +330,21 @@
{
ARM_COMPUTE_UNUSED(policy);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32);
const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());
ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
- if(is_data_type_fixed_point(input1.data_type()) || is_data_type_fixed_point(input2.data_type()))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(&input1, &input2);
- }
-
// Validate in case of configured output
if(output.total_size() > 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(
- !(input1.data_type() == DataType::QS8 && input2.data_type() == DataType::QS8 && output.data_type() == DataType::QS8)
- && !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8 && output.data_type() == DataType::U8)
+ !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8 && output.data_type() == DataType::U8)
&& !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8 && output.data_type() == DataType::S16)
&& !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::S16 && output.data_type() == DataType::S16)
&& !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::U8 && output.data_type() == DataType::S16)
- && !(input1.data_type() == DataType::QS16 && input2.data_type() == DataType::QS16 && output.data_type() == DataType::QS16)
&& !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::S16 && output.data_type() == DataType::S16)
&& !(input1.data_type() == DataType::F32 && input2.data_type() == DataType::F32 && output.data_type() == DataType::F32)
&& !(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16 && output.data_type() == DataType::F16),
@@ -391,11 +352,6 @@
ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
"Wrong shape for output");
-
- if(is_data_type_fixed_point(input1.data_type()) || is_data_type_fixed_point(output.data_type()))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(&input1, &output);
- }
}
return Status{};
@@ -460,8 +416,6 @@
static std::map<std::string, AddFunction *> map_function =
{
- { "add_wrap_QS8_QS8_QS8", &add_wrap_QS8_QS8_QS8 },
- { "add_saturate_QS8_QS8_QS8", &add_saturate_QS8_QS8_QS8 },
{ "add_wrap_U8_U8_U8", &add_wrap_U8_U8_U8 },
{ "add_saturate_U8_U8_U8", &add_saturate_U8_U8_U8 },
{ "add_wrap_S16_U8_S16", &add_wrap_S16_U8_S16 },
@@ -470,8 +424,6 @@
{ "add_saturate_U8_S16_S16", &add_saturate_U8_S16_S16 },
{ "add_wrap_U8_U8_S16", &add_wrap_U8_U8_S16 },
{ "add_saturate_U8_U8_S16", &add_saturate_U8_U8_S16 },
- { "add_wrap_QS16_QS16_QS16", &add_wrap_S16_S16_S16 },
- { "add_saturate_QS16_QS16_QS16", &add_saturate_S16_S16_S16 },
{ "add_wrap_S16_S16_S16", &add_wrap_S16_S16_S16 },
{ "add_saturate_S16_S16_S16", &add_saturate_S16_S16_S16 },
{ "add_wrap_F32_F32_F32", &add_F32_F32_F32 },
diff --git a/src/core/NEON/kernels/NEArithmeticSubtractionKernel.cpp b/src/core/NEON/kernels/NEArithmeticSubtractionKernel.cpp
index 3db8028..5a162e3 100644
--- a/src/core/NEON/kernels/NEArithmeticSubtractionKernel.cpp
+++ b/src/core/NEON/kernels/NEArithmeticSubtractionKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -45,38 +45,6 @@
namespace
{
-void sub_wrap_QS8_QS8_QS8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
-{
- Iterator input1(in1, window);
- Iterator input2(in2, window);
- Iterator output(out, window);
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const qint8x16_t a = vld1q_qs8(reinterpret_cast<const qint8_t *>(input1.ptr()));
- const qint8x16_t b = vld1q_qs8(reinterpret_cast<const qint8_t *>(input2.ptr()));
-
- vst1q_qs8(reinterpret_cast<qint8_t *>(output.ptr()), vsubq_qs8(a, b));
- },
- input1, input2, output);
-}
-
-void sub_saturate_QS8_QS8_QS8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
-{
- Iterator input1(in1, window);
- Iterator input2(in2, window);
- Iterator output(out, window);
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const qint8x16_t a = vld1q_qs8(reinterpret_cast<const qint8_t *>(input1.ptr()));
- const qint8x16_t b = vld1q_qs8(reinterpret_cast<const qint8_t *>(input2.ptr()));
-
- vst1q_qs8(reinterpret_cast<qint8_t *>(output.ptr()), vqsubq_qs8(a, b));
- },
- input1, input2, output);
-}
-
void sub_wrap_U8_U8_U8(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
{
Iterator input1(in1, window);
@@ -353,23 +321,15 @@
{
ARM_COMPUTE_UNUSED(policy);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, input2, output);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::QS8, DataType::U8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::QS8, DataType::U8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::U8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
-
- if(is_data_type_fixed_point(input1->data_type()) || is_data_type_fixed_point(input2->data_type()) || is_data_type_fixed_point(output->data_type()))
- {
- // Check that all data types are the same and all fixed-point positions are the same
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input1, input2, output);
- }
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(
- !(input1->data_type() == DataType::QS8 && input2->data_type() == DataType::QS8 && output->data_type() == DataType::QS8)
- && !(input1->data_type() == DataType::U8 && input2->data_type() == DataType::U8 && output->data_type() == DataType::U8)
+ !(input1->data_type() == DataType::U8 && input2->data_type() == DataType::U8 && output->data_type() == DataType::U8)
&& !(input1->data_type() == DataType::U8 && input2->data_type() == DataType::U8 && output->data_type() == DataType::S16)
&& !(input1->data_type() == DataType::U8 && input2->data_type() == DataType::S16 && output->data_type() == DataType::S16)
&& !(input1->data_type() == DataType::S16 && input2->data_type() == DataType::U8 && output->data_type() == DataType::S16)
- && !(input1->data_type() == DataType::QS16 && input2->data_type() == DataType::QS16 && output->data_type() == DataType::QS16)
&& !(input1->data_type() == DataType::S16 && input2->data_type() == DataType::S16 && output->data_type() == DataType::S16)
&& !(input1->data_type() == DataType::F32 && input2->data_type() == DataType::F32 && output->data_type() == DataType::F32)
&& !(input1->data_type() == DataType::F16 && input2->data_type() == DataType::F16 && output->data_type() == DataType::F16),
@@ -432,8 +392,6 @@
static std::map<std::string, NEArithmeticSubtractionKernel::SubFunction *> map_function =
{
- { "sub_wrap_QS8_QS8_QS8", &sub_wrap_QS8_QS8_QS8 },
- { "sub_saturate_QS8_QS8_QS8", &sub_saturate_QS8_QS8_QS8 },
{ "sub_wrap_U8_U8_U8", &sub_wrap_U8_U8_U8 },
{ "sub_wrap_U8_U8_S16", &sub_wrap_U8_U8_S16 },
{ "sub_saturate_U8_U8_U8", &sub_saturate_U8_U8_U8 },
@@ -442,8 +400,6 @@
{ "sub_wrap_S16_U8_S16", &sub_wrap_S16_U8_S16 },
{ "sub_saturate_U8_S16_S16", &sub_saturate_U8_S16_S16 },
{ "sub_saturate_S16_U8_S16", &sub_saturate_S16_U8_S16 },
- { "sub_wrap_QS16_QS16_QS16", &sub_wrap_S16_S16_S16 },
- { "sub_saturate_QS16_QS16_QS16", &sub_saturate_S16_S16_S16 },
{ "sub_wrap_S16_S16_S16", &sub_wrap_S16_S16_S16 },
{ "sub_saturate_S16_S16_S16", &sub_saturate_S16_S16_S16 },
{ "sub_wrap_F32_F32_F32", &sub_F32_F32_F32 },
diff --git a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
index 6be50fd..6aed41f 100644
--- a/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp
@@ -43,7 +43,7 @@
const ITensorInfo *beta, const ITensorInfo *gamma, float epsilon, ActivationLayerInfo act_info)
{
ARM_COMPUTE_UNUSED(epsilon);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16,
DataType::F32);
if(act_info.enabled())
@@ -60,22 +60,18 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, mean, var);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, mean, var);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, var);
if(beta != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, beta);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, beta);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, beta);
}
if(gamma != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, gamma);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, gamma);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, gamma);
}
ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL)) != mean->dimension(0));
@@ -104,112 +100,6 @@
} //namespace
template <bool fused_activation>
-void NEBatchNormalizationLayerKernel::batch_normalization_qs8(const Window &window)
-{
- static_assert(!fused_activation, "Activation is not supported for QS8");
-
- Iterator input(_input, window);
- Iterator output(_output, window);
-
- // Hold information about the current feature map we are iterating.
- // Only compute denominator and NEON vectors once per feature map.
- int slice = -1;
-
- const int fixed_point_position = _input->info()->fixed_point_position();
- const auto input_mean = reinterpret_cast<const qint8_t *>(_mean->ptr_to_element(Coordinates(0, 0)));
- const auto input_var = reinterpret_cast<const qint8_t *>(_var->ptr_to_element(Coordinates(0, 0)));
- const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const qint8_t *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr;
- const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const qint8_t *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr;
-
- qint8x16_t mean_vec = vdupq_n_qs8(0);
- qint8x16_t var_vec = vdupq_n_qs8(0);
- qint8x16_t gamma_vec = vdupq_n_qs8(sqcvt_qs8_f32(1, fixed_point_position));
- qint8x16_t beta_vec = vdupq_n_qs8(sqcvt_qs8_f32(0, fixed_point_position));
- qint8x16_t denominator = vdupq_n_qs8(0);
- const qint8x16_t epsilon_vec = vdupq_n_qs8(sqcvt_qs8_f32(_epsilon, fixed_point_position));
- execute_window_loop(window, [&](const Coordinates & id)
- {
- if(slice != id.z())
- {
- // Conctruct vectors
- mean_vec = vdupq_n_qs8(*(input_mean + id.z()));
- var_vec = vdupq_n_qs8(*(input_var + id.z()));
- if(input_gamma != nullptr)
- {
- gamma_vec = vdupq_n_qs8(*(input_gamma + id.z()));
- }
- if(input_beta != nullptr)
- {
- beta_vec = vdupq_n_qs8(*(input_beta + id.z()));
- }
-
- // Calculate denominator
- denominator = vqinvsqrtq_qs8(vqaddq_qs8(var_vec, epsilon_vec), fixed_point_position);
- slice = id.z();
- }
-
- // Calculate x bar and store results
- const qint8x16_t numerator = vqsubq_qs8(vld1q_qs8(reinterpret_cast<const qint8_t *>(input.ptr())), mean_vec);
- const qint8x16_t x_bar = vqmulq_qs8(numerator, denominator, fixed_point_position);
- vst1q_qs8(reinterpret_cast<qint8_t *>(output.ptr()), vqmlaq_qs8(beta_vec, x_bar, gamma_vec, fixed_point_position));
- },
- input, output);
-}
-
-template <bool fused_activation>
-void NEBatchNormalizationLayerKernel::batch_normalization_qs16(const Window &window)
-{
- static_assert(!fused_activation, "Activation is not supported for QS16");
-
- Iterator input(_input, window);
- Iterator output(_output, window);
-
- // Hold information about the current feature map we are iterating.
- // Only compute denominator and NEON vectors once per feature map.
- int slice = -1;
-
- const int fixed_point_position = _input->info()->fixed_point_position();
- const auto input_mean = reinterpret_cast<const qint16_t *>(_mean->ptr_to_element(Coordinates(0, 0)));
- const auto input_var = reinterpret_cast<const qint16_t *>(_var->ptr_to_element(Coordinates(0, 0)));
- const auto input_gamma = (_gamma != nullptr) ? reinterpret_cast<const qint16_t *>(_gamma->ptr_to_element(Coordinates(0, 0))) : nullptr;
- const auto input_beta = (_beta != nullptr) ? reinterpret_cast<const qint16_t *>(_beta->ptr_to_element(Coordinates(0, 0))) : nullptr;
-
- qint16x8_t mean_vec = vdupq_n_qs16(0);
- qint16x8_t var_vec = vdupq_n_qs16(0);
- qint16x8_t gamma_vec = vdupq_n_qs16(sqcvt_qs16_f32(1, fixed_point_position));
- qint16x8_t beta_vec = vdupq_n_qs16(sqcvt_qs16_f32(0, fixed_point_position));
- qint16x8_t denominator = vdupq_n_qs16(0);
- const qint16x8_t epsilon_vec = vdupq_n_qs16(sqcvt_qs16_f32(_epsilon, fixed_point_position));
- execute_window_loop(window, [&](const Coordinates & id)
- {
- if(slice != id.z())
- {
- // Conctruct vectors
- mean_vec = vdupq_n_qs16(*(input_mean + id.z()));
- var_vec = vdupq_n_qs16(*(input_var + id.z()));
- if(input_gamma != nullptr)
- {
- gamma_vec = vdupq_n_qs16(*(input_gamma + id.z()));
- }
- if(input_beta != nullptr)
- {
- beta_vec = vdupq_n_qs16(*(input_beta + id.z()));
- }
-
- // Calculate denominator
- denominator = vqinvsqrtq_qs16(vqaddq_qs16(var_vec, epsilon_vec), fixed_point_position);
- slice = id.z();
- }
-
- // Calculate x bar and store results
- const qint16x8_t numerator = vqsubq_qs16(vld1q_qs16(reinterpret_cast<const qint16_t *>(input.ptr())), mean_vec);
- const qint16x8_t x_bar = vqmulq_qs16(numerator, denominator, fixed_point_position);
- vst1q_qs16(reinterpret_cast<qint16_t *>(output.ptr()), vqmlaq_qs16(beta_vec, x_bar, gamma_vec, fixed_point_position));
- },
- input, output);
-}
-
-template <bool fused_activation>
void NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw(const Window &window)
{
static_assert(!fused_activation, "Activation is not supported for FP16");
@@ -406,12 +296,6 @@
const bool is_nhwc = _input->info()->data_layout() == DataLayout::NHWC;
switch(_input->info()->data_type())
{
- case DataType::QS8:
- _func = &NEBatchNormalizationLayerKernel::batch_normalization_qs8<false>;
- break;
- case DataType::QS16:
- _func = &NEBatchNormalizationLayerKernel::batch_normalization_qs16<false>;
- break;
case DataType::F16:
_func = (is_nhwc) ? &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nhwc<false> : &NEBatchNormalizationLayerKernel::batch_normalization_fp16_nchw<false>;
break;
diff --git a/src/core/NEON/kernels/NECol2ImKernel.cpp b/src/core/NEON/kernels/NECol2ImKernel.cpp
index 9fda65f..d09d174 100644
--- a/src/core/NEON/kernels/NECol2ImKernel.cpp
+++ b/src/core/NEON/kernels/NECol2ImKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -50,8 +50,8 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &convolved_dims)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8,
- DataType::U16, DataType::S16, DataType::QS16,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8,
+ DataType::U16, DataType::S16,
DataType::U32, DataType::S32,
DataType::F16, DataType::F32);
@@ -60,7 +60,6 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), get_output_shape(input, convolved_dims));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
diff --git a/src/core/NEON/kernels/NEConvertFullyConnectedWeightsKernel.cpp b/src/core/NEON/kernels/NEConvertFullyConnectedWeightsKernel.cpp
index b3746bd..e581f22 100644
--- a/src/core/NEON/kernels/NEConvertFullyConnectedWeightsKernel.cpp
+++ b/src/core/NEON/kernels/NEConvertFullyConnectedWeightsKernel.cpp
@@ -65,7 +65,7 @@
Status NEConvertFullyConnectedWeightsKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const TensorShape &original_input_shape,
DataLayout data_layout)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8, DataType::U16, DataType::S16, DataType::QS16, DataType::U32, DataType::S32,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8, DataType::U16, DataType::S16, DataType::U32, DataType::S32,
DataType::QS32, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
diff --git a/src/core/NEON/kernels/NEDepthConcatenateLayerKernel.cpp b/src/core/NEON/kernels/NEDepthConcatenateLayerKernel.cpp
index 891a03c..38443ca 100644
--- a/src/core/NEON/kernels/NEDepthConcatenateLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEDepthConcatenateLayerKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017, 2018 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -41,10 +41,6 @@
namespace
{
// Overloads of 128-bit vector loads
-uint8x16_t loadq(const uint8_t *ptr)
-{
- return vld1q_u8(ptr);
-}
uint16x8_t loadq(const uint16_t *ptr)
{
return vld1q_u16(ptr);
@@ -54,10 +50,6 @@
return vld1q_u32(ptr);
}
// Overloads of 128-bit vector stores
-void storeq(uint8_t *ptr, uint8x16_t val)
-{
- return vst1q_u8(ptr, val);
-}
void storeq(uint16_t *ptr, uint16x8_t val)
{
return vst1q_u16(ptr, val);
@@ -107,9 +99,8 @@
void NEDepthConcatenateLayerKernel::configure(const ITensor *input, unsigned int depth_offset, ITensor *output)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output);
ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) + depth_offset > output->info()->dimension(2));
ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) > output->info()->dimension(0));
ARM_COMPUTE_ERROR_ON(input->info()->dimension(1) > output->info()->dimension(1));
@@ -129,10 +120,6 @@
switch(input->info()->data_type())
{
- case DataType::QS8:
- _func = &depth_concat<uint8_t>;
- break;
- case DataType::QS16:
case DataType::F16:
_func = &depth_concat<uint16_t>;
break;
diff --git a/src/core/NEON/kernels/NEDepthConvertLayerKernel.cpp b/src/core/NEON/kernels/NEDepthConvertLayerKernel.cpp
index c29cb57..8280b52 100644
--- a/src/core/NEON/kernels/NEDepthConvertLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEDepthConvertLayerKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -40,13 +40,13 @@
} // namespace arm_compute
NEDepthConvertLayerKernel::NEDepthConvertLayerKernel()
- : _input(nullptr), _output(nullptr), _policy(), _shift(0), _fixed_point_position_input(0), _fixed_point_position_output(0)
+ : _input(nullptr), _output(nullptr), _policy(), _shift(0)
{
}
void NEDepthConvertLayerKernel::configure(ITensor *input, ITensor *output, ConvertPolicy policy, uint32_t shift)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QS8, DataType::S16, DataType::U16, DataType::QS16, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S16, DataType::U16);
_input = input;
_output = input;
@@ -58,48 +58,26 @@
// Auto initialize output shape if not initialized (We can only auto-configure the shape, datatype must be given)
set_shape_if_empty(*output->info(), input->info()->tensor_shape());
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QS8, DataType::S16, DataType::U16, DataType::QS16, DataType::U32, DataType::S32, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16, DataType::U16, DataType::U32, DataType::S32, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
// Set output
_output = output;
}
- // Set initial fixed point position of input and output
- _fixed_point_position_input = input->info()->fixed_point_position();
- _fixed_point_position_output = _output->info()->fixed_point_position();
-
- // Set the fixed point position to the output tensor if needed
- if(is_data_type_fixed_point(input->info()->data_type()) && is_data_type_fixed_point(_output->info()->data_type()))
- {
- // If in-place set the fixed point position of the output tensor to be equal to shift
- _fixed_point_position_output = (_input == _output) ? static_cast<int>(_shift) : _fixed_point_position_output;
- // Set fixed point position to output tensor
- _output->info()->set_fixed_point_position(_fixed_point_position_output);
- }
-
- ARM_COMPUTE_ERROR_ON(shift >= 8 && (!is_data_type_fixed_point(input->info()->data_type()) && !is_data_type_fixed_point(output->info()->data_type())));
+ ARM_COMPUTE_ERROR_ON(shift >= 8);
ARM_COMPUTE_ERROR_ON(input == output && (data_size_from_type(input->info()->data_type()) != data_size_from_type(output->info()->data_type())));
ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == DataType::U8 && (output->info()->data_type() != DataType::S16 && output->info()->data_type() != DataType::U16
&& output->info()->data_type() != DataType::S32),
"Only data_types supported [in] U8 -> [out] U16, S16, S32");
- ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == DataType::QS8 && (output->info()->data_type() != DataType::QS8 && output->info()->data_type() != DataType::F32),
- "Only data_types supported [in] QS8 -> [out] QS8, F32");
-
ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == DataType::U16 && (output->info()->data_type() != DataType::U8 && output->info()->data_type() != DataType::U32),
"Only data_types supported [in] U16 -> [out] U8, U32");
ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == DataType::S16 && (output->info()->data_type() != DataType::U8 && output->info()->data_type() != DataType::S32),
"Only data_types supported [in] S16 -> [out] U8, S32");
- ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == DataType::QS16 && (output->info()->data_type() != DataType::QS16 && output->info()->data_type() != DataType::F32),
- "Only data_types supported [in] QS16 -> [out] QS16, F32");
-
- ARM_COMPUTE_ERROR_ON_MSG(input->info()->data_type() == DataType::F32 && (output->info()->data_type() != DataType::QS8 && output->info()->data_type() != DataType::QS16),
- "Only data_types supported [in] F32 -> [out] QS8, QS16");
-
constexpr unsigned int num_elems_processed_per_iteration = 16;
// Configure kernel window
@@ -132,8 +110,6 @@
Iterator input(_input, window);
Iterator output(_output, window);
- bool in_place = (_input == _output);
-
switch(_input->info()->data_type())
{
case DataType::U8:
@@ -212,49 +188,6 @@
}
break;
}
- case DataType::QS8:
- {
- switch(_output->info()->data_type())
- {
- case DataType::QS8:
- {
- const int relative_shift = _fixed_point_position_output - _fixed_point_position_input;
- /* Fixed point position conversion QS8 -> QS8 */
- if(relative_shift != 0 || !in_place)
- {
- const auto relative_shift_vec = vdupq_n_qs8(relative_shift);
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const qint8x16_t texels_qs8 = vld1q_qs8(reinterpret_cast<const qint8_t *>(input.ptr()));
- vst1q_qs8(reinterpret_cast<qint8_t *>(output.ptr()), vqrshlq_s8(texels_qs8, relative_shift_vec));
- },
- input, output);
- }
- break;
- }
- case DataType::F32:
- {
- /* Up-conversion QS8 -> F32 */
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const qint8x16_t texels_qs8 = vld1q_qs8(reinterpret_cast<const qint8_t *>(input.ptr()));
-
- float32x4x2_t texels_low = vcvt_f32_qs8(vget_low_s8(texels_qs8), _fixed_point_position_input);
- float32x4x2_t texels_high = vcvt_f32_qs8(vget_high_s8(texels_qs8), _fixed_point_position_input);
-
- vst1q_f32(reinterpret_cast<float *>(output.ptr()), texels_low.val[0]);
- vst1q_f32(reinterpret_cast<float *>(output.ptr()) + 4, texels_low.val[1]);
- vst1q_f32(reinterpret_cast<float *>(output.ptr()) + 8, texels_high.val[0]);
- vst1q_f32(reinterpret_cast<float *>(output.ptr()) + 12, texels_high.val[1]);
- },
- input, output);
- break;
- }
- default:
- ARM_COMPUTE_ERROR("Output data type not supported");
- }
- break;
- }
case DataType::S16:
{
switch(_output->info()->data_type())
@@ -408,116 +341,6 @@
}
break;
}
- case DataType::QS16:
- {
- switch(_output->info()->data_type())
- {
- case DataType::QS16:
- {
- const int relative_shift = _fixed_point_position_output - _fixed_point_position_input;
- /* Fixed point position conversion QS16 -> QS16 */
- if(relative_shift != 0 || !in_place)
- {
- const auto relative_shift_vec = vdupq_n_qs16(relative_shift);
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const qint16x8x2_t texels_qs16 =
- {
- {
- vld1q_qs16(reinterpret_cast<qint16_t *>(input.ptr())),
- vld1q_qs16(reinterpret_cast<qint16_t *>(input.ptr()) + 8)
- }
- };
- vst1q_qs16(reinterpret_cast<qint16_t *>(output.ptr()), vqrshlq_s16(texels_qs16.val[0], relative_shift_vec));
- vst1q_qs16(reinterpret_cast<qint16_t *>(output.ptr()) + 8, vqrshlq_s16(texels_qs16.val[1], relative_shift_vec));
- },
- input, output);
- }
- break;
- }
- case DataType::F32:
- {
- /* Up-conversion QS16 -> F32 */
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const int16x8x2_t texels_qs16 =
- {
- {
- vld1q_s16(reinterpret_cast<qint16_t *>(input.ptr())),
- vld1q_s16(reinterpret_cast<qint16_t *>(input.ptr()) + 8)
- }
- };
-
- vst1q_f32(reinterpret_cast<float *>(output.ptr()), vcvt_f32_qs16(vget_low_s16(texels_qs16.val[0]), _fixed_point_position_input));
- vst1q_f32(reinterpret_cast<float *>(output.ptr()) + 4, vcvt_f32_qs16(vget_high_s16(texels_qs16.val[0]), _fixed_point_position_input));
- vst1q_f32(reinterpret_cast<float *>(output.ptr()) + 8, vcvt_f32_qs16(vget_low_s16(texels_qs16.val[1]), _fixed_point_position_input));
- vst1q_f32(reinterpret_cast<float *>(output.ptr()) + 12, vcvt_f32_qs16(vget_high_s16(texels_qs16.val[1]), _fixed_point_position_input));
- },
- input, output);
- break;
- }
- default:
- ARM_COMPUTE_ERROR("Output data type not supported");
- }
- break;
- }
- case DataType::F32:
- {
- switch(_output->info()->data_type())
- {
- case DataType::QS8:
- {
- /* Down-conversion F32 -> QS8 */
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const float32x4x4_t texels_f32 =
- {
- {
- vld1q_f32(reinterpret_cast<const float *>(input.ptr())),
- vld1q_f32(reinterpret_cast<const float *>(input.ptr()) + 4),
- vld1q_f32(reinterpret_cast<const float *>(input.ptr()) + 8),
- vld1q_f32(reinterpret_cast<const float *>(input.ptr()) + 12)
- }
- };
-
- const qint8x16_t texels_s8 = vqcvtq_qs8_f32(texels_f32, _fixed_point_position_output);
-
- vst1q_s8(reinterpret_cast<int8_t *>(output.ptr()), texels_s8);
- },
- input, output);
- break;
- }
- case DataType::QS16:
- {
- /* Down-conversion F32 -> QS16 */
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const float32x4x2_t texels_f32_1 =
- {
- {
- vld1q_f32(reinterpret_cast<const float *>(input.ptr())),
- vld1q_f32(reinterpret_cast<const float *>(input.ptr()) + 4),
- }
- };
- const float32x4x2_t texels_f32_2 =
- {
- {
- vld1q_f32(reinterpret_cast<const float *>(input.ptr()) + 8),
- vld1q_f32(reinterpret_cast<const float *>(input.ptr()) + 12)
- }
- };
-
- vst1q_s16(reinterpret_cast<qint16_t *>(output.ptr()), vqcvtq_qs16_f32(texels_f32_1, _fixed_point_position_output));
- vst1q_s16(reinterpret_cast<qint16_t *>(output.ptr()) + 8, vqcvtq_qs16_f32(texels_f32_2, _fixed_point_position_output));
- },
- input, output);
- break;
- }
- default:
- ARM_COMPUTE_ERROR("Output data type not supported");
- }
- break;
- }
default:
ARM_COMPUTE_ERROR("Not supported");
}
diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp
index 8cdf175..09728e2 100644
--- a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp
+++ b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp
@@ -115,7 +115,7 @@
in_top += delta_input, in_mid += delta_input, in_low += delta_input,
p_out += num_elems_written_per_iteration)
{
- auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, 0, input_offset);
+ auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, input_offset);
store_results<stridex>(p_out, vres);
}
}
diff --git a/src/core/NEON/kernels/NEDepthwiseIm2ColKernel.cpp b/src/core/NEON/kernels/NEDepthwiseIm2ColKernel.cpp
index cfd8eac..5b43e2b 100644
--- a/src/core/NEON/kernels/NEDepthwiseIm2ColKernel.cpp
+++ b/src/core/NEON/kernels/NEDepthwiseIm2ColKernel.cpp
@@ -122,7 +122,6 @@
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(input->info()->data_type()) && has_bias);
ARM_COMPUTE_ERROR_ON((input->info()->dimension(2) * depth_multiplier) != output->info()->dimension(2));
ARM_COMPUTE_ERROR_ON(output->info()->dimension(0) != (kernel_dims.width * kernel_dims.height + ((has_bias) ? 1 : 0)));
diff --git a/src/core/NEON/kernels/NEDepthwiseVectorToTensorKernel.cpp b/src/core/NEON/kernels/NEDepthwiseVectorToTensorKernel.cpp
index 8960d8a..86a6d1c 100644
--- a/src/core/NEON/kernels/NEDepthwiseVectorToTensorKernel.cpp
+++ b/src/core/NEON/kernels/NEDepthwiseVectorToTensorKernel.cpp
@@ -89,7 +89,6 @@
ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
_input = input;
_output = output;
diff --git a/src/core/NEON/kernels/NEDepthwiseWeightsReshapeKernel.cpp b/src/core/NEON/kernels/NEDepthwiseWeightsReshapeKernel.cpp
index 36b17bf..47fcf12 100644
--- a/src/core/NEON/kernels/NEDepthwiseWeightsReshapeKernel.cpp
+++ b/src/core/NEON/kernels/NEDepthwiseWeightsReshapeKernel.cpp
@@ -88,7 +88,6 @@
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(input->info()->data_type()) && (biases != nullptr));
ARM_COMPUTE_ERROR_ON(input->info()->dimension(2) != output->info()->dimension(1));
ARM_COMPUTE_ERROR_ON(output->info()->dimension(0) != (input->info()->dimension(0) * input->info()->dimension(1) + ((biases != nullptr) ? 1 : 0)));
@@ -96,7 +95,6 @@
if(biases != nullptr)
{
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != input->info()->dimension(2));
ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
}
diff --git a/src/core/NEON/kernels/NEDequantizationLayerKernel.cpp b/src/core/NEON/kernels/NEDequantizationLayerKernel.cpp
index 4120e5f..47c895c 100644
--- a/src/core/NEON/kernels/NEDequantizationLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEDequantizationLayerKernel.cpp
@@ -54,7 +54,7 @@
std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *min_max)
{
// Output tensor auto initialization if not yet initialized
- auto_init_if_empty(*output, input->tensor_shape(), 1, DataType::F32, 0);
+ auto_init_if_empty(*output, input->tensor_shape(), 1, DataType::F32);
constexpr unsigned int num_elems_processed_per_iteration = 8;
diff --git a/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp
index 5eafdf0..54a0468 100644
--- a/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEDirectConvolutionLayerKernel.cpp
@@ -43,34 +43,6 @@
namespace
{
-template <unsigned int stridex>
-qint16x8_t internal_vld1q(const qint16_t *in);
-
-template <>
-qint16x8_t internal_vld1q<1>(const qint16_t *in)
-{
- return vld1q_qs16(in);
-}
-
-template <>
-qint16x8_t internal_vld1q<2>(const qint16_t *in)
-{
- const int16x8x2_t tmp = vld2q_s16(in);
- return tmp.val[0];
-}
-
-template <>
-qint16x8_t internal_vld1q<3>(const qint16_t *in)
-{
- const int16x8x3_t tmp = vld3q_s16(in);
- return tmp.val[0];
-}
-
-inline qint16x8_t internal_vdupq_n(qint16_t v)
-{
- return vdupq_n_qs16(v);
-}
-
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
template <unsigned int stridex>
float16x8_t internal_vld1q(const float16_t *in);
@@ -105,15 +77,13 @@
vst1q_f16(p, v);
}
-float16x8_t internal_vmull(const float16x8_t &x, const float16x8_t &y, int fixed_point_position)
+float16x8_t internal_vmull(const float16x8_t &x, const float16x8_t &y)
{
- ARM_COMPUTE_UNUSED(fixed_point_position);
return vmulq_f16(x, y);
}
-inline float16x8_t internal_vmlal(const float16x8_t &x, const float16x8_t &y, const float16x8_t &z, int fixed_point_position)
+inline float16x8_t internal_vmlal(const float16x8_t &x, const float16x8_t &y, const float16x8_t &z)
{
- ARM_COMPUTE_UNUSED(fixed_point_position);
return vaddq_f16(x, vmulq_f16(y, z));
}
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
@@ -151,107 +121,16 @@
vst1q_f32(p, v);
}
-float32x4_t internal_vmull(const float32x4_t &x, const float32x4_t &y, int fixed_point_position)
+float32x4_t internal_vmull(const float32x4_t &x, const float32x4_t &y)
{
- ARM_COMPUTE_UNUSED(fixed_point_position);
return vmulq_f32(x, y);
}
-inline float32x4_t internal_vmlal(const float32x4_t &x, const float32x4_t &y, const float32x4_t &z, int fixed_point_position)
+inline float32x4_t internal_vmlal(const float32x4_t &x, const float32x4_t &y, const float32x4_t &z)
{
- ARM_COMPUTE_UNUSED(fixed_point_position);
return vmlaq_f32(x, y, z);
}
-template <unsigned int stridex>
-qint8x8_t internal_vld1q(const qint8_t *in);
-
-template <>
-qint8x8_t internal_vld1q<1>(const qint8_t *in)
-{
- return vld1_qs8(in);
-}
-
-template <>
-qint8x8_t internal_vld1q<2>(const qint8_t *in)
-{
- const qint8x8x2_t tmp = vld2_s8(in);
- return tmp.val[0];
-}
-
-template <>
-qint8x8_t internal_vld1q<3>(const qint8_t *in)
-{
- const qint8x8x3_t tmp = vld3_s8(in);
- return tmp.val[0];
-}
-
-inline qint8x8_t internal_vdupq_n(qint8_t v)
-{
- return vdup_n_qs8(v);
-}
-
-inline qint16x8_t internal_vmull(const qint8x8_t &x, const qint8x8_t &y, int fixed_point_position)
-{
- return vmull_qs8(x, y, fixed_point_position);
-}
-
-inline qint16x8_t internal_vmlal(const qint16x8_t &x, const qint8x8_t &y, const qint8x8_t &z, int fixed_point_position)
-{
- return vqmlal_qs8(x, y, z, fixed_point_position);
-}
-
-inline void internal_vst1q(qint16_t *p, const qint16x8_t &v)
-{
- vst1q_qs16(p, v);
-}
-
-inline void internal_vst1q(int32_t *p, const qint32x4x2_t &v)
-{
- vst1q_s32(p, v.val[0]);
- vst1q_s32(p + 4, v.val[1]);
-}
-
-template <unsigned int stridex>
-qint32x4x2_t internal_vld1q(const qint32_t *in);
-
-template <>
-qint32x4x2_t internal_vld1q<1>(const qint32_t *in)
-{
- const qint32x4x2_t r =
- {
- {
- vld1q_s32(in),
- vld1q_s32(in + 4)
- }
- };
- return r;
-}
-
-inline qint32x4x2_t internal_vmull(const qint16x8_t &x, const qint16x8_t &y, int fixed_point_position)
-{
- const qint32x4x2_t r =
- {
- {
- vmull_qs16(vget_low_s16(x), vget_low_s16(y), fixed_point_position),
- vmull_qs16(vget_high_s16(x), vget_high_s16(y), fixed_point_position),
- }
- };
- return r;
-}
-
-inline qint32x4x2_t internal_vmlal(const qint32x4x2_t &x, const qint16x8_t &y, const qint16x8_t &z, int fixed_point_position)
-{
- const qint32x4x2_t r =
- {
- {
- vqmlal_qs16(x.val[0], vget_low_s16(y), vget_low_s16(z), fixed_point_position),
- vqmlal_qs16(x.val[1], vget_high_s16(y), vget_high_s16(z), fixed_point_position)
- }
- };
- return r;
-}
-
constexpr int small_tensor_size_optim = 8;
inline bool run_optim_small_tensor_info(const ITensorInfo *t)
{
@@ -355,21 +234,20 @@
static void convolve(const Window &window, unsigned int num_elems_read_per_iteration, unsigned int num_elems_written_per_iteration,
const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
{
- const int input_stride_x = input->info()->strides_in_bytes().x();
- const int input_stride_y = input->info()->strides_in_bytes().y();
- const int input_stride_z = input->info()->strides_in_bytes().z();
- const int output_stride_y = output->info()->strides_in_bytes().y();
- const int output_stride_z = output->info()->strides_in_bytes().z();
- const int kernel_stride_z = weights->info()->strides_in_bytes().z();
- const int kernel_stride_w = weights->info()->strides_in_bytes()[3];
- const int output_w = output->info()->dimension(0);
- const int output_h = output->info()->dimension(1);
- const int range_z = window.z().end() - window.z().start();
- const int kernel_depth = weights->info()->dimension(Window::DimZ);
- const unsigned int conv_stride_y = std::get<1>(conv_info.stride());
- const unsigned int conv_pad_left = conv_info.pad_left();
- const unsigned int conv_pad_top = conv_info.pad_top();
- const int fixed_point_position = input->info()->fixed_point_position();
+ const int input_stride_x = input->info()->strides_in_bytes().x();
+ const int input_stride_y = input->info()->strides_in_bytes().y();
+ const int input_stride_z = input->info()->strides_in_bytes().z();
+ const int output_stride_y = output->info()->strides_in_bytes().y();
+ const int output_stride_z = output->info()->strides_in_bytes().z();
+ const int kernel_stride_z = weights->info()->strides_in_bytes().z();
+ const int kernel_stride_w = weights->info()->strides_in_bytes()[3];
+ const int output_w = output->info()->dimension(0);
+ const int output_h = output->info()->dimension(1);
+ const int range_z = window.z().end() - window.z().start();
+ const int kernel_depth = weights->info()->dimension(Window::DimZ);
+ const unsigned int conv_stride_y = std::get<1>(conv_info.stride());
+ const unsigned int conv_pad_left = conv_info.pad_left();
+ const unsigned int conv_pad_top = conv_info.pad_top();
// setup output window for the iterator
Window window_out = window;
@@ -414,7 +292,7 @@
auto p_out = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y);
for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_val += num_elems_read_per_iteration, p_out += num_elems_written_per_iteration)
{
- internal_vst1q(p_out, internal_vmull(vk, internal_vld1q<stridex>(in_val), fixed_point_position));
+ internal_vst1q(p_out, internal_vmull(vk, internal_vld1q<stridex>(in_val)));
}
}
}
@@ -431,7 +309,7 @@
auto p_out = reinterpret_cast<T2 *>(p_out_base + oh * output_stride_y);
for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_val += num_elems_read_per_iteration, p_out += num_elems_written_per_iteration)
{
- internal_vst1q(p_out, internal_vmlal(internal_vld1q<1>(p_out), vk, internal_vld1q<stridex>(in_val), fixed_point_position));
+ internal_vst1q(p_out, internal_vmlal(internal_vld1q<1>(p_out), vk, internal_vld1q<stridex>(in_val)));
}
}
}
@@ -469,7 +347,7 @@
template <unsigned int stridex>
float32x4x2_t convolve_5x5(const float *in_0, const float *in_1, const float *in_2, const float *in_3, const float *in_4,
- const float *m0, const float *m1, const float *m2, const float *m3, const float *m4, int fixed_point_position);
+ const float *m0, const float *m1, const float *m2, const float *m3, const float *m4);
inline float32x4x3_t load_matrix_hi(const float *const m0, const float *const m1, const float *const m2)
{
@@ -511,9 +389,8 @@
template <>
inline float32x4x2_t convolve_5x5<1>(const float *in_0, const float *in_1, const float *in_2, const float *in_3, const float *in_4,
- const float *m0, const float *m1, const float *m2, const float *m3, const float *m4, int fixed_point_position)
+ const float *m0, const float *m1, const float *m2, const float *m3, const float *m4)
{
- ARM_COMPUTE_UNUSED(fixed_point_position);
const float32x4x3_t vin0 = load_input(in_0);
const float32x4x3_t vin1 = load_input(in_1);
const float32x4x3_t vin2 = load_input(in_2);
@@ -601,10 +478,9 @@
template <>
inline float32x4x2_t convolve_5x5<2>(const float *in_0, const float *in_1, const float *in_2, const float *in_3, const float *in_4,
- const float *m0, const float *m1, const float *m2, const float *m3, const float *m4, int fixed_point_position)
+ const float *m0, const float *m1, const float *m2, const float *m3, const float *m4)
{
- ARM_COMPUTE_UNUSED(fixed_point_position);
- float32x4x2_t out = convolve_5x5<1>(in_0, in_1, in_2, in_3, in_4, m0, m1, m2, m3, m4, fixed_point_position);
+ float32x4x2_t out = convolve_5x5<1>(in_0, in_1, in_2, in_3, in_4, m0, m1, m2, m3, m4);
out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 2), out.val[0], 1);
out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 0), out.val[0], 2);
out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[1], 2), out.val[0], 3);
@@ -613,9 +489,9 @@
template <>
inline float32x4x2_t convolve_5x5<3>(const float *in_0, const float *in_1, const float *in_2, const float *in_3, const float *in_4,
- const float *m0, const float *m1, const float *m2, const float *m3, const float *m4, int fixed_point_position)
+ const float *m0, const float *m1, const float *m2, const float *m3, const float *m4)
{
- float32x4x2_t out = convolve_5x5<1>(in_0, in_1, in_2, in_3, in_4, m0, m1, m2, m3, m4, fixed_point_position);
+ float32x4x2_t out = convolve_5x5<1>(in_0, in_1, in_2, in_3, in_4, m0, m1, m2, m3, m4);
out.val[0] = vsetq_lane_f32(vgetq_lane_f32(out.val[0], 3), out.val[0], 1);
return out;
}
@@ -642,28 +518,6 @@
vst1_f32(buffer, vadd_f32(vld1_f32(buffer), vget_low_f32(values.val[0])));
}
-template <unsigned int stridex>
-void accumulate_results(qint16_t *buffer, const qint16x8x2_t &values);
-
-template <>
-void accumulate_results<1>(qint16_t *buffer, const qint16x8x2_t &values)
-{
- vst1q_qs16(buffer, vqaddq_qs16(vld1q_qs16(buffer), values.val[0]));
- vst1q_qs16(buffer + 8, vqaddq_qs16(vld1q_qs16(buffer + 8), values.val[1]));
-}
-
-template <>
-void accumulate_results<2>(qint16_t *buffer, const qint16x8x2_t &values)
-{
- vst1q_qs16(buffer, vqaddq_qs16(vld1q_qs16(buffer), values.val[0]));
-}
-
-template <>
-void accumulate_results<3>(qint16_t *buffer, const qint16x8x2_t &values)
-{
- vst1_qs16(buffer, vqadd_qs16(vld1_qs16(buffer), vget_low_s16(values.val[0])));
-}
-
template <typename T1>
class convolver_nhwc
{
@@ -745,7 +599,7 @@
const auto we_addr = reinterpret_cast<const T1 *>(we_addr_base1 + x * kernel_stride_x);
const auto we_values = internal_vld1q<1>(we_addr);
- out_values = internal_vmlal(out_values, in_values, we_values, 0);
+ out_values = internal_vmlal(out_values, in_values, we_values);
}
out_val += out_values[0];
@@ -784,24 +638,23 @@
const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
{
ARM_COMPUTE_UNUSED(num_elems_read_per_iteration);
- const int input_stride_x = input->info()->strides_in_bytes().x();
- const int input_stride_y = input->info()->strides_in_bytes().y();
- const int input_stride_z = input->info()->strides_in_bytes().z();
- const int output_stride_y = output->info()->strides_in_bytes().y();
- const int output_stride_z = output->info()->strides_in_bytes().z();
- const int kernel_stride_x = weights->info()->strides_in_bytes().x();
- const int kernel_stride_y = weights->info()->strides_in_bytes().y();
- const int kernel_stride_z = weights->info()->strides_in_bytes().z();
- const int kernel_stride_w = weights->info()->strides_in_bytes()[3];
- const int output_w = output->info()->dimension(0);
- const int output_h = output->info()->dimension(1);
- const int num_planes_z = window.z().end() - window.z().start();
- const int delta_input = get_input_num_elems_processed<stridex>(num_elems_written_per_iteration);
- const int kernel_depth = weights->info()->dimension(Window::DimZ);
- const unsigned int conv_stride_y = std::get<1>(conv_info.stride());
- const unsigned int conv_pad_left = conv_info.pad_left();
- const unsigned int conv_pad_top = conv_info.pad_top();
- const int fixed_point_position = input->info()->fixed_point_position();
+ const int input_stride_x = input->info()->strides_in_bytes().x();
+ const int input_stride_y = input->info()->strides_in_bytes().y();
+ const int input_stride_z = input->info()->strides_in_bytes().z();
+ const int output_stride_y = output->info()->strides_in_bytes().y();
+ const int output_stride_z = output->info()->strides_in_bytes().z();
+ const int kernel_stride_x = weights->info()->strides_in_bytes().x();
+ const int kernel_stride_y = weights->info()->strides_in_bytes().y();
+ const int kernel_stride_z = weights->info()->strides_in_bytes().z();
+ const int kernel_stride_w = weights->info()->strides_in_bytes()[3];
+ const int output_w = output->info()->dimension(0);
+ const int output_h = output->info()->dimension(1);
+ const int num_planes_z = window.z().end() - window.z().start();
+ const int delta_input = get_input_num_elems_processed<stridex>(num_elems_written_per_iteration);
+ const int kernel_depth = weights->info()->dimension(Window::DimZ);
+ const unsigned int conv_stride_y = std::get<1>(conv_info.stride());
+ const unsigned int conv_pad_left = conv_info.pad_left();
+ const unsigned int conv_pad_top = conv_info.pad_top();
// setup output window for the iterator
Window window_out = window;
@@ -864,7 +717,7 @@
for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration,
in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration)
{
- auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2, fixed_point_position);
+ auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2);
store_results<stridex>(p_out, vres);
}
}
@@ -889,7 +742,7 @@
for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration,
in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration)
{
- auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2, fixed_point_position);
+ auto vres = convolve_3x3<stridex>(in_top, in_mid, in_low, vk_r0, vk_r1, vk_r2);
accumulate_results<stridex>(p_out, vres);
}
}
@@ -908,24 +761,23 @@
const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info)
{
ARM_COMPUTE_UNUSED(num_elems_read_per_iteration);
- const int input_stride_x = input->info()->strides_in_bytes().x();
- const int input_stride_y = input->info()->strides_in_bytes().y();
- const int input_stride_z = input->info()->strides_in_bytes().z();
- const int output_stride_y = output->info()->strides_in_bytes().y();
- const int output_stride_z = output->info()->strides_in_bytes().z();
- const int kernel_stride_x = weights->info()->strides_in_bytes().x();
- const int kernel_stride_y = weights->info()->strides_in_bytes().y();
- const int kernel_stride_z = weights->info()->strides_in_bytes().z();
- const int kernel_stride_w = weights->info()->strides_in_bytes()[3];
- const int output_w = output->info()->dimension(0);
- const int output_h = output->info()->dimension(1);
- const int num_planes_z = window.z().end() - window.z().start();
- const int delta_input = get_input_num_elems_processed<stridex>(num_elems_written_per_iteration);
- const int kernel_depth = weights->info()->dimension(Window::DimZ);
- const unsigned int conv_stride_y = std::get<1>(conv_info.stride());
- const unsigned int conv_pad_left = conv_info.pad_left();
- const unsigned int conv_pad_top = conv_info.pad_top();
- const int fixed_point_position = input->info()->fixed_point_position();
+ const int input_stride_x = input->info()->strides_in_bytes().x();
+ const int input_stride_y = input->info()->strides_in_bytes().y();
+ const int input_stride_z = input->info()->strides_in_bytes().z();
+ const int output_stride_y = output->info()->strides_in_bytes().y();
+ const int output_stride_z = output->info()->strides_in_bytes().z();
+ const int kernel_stride_x = weights->info()->strides_in_bytes().x();
+ const int kernel_stride_y = weights->info()->strides_in_bytes().y();
+ const int kernel_stride_z = weights->info()->strides_in_bytes().z();
+ const int kernel_stride_w = weights->info()->strides_in_bytes()[3];
+ const int output_w = output->info()->dimension(0);
+ const int output_h = output->info()->dimension(1);
+ const int num_planes_z = window.z().end() - window.z().start();
+ const int delta_input = get_input_num_elems_processed<stridex>(num_elems_written_per_iteration);
+ const int kernel_depth = weights->info()->dimension(Window::DimZ);
+ const unsigned int conv_stride_y = std::get<1>(conv_info.stride());
+ const unsigned int conv_pad_left = conv_info.pad_left();
+ const unsigned int conv_pad_top = conv_info.pad_top();
// setup output window for the iterator
Window window_out = window;
@@ -976,7 +828,7 @@
for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration,
in_0 += delta_input, in_1 += delta_input, in_2 += delta_input, in_3 += delta_input, in_4 += delta_input, p_out += num_elems_written_per_iteration)
{
- auto vres = convolve_5x5<stridex>(in_0, in_1, in_2, in_3, in_4, ptr_k_r0, ptr_k_r1, ptr_k_r2, ptr_k_r3, ptr_k_r4, fixed_point_position);
+ auto vres = convolve_5x5<stridex>(in_0, in_1, in_2, in_3, in_4, ptr_k_r0, ptr_k_r1, ptr_k_r2, ptr_k_r3, ptr_k_r4);
store_results<stridex>(p_out, vres);
}
}
@@ -1001,7 +853,7 @@
for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration,
in_0 += delta_input, in_1 += delta_input, in_2 += delta_input, in_3 += delta_input, in_4 += delta_input, p_out += num_elems_written_per_iteration)
{
- auto vres = convolve_5x5<stridex>(in_0, in_1, in_2, in_3, in_4, ptr_k_r0, ptr_k_r1, ptr_k_r2, ptr_k_r3, ptr_k_r4, fixed_point_position);
+ auto vres = convolve_5x5<stridex>(in_0, in_1, in_2, in_3, in_4, ptr_k_r0, ptr_k_r1, ptr_k_r2, ptr_k_r3, ptr_k_r4);
accumulate_results<stridex>(p_out, vres);
}
}
@@ -1120,7 +972,7 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
const DataLayout data_layout = input->data_layout();
@@ -1140,11 +992,6 @@
TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(*input, *weights, conv_info);
DataType data_type = input->data_type();
- if(is_data_type_fixed_point(data_type))
- {
- // Promote data type in case of fixed point
- data_type = ((data_type == DataType::QS8) ? DataType::QS16 : DataType::QS32);
- }
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
ARM_COMPUTE_RETURN_ERROR_ON(output->data_type() != data_type);
@@ -1180,11 +1027,9 @@
{
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
-#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
- case DataType::QS8:
- case DataType::QS16:
num_elems_written_per_iteration = 8;
break;
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
case DataType::F32:
if(run_optim_small_tensor_info(input))
{
@@ -1215,13 +1060,11 @@
break;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
-#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
- case DataType::QS8:
- case DataType::QS16:
num_weight_elems_read_per_row = 8 + kernel_size - 1;
num_elems_read_per_iteration = 24;
num_elems_written_per_iteration = 32 >> conv_stride_x;
break;
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
default:
ARM_COMPUTE_ERROR("Data type not supported.");
break;
@@ -1315,14 +1158,8 @@
DataType data_type = input->info()->data_type();
- if(is_data_type_fixed_point(data_type))
- {
- // Promote data type in case of fixed point
- data_type = ((data_type == DataType::QS8) ? DataType::QS16 : DataType::QS32);
- }
-
// Output auto inizialitation if not yet initialized
- auto_init_if_empty(*output->info(), output_shape, 1, data_type, input->info()->fixed_point_position());
+ auto_init_if_empty(*output->info(), output_shape, 1, data_type);
// Perform validation step
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), output->info(), conv_info));
@@ -1371,12 +1208,6 @@
{
switch(_input->info()->data_type())
{
- case DataType::QS8:
- convolve_1x1<qint8_t, qint16_t>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
- break;
- case DataType::QS16:
- convolve_1x1<qint16_t, qint32_t>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
- break;
case DataType::F32:
convolve_1x1<float, float>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
break;
@@ -1395,9 +1226,6 @@
{
switch(_input->info()->data_type())
{
- case DataType::QS8:
- convolve_3x3<qint8_t, qint16_t>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
- break;
case DataType::F32:
convolve_3x3<float, float>(window, _num_elems_read_per_iteration, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info);
break;
diff --git a/src/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.cpp b/src/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.cpp
index 37a3804..e4cd4d0 100644
--- a/src/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.cpp
+++ b/src/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.cpp
@@ -45,22 +45,15 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8,
- DataType::QS16, DataType::F16,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8,
+ DataType::F16,
DataType::QS32, DataType::S32, DataType::F32);
if(bias != nullptr)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::QS32, DataType::S32, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::F16, DataType::QS32, DataType::S32, DataType::F32);
- if(is_data_type_fixed_point(input->data_type()))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QS8 && bias->data_type() != DataType::QS8, "Wrong data type for bias");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QS16 && bias->data_type() != DataType::QS8, "Wrong data type for bias");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QS32 && bias->data_type() != DataType::QS16, "Wrong data type for bias");
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, bias);
- }
- else if(is_data_type_quantized_asymmetric(input->data_type()))
+ if(is_data_type_quantized_asymmetric(input->data_type()))
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
}
@@ -80,17 +73,10 @@
// Checks performed when output is configured
if((output != nullptr) && (output->total_size() != 0))
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
- if(is_data_type_fixed_point(input->data_type()))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QS8 && output->data_type() != DataType::QS8, "Wrong data type for output");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QS16 && output->data_type() != DataType::QS8, "Wrong data type for output");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QS32 && output->data_type() != DataType::QS16, "Wrong data type for output");
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output);
- }
- else if(is_data_type_quantized_asymmetric(output->data_type()))
+ if(is_data_type_quantized_asymmetric(output->data_type()))
{
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::S32 && output->data_type() != DataType::QASYMM8, "Wrong data type for bias");
}
@@ -168,81 +154,24 @@
{
return vld1q_f32(in);
}
-inline qint8x16_t internal_vld1q(const qint8_t *in)
-{
- return vld1q_qs8(in);
-}
-inline qint16x8_t internal_vld1q(const qint16_t *in)
-{
- return vld1q_qs16(in);
-}
-inline qint32x4_t internal_vld1q(const qint32_t *in)
-{
- return vld1q_s32(in);
-}
// Internal store
inline void internal_vst1q(float *p, const float32x4_t &v)
{
vst1q_f32(p, v);
}
-inline void internal_vst1q(qint8_t *p, const qint8x16_t &v)
-{
- vst1q_qs8(p, v);
-}
-inline void internal_vst1q(qint8_t *p, const qint16x8_t &v)
-{
- vst1_qs8(p, vqmovn_s16(v));
-}
-inline void internal_vst1q(qint16_t *p, const qint16x8_t &v)
-{
- vst1q_qs16(p, v);
-}
-inline void internal_vst1q(qint32_t *p, const qint32x4_t &v)
-{
- vst1q_s32(p, v);
-}
-
-inline void internal_vst1q(qint16_t *p, const qint32x4_t &v)
-{
- vst1_qs16(p, vqmovn_qs32(v));
-}
// Internal vdup
inline float32x4_t internal_vdupq_n(float v)
{
return vdupq_n_f32(v);
}
-inline qint8x16_t internal_vdupq_n(qint8_t v)
-{
- return vdupq_n_qs8(v);
-}
-inline qint16x8_t internal_vdupq_n(qint16_t v)
-{
- return vdupq_n_qs16(v);
-}
-inline qint32x4_t internal_vdupq_n(qint32_t v)
-{
- return vdupq_n_qs32(v);
-}
// Internal vadd
inline float32x4_t internal_vqaddq(const float32x4_t &x, const float32x4_t &y)
{
return vaddq_f32(x, y);
}
-inline qint8x16_t internal_vqaddq(const qint8x16_t &x, const qint8x16_t &y)
-{
- return vqaddq_qs8(x, y);
-}
-inline qint16x8_t internal_vqaddq(const qint16x8_t &x, const qint16x8_t &y)
-{
- return vqaddq_qs16(x, y);
-}
-inline qint32x4_t internal_vqaddq(const qint32x4_t &x, const qint32x4_t &y)
-{
- return vqaddq_qs32(x, y);
-}
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
inline float16x8_t internal_vld1q(const float16_t *in)
@@ -494,39 +423,6 @@
{
switch(input->info()->data_type())
{
- case DataType::QS8:
- {
- if(bias == nullptr)
- {
- _func = (output == nullptr) ? &output_stage<qint8_t, qint8_t, true, false> : &output_stage<qint8_t, qint8_t, false, false>;
- }
- else
- {
- _func = (output == nullptr) ? &output_stage<qint8_t, qint8_t, true, true> : &output_stage<qint8_t, qint8_t, false, true>;
- }
- break;
- }
- case DataType::QS16:
- {
- if(bias != nullptr && bias->info()->data_type() == DataType::QS8)
- {
- _func = (output == nullptr) ? &output_stage<qint16_t, qint8_t, true, true> : &output_stage<qint16_t, qint8_t, false, true>;
- }
- else if(bias == nullptr)
- {
- _func = (output == nullptr) ? &output_stage<qint16_t, qint8_t, true, false> : &output_stage<qint16_t, qint8_t, false, false>;
- }
- else
- {
- ARM_COMPUTE_ERROR("Not implemented");
- }
- break;
- }
- case DataType::QS32:
- {
- _func = (output == nullptr) ? &output_stage<qint32_t, qint16_t, true, true> : &output_stage<qint32_t, qint16_t, false, true>;
- break;
- }
case DataType::S32:
{
_func = (bias == nullptr) ? &output_stage<int32_t, uint8_t, false, false> : &output_stage<int32_t, uint8_t, false, true>;
diff --git a/src/core/NEON/kernels/NEFillBorderKernel.cpp b/src/core/NEON/kernels/NEFillBorderKernel.cpp
index 747b8b1..3d08caf 100644
--- a/src/core/NEON/kernels/NEFillBorderKernel.cpp
+++ b/src/core/NEON/kernels/NEFillBorderKernel.cpp
@@ -105,8 +105,8 @@
void NEFillBorderKernel::configure(ITensor *tensor, BorderSize border_size, BorderMode border_mode, const PixelValue &constant_border_value)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(tensor, 1, DataType::U8, DataType::QS8, DataType::QASYMM8,
- DataType::QS16, DataType::U16, DataType::S16,
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(tensor, 1, DataType::U8, DataType::QASYMM8,
+ DataType::U16, DataType::S16,
DataType::U32, DataType::S32,
DataType::F16, DataType::F32);
@@ -147,7 +147,6 @@
case DataType::U8:
fill_constant_value_single_channel<uint8_t>(window);
break;
- case DataType::QS8:
case DataType::S8:
fill_constant_value_single_channel<int8_t>(window);
break;
@@ -155,7 +154,6 @@
fill_constant_value_single_channel<uint16_t>(window);
break;
case DataType::S16:
- case DataType::QS16:
fill_constant_value_single_channel<int16_t>(window);
break;
case DataType::U32:
@@ -192,7 +190,6 @@
case DataType::U8:
fill_replicate_single_channel<uint8_t>(window);
break;
- case DataType::QS8:
case DataType::S8:
fill_replicate_single_channel<int8_t>(window);
break;
@@ -200,7 +197,6 @@
fill_replicate_single_channel<uint16_t>(window);
break;
case DataType::S16:
- case DataType::QS16:
fill_replicate_single_channel<int16_t>(window);
break;
case DataType::U32:
diff --git a/src/core/NEON/kernels/NEFloorKernel.cpp b/src/core/NEON/kernels/NEFloorKernel.cpp
index 72b652d..872ac26 100644
--- a/src/core/NEON/kernels/NEFloorKernel.cpp
+++ b/src/core/NEON/kernels/NEFloorKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -40,7 +40,7 @@
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
// Auto initialize output
- auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position());
+ auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type());
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output);
diff --git a/src/core/NEON/kernels/NEGEMMInterleave4x4Kernel.cpp b/src/core/NEON/kernels/NEGEMMInterleave4x4Kernel.cpp
index 12755a4..6519a39 100644
--- a/src/core/NEON/kernels/NEGEMMInterleave4x4Kernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMInterleave4x4Kernel.cpp
@@ -44,11 +44,10 @@
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::U8, DataType::S8,
- DataType::QS16, DataType::U16, DataType::S16, DataType::U32, DataType::S32,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::U8, DataType::S8,
+ DataType::U16, DataType::S16, DataType::U32, DataType::S32,
DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
if(output->total_size() != 0)
{
@@ -57,7 +56,6 @@
output_shape.set(1, std::ceil(input->dimension(1) / 4.0f));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
diff --git a/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp b/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp
index cab3c7a..421a6f0 100644
--- a/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp
@@ -43,9 +43,8 @@
{
inline Status validate_arguments(const ITensorInfo *accum, const ITensorInfo *biases)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(biases, accum);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(biases, accum);
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != accum->dimension(0));
@@ -161,33 +160,6 @@
break;
}
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
- case DataType::QS8:
- {
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const qint8x16_t accum = vld1q_qs8(reinterpret_cast<const qint8_t *>(in0_out.ptr()));
- const qint8x16_t biases = vld1q_qs8(reinterpret_cast<const qint8_t *>(in1.ptr()));
-
- vst1q_qs8(reinterpret_cast<qint8_t *>(in0_out.ptr()), vqaddq_qs8(accum, biases));
- },
- in0_out, in1);
- break;
- }
- case DataType::QS16:
- {
- execute_window_loop(window, [&](const Coordinates & id)
- {
- qint16x8x2_t accum = vld2q_s16(reinterpret_cast<const qint16_t *>(in0_out.ptr()));
- const qint16x8x2_t biases = vld2q_s16(reinterpret_cast<const qint16_t *>(in1.ptr()));
-
- accum.val[0] = vqaddq_qs16(accum.val[0], biases.val[0]);
- accum.val[1] = vqaddq_qs16(accum.val[1], biases.val[1]);
-
- vst2q_s16(reinterpret_cast<qint16_t *>(in0_out.ptr()), accum);
- },
- in0_out, in1);
- break;
- }
default:
ARM_COMPUTE_ERROR("Data type not supported");
break;
diff --git a/src/core/NEON/kernels/NEGEMMMatrixAdditionKernel.cpp b/src/core/NEON/kernels/NEGEMMMatrixAdditionKernel.cpp
index dfba743..d025043 100644
--- a/src/core/NEON/kernels/NEGEMMMatrixAdditionKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMMatrixAdditionKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -91,54 +91,6 @@
}
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
-void matrix_addition_qs8(const ITensor *input, ITensor *output, const Window &window, float beta)
-{
- const int fixed_point_position = input->info()->fixed_point_position();
- const qint8x16_t beta_qs8 = vdupq_n_qs8(sqcvt_qs8_f32(beta, fixed_point_position));
-
- Iterator in(input, window);
- Iterator out(output, window);
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const auto in_ptr = reinterpret_cast<const qint8_t *>(in.ptr());
- const auto out_ptr = reinterpret_cast<qint8_t *>(out.ptr());
-
- qint8x16_t alpha_ab = vld1q_qs8(out_ptr);
- const qint8x16_t c = vld1q_qs8(in_ptr);
-
- // Multiply matrix C by its weight and accumulate
- alpha_ab = vqmlaq_qs8(alpha_ab, c, beta_qs8, fixed_point_position);
-
- vst1q_qs8(out_ptr, alpha_ab);
- },
- in, out);
-}
-
-void matrix_addition_qs16(const ITensor *input, ITensor *output, const Window &window, float beta)
-{
- const int fixed_point_position = input->info()->fixed_point_position();
- const qint16x8_t beta_qs16 = vdupq_n_qs16(sqcvt_qs16_f32(beta, fixed_point_position));
-
- Iterator in(input, window);
- Iterator out(output, window);
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const auto in_ptr = reinterpret_cast<const qint16_t *>(in.ptr());
- const auto out_ptr = reinterpret_cast<qint16_t *>(out.ptr());
-
- qint16x8x2_t alpha_ab = vld2q_s16(out_ptr);
- const qint16x8x2_t c = vld2q_s16(in_ptr);
-
- // Multiply matrix C by its weight and accumulate
- alpha_ab.val[0] = vqmlaq_qs16(alpha_ab.val[0], c.val[0], beta_qs16, fixed_point_position);
- alpha_ab.val[1] = vqmlaq_qs16(alpha_ab.val[1], c.val[1], beta_qs16, fixed_point_position);
-
- vst2q_s16(out_ptr, alpha_ab);
- },
- in, out);
-}
} // namespace
NEGEMMMatrixAdditionKernel::NEGEMMMatrixAdditionKernel()
@@ -148,10 +100,9 @@
void NEGEMMMatrixAdditionKernel::configure(const ITensor *input, ITensor *output, float beta)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != output->info()->dimension(0));
ARM_COMPUTE_ERROR_ON(input->info()->dimension(1) != output->info()->dimension(1));
@@ -160,12 +111,6 @@
case DataType::F32:
_func = &matrix_addition_f32;
break;
- case DataType::QS8:
- _func = &matrix_addition_qs8;
- break;
- case DataType::QS16:
- _func = &matrix_addition_qs16;
- break;
case DataType::F16:
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
_func = &matrix_addition_f16;
diff --git a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp b/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp
index 69b052a..196398a 100644
--- a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp
@@ -356,263 +356,6 @@
}
template <bool multiply_alpha>
-void vector_matrix_multiply_qs8(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, const ThreadInfo &info, float alpha)
-{
- const auto width_matrix_b = static_cast<int>(output->info()->dimension(0));
- const auto in_b_stride = static_cast<int>(input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type()));
- const auto num_elems_vec_a = static_cast<int>(input0->info()->dimension(0));
- const int fixed_point_position = input0->info()->fixed_point_position();
-
- // The implementation computes 32 elements per iteration
- const int window_start_x = 32 * info.thread_id;
- const int window_step_x = 32 * info.num_threads;
- // Make sure (window_end_x - window_start_x) is a multiple of window_step_x
- const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x;
-
- Window win_out(window);
- win_out.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
- win_out.set(Window::DimY, Window::Dimension(0, 1, 1));
-
- Window win_a(window);
- win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
- win_a.set(Window::DimY, Window::Dimension(0, 0, 0));
-
- Window win_b;
- // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
- // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
- if(input1->info()->num_dimensions() >= 3)
- {
- win_b = window;
- }
- win_b.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
- win_b.set(Window::DimY, Window::Dimension(0, 1, 1));
-
- Iterator ina(input0, win_a);
- Iterator inb(input1, win_b);
- Iterator out(output, win_out);
-
- execute_window_loop(win_out, [&](const Coordinates & id)
- {
- if(id.x() > width_matrix_b)
- {
- return;
- }
-
- // Reset accumulators
- qint16x8_t acc00_qs16 = vdupq_n_qs16(0);
- qint16x8_t acc01_qs16 = vdupq_n_qs16(0);
- qint16x8_t acc02_qs16 = vdupq_n_qs16(0);
- qint16x8_t acc03_qs16 = vdupq_n_qs16(0);
-
- auto vec_a = reinterpret_cast<const qint8_t *>(ina.ptr());
- auto matrix_b = reinterpret_cast<const qint8_t *>(inb.ptr());
-
- auto vec_a_end_addr = vec_a + num_elems_vec_a;
- for(; vec_a <= (vec_a_end_addr - 2);)
- {
- const qint8x8_t a0 = vld1_dup_qs8(vec_a + 0);
- const qint8x8_t a1 = vld1_dup_qs8(vec_a + 1);
-
- const qint8x8_t b00 = vld1_qs8(matrix_b + 0 + 0 * in_b_stride);
- const qint8x8_t b01 = vld1_qs8(matrix_b + 8 + 0 * in_b_stride);
- const qint8x8_t b02 = vld1_qs8(matrix_b + 16 + 0 * in_b_stride);
- const qint8x8_t b03 = vld1_qs8(matrix_b + 24 + 0 * in_b_stride);
- const qint8x8_t b10 = vld1_qs8(matrix_b + 0 + 1 * in_b_stride);
- const qint8x8_t b11 = vld1_qs8(matrix_b + 8 + 1 * in_b_stride);
- const qint8x8_t b12 = vld1_qs8(matrix_b + 16 + 1 * in_b_stride);
- const qint8x8_t b13 = vld1_qs8(matrix_b + 24 + 1 * in_b_stride);
-
- // First accumulation
- acc00_qs16 = vqmlal_qs8(acc00_qs16, b00, a0, fixed_point_position);
- acc01_qs16 = vqmlal_qs8(acc01_qs16, b01, a0, fixed_point_position);
- acc02_qs16 = vqmlal_qs8(acc02_qs16, b02, a0, fixed_point_position);
- acc03_qs16 = vqmlal_qs8(acc03_qs16, b03, a0, fixed_point_position);
-
- // Second accumulation
- acc00_qs16 = vqmlal_qs8(acc00_qs16, b10, a1, fixed_point_position);
- acc01_qs16 = vqmlal_qs8(acc01_qs16, b11, a1, fixed_point_position);
- acc02_qs16 = vqmlal_qs8(acc02_qs16, b12, a1, fixed_point_position);
- acc03_qs16 = vqmlal_qs8(acc03_qs16, b13, a1, fixed_point_position);
-
- vec_a += 2;
- matrix_b += 2 * in_b_stride;
- }
-
- for(; vec_a < vec_a_end_addr;)
- {
- const qint8x8_t a0 = vld1_dup_qs8(vec_a);
-
- const qint8x8_t b00 = vld1_qs8(matrix_b + 0);
- const qint8x8_t b01 = vld1_qs8(matrix_b + 8);
- const qint8x8_t b02 = vld1_qs8(matrix_b + 16);
- const qint8x8_t b03 = vld1_qs8(matrix_b + 24);
-
- acc00_qs16 = vqmlal_qs8(acc00_qs16, b00, a0, fixed_point_position);
- acc01_qs16 = vqmlal_qs8(acc01_qs16, b01, a0, fixed_point_position);
- acc02_qs16 = vqmlal_qs8(acc02_qs16, b02, a0, fixed_point_position);
- acc03_qs16 = vqmlal_qs8(acc03_qs16, b03, a0, fixed_point_position);
-
- vec_a += 1;
- matrix_b += in_b_stride;
- }
-
- // Convert back to qint8x8_t and saturate
- qint8x8_t acc00_qs8 = vqmovn_qs16(acc00_qs16);
- qint8x8_t acc01_qs8 = vqmovn_qs16(acc01_qs16);
- qint8x8_t acc02_qs8 = vqmovn_qs16(acc02_qs16);
- qint8x8_t acc03_qs8 = vqmovn_qs16(acc03_qs16);
-
- // Multiply by the weight of the matrix product (alpha)
- if(multiply_alpha)
- {
- const qint8x8_t alpha_qs8 = vdup_n_qs8(sqcvt_qs8_f32(alpha, fixed_point_position));
- acc00_qs8 = vqmul_qs8(acc00_qs8, alpha_qs8, fixed_point_position);
- acc01_qs8 = vqmul_qs8(acc01_qs8, alpha_qs8, fixed_point_position);
- acc02_qs8 = vqmul_qs8(acc02_qs8, alpha_qs8, fixed_point_position);
- acc03_qs8 = vqmul_qs8(acc03_qs8, alpha_qs8, fixed_point_position);
- }
-
- const auto mtx_out0 = reinterpret_cast<qint8_t *>(out.ptr());
-
- // Store 8x4 output elements
- vst1_qs8(mtx_out0 + 0, acc00_qs8);
- vst1_qs8(mtx_out0 + 8, acc01_qs8);
- vst1_qs8(mtx_out0 + 16, acc02_qs8);
- vst1_qs8(mtx_out0 + 24, acc03_qs8);
- },
- ina, inb, out);
-}
-
-template <bool multiply_alpha>
-void vector_matrix_multiply_qs16(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, const ThreadInfo &info, float alpha)
-{
- const auto width_matrix_b = static_cast<int>(output->info()->dimension(0));
- const auto in_b_stride = static_cast<int>(input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type()));
- const auto num_elems_vec_a = static_cast<int>(input0->info()->dimension(0));
- const int fixed_point_position = input0->info()->fixed_point_position();
-
- // The implementation computes 16 elements per iteration
- const int window_start_x = 16 * info.thread_id;
- const int window_step_x = 16 * info.num_threads;
- // Make sure (window_end_x - window_start_x) is a multiple of window_step_x
- const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x;
- ARM_COMPUTE_ERROR_ON_MSG((window_end_x - window_start_x) % window_step_x, " (window_end_x - window_start_x) must be multiple of window_step_x");
-
- Window win_out(window);
- win_out.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
- win_out.set(Window::DimY, Window::Dimension(0, 1, 1));
-
- Window win_a(window);
- win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
- win_a.set(Window::DimY, Window::Dimension(0, 0, 0));
-
- Window win_b;
- // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
- // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
- if(input1->info()->num_dimensions() >= 3)
- {
- win_b = window;
- }
- win_b.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
- win_b.set(Window::DimY, Window::Dimension(0, 1, 1));
-
- Iterator ina(input0, win_a);
- Iterator inb(input1, win_b);
- Iterator out(output, win_out);
-
- execute_window_loop(win_out, [&](const Coordinates & id)
- {
- if(id.x() > width_matrix_b)
- {
- return;
- }
-
- // Reset accumulators
- qint32x4_t acc00_qs32 = vdupq_n_qs32(0);
- qint32x4_t acc01_qs32 = vdupq_n_qs32(0);
- qint32x4_t acc02_qs32 = vdupq_n_qs32(0);
- qint32x4_t acc03_qs32 = vdupq_n_qs32(0);
-
- auto vec_a = reinterpret_cast<const qint16_t *>(ina.ptr());
- auto matrix_b = reinterpret_cast<const qint16_t *>(inb.ptr());
-
- auto vec_a_end_addr = vec_a + num_elems_vec_a;
- for(; vec_a <= (vec_a_end_addr - 2);)
- {
- const qint16x4_t a0 = vld1_dup_qs16(vec_a + 0);
- const qint16x4_t a1 = vld1_dup_qs16(vec_a + 1);
-
- const qint16x4_t b00 = vld1_qs16(matrix_b + 0 + 0 * in_b_stride);
- const qint16x4_t b01 = vld1_qs16(matrix_b + 4 + 0 * in_b_stride);
- const qint16x4_t b02 = vld1_qs16(matrix_b + 8 + 0 * in_b_stride);
- const qint16x4_t b03 = vld1_qs16(matrix_b + 12 + 0 * in_b_stride);
- const qint16x4_t b10 = vld1_qs16(matrix_b + 0 + 1 * in_b_stride);
- const qint16x4_t b11 = vld1_qs16(matrix_b + 4 + 1 * in_b_stride);
- const qint16x4_t b12 = vld1_qs16(matrix_b + 8 + 1 * in_b_stride);
- const qint16x4_t b13 = vld1_qs16(matrix_b + 12 + 1 * in_b_stride);
-
- // First accumulation
- acc00_qs32 = vqmlal_qs16(acc00_qs32, b00, a0, fixed_point_position);
- acc01_qs32 = vqmlal_qs16(acc01_qs32, b01, a0, fixed_point_position);
- acc02_qs32 = vqmlal_qs16(acc02_qs32, b02, a0, fixed_point_position);
- acc03_qs32 = vqmlal_qs16(acc03_qs32, b03, a0, fixed_point_position);
-
- // Second accumulation
- acc00_qs32 = vqmlal_qs16(acc00_qs32, b10, a1, fixed_point_position);
- acc01_qs32 = vqmlal_qs16(acc01_qs32, b11, a1, fixed_point_position);
- acc02_qs32 = vqmlal_qs16(acc02_qs32, b12, a1, fixed_point_position);
- acc03_qs32 = vqmlal_qs16(acc03_qs32, b13, a1, fixed_point_position);
-
- vec_a += 2;
- matrix_b += 2 * in_b_stride;
- }
-
- for(; vec_a < vec_a_end_addr;)
- {
- const qint16x4_t a0 = vld1_dup_qs16(vec_a);
-
- const qint16x4_t b00 = vld1_qs16(matrix_b + 0);
- const qint16x4_t b01 = vld1_qs16(matrix_b + 4);
- const qint16x4_t b02 = vld1_qs16(matrix_b + 8);
- const qint16x4_t b03 = vld1_qs16(matrix_b + 12);
-
- acc00_qs32 = vqmlal_qs16(acc00_qs32, b00, a0, fixed_point_position);
- acc01_qs32 = vqmlal_qs16(acc01_qs32, b01, a0, fixed_point_position);
- acc02_qs32 = vqmlal_qs16(acc02_qs32, b02, a0, fixed_point_position);
- acc03_qs32 = vqmlal_qs16(acc03_qs32, b03, a0, fixed_point_position);
-
- vec_a += 1;
- matrix_b += in_b_stride;
- }
-
- // Convert back to qint16x4_t and saturate
- qint16x4_t acc00_qs16 = vqmovn_qs32(acc00_qs32);
- qint16x4_t acc01_qs16 = vqmovn_qs32(acc01_qs32);
- qint16x4_t acc02_qs16 = vqmovn_qs32(acc02_qs32);
- qint16x4_t acc03_qs16 = vqmovn_qs32(acc03_qs32);
-
- // Multiply by the weight of the matrix product (alpha)
- if(multiply_alpha)
- {
- const qint16x4_t alpha_qs16 = vdup_n_qs16(sqcvt_qs16_f32(alpha, fixed_point_position));
- acc00_qs16 = vqmul_qs16(acc00_qs16, alpha_qs16, fixed_point_position);
- acc01_qs16 = vqmul_qs16(acc01_qs16, alpha_qs16, fixed_point_position);
- acc02_qs16 = vqmul_qs16(acc02_qs16, alpha_qs16, fixed_point_position);
- acc03_qs16 = vqmul_qs16(acc03_qs16, alpha_qs16, fixed_point_position);
- }
-
- const auto mtx_out0 = reinterpret_cast<qint16_t *>(out.ptr());
-
- // Store 16x4 output elements
- vst1_qs16(mtx_out0 + 0, acc00_qs16);
- vst1_qs16(mtx_out0 + 4, acc01_qs16);
- vst1_qs16(mtx_out0 + 8, acc02_qs16);
- vst1_qs16(mtx_out0 + 12, acc03_qs16);
- },
- ina, inb, out);
-}
-
-template <bool multiply_alpha>
void matrix_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha)
{
const size_t in_b_stride = input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type());
@@ -1063,361 +806,12 @@
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
}
-template <bool multiply_alpha>
-void matrix_matrix_multiply_qs8(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha)
-{
- const size_t in_b_stride = input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type());
- const size_t out_stride1 = output->info()->strides_in_bytes()[1] / data_size_from_type(output->info()->data_type());
- const size_t out_stride2 = out_stride1 * 2;
- const size_t out_stride3 = out_stride1 * 3;
- const int num_elems_matrix_b_x = input1->info()->dimension(0);
- const int fixed_point_position = input0->info()->fixed_point_position();
- const qint8x8_t alpha_qs8 = vdup_n_qs8(sqcvt_qs8_f32(alpha, fixed_point_position));
- ARM_COMPUTE_UNUSED(alpha_qs8);
-
- // Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the output matrix
- Window win_a(window);
- win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
- win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, std::max(window.y().end() / 4, 1), 1));
-
- Window win_b;
- // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
- // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
- if(input1->info()->num_dimensions() >= 3)
- {
- win_b = window;
- }
- // Set step_x and step_y for matrix B. Scale by a factor of 16 the X range as the input transposed matrix A has 16 times less the cols of the output matrix
- // The step along the x direction is 2 times the in_b_stride because for each iteration we compute 2 blocks of size 16x4
- win_b.set(Window::DimX, Window::Dimension(window.x().start() / 16, window.x().end() / 16, 2 * in_b_stride));
- win_b.set(Window::DimY, Window::Dimension(0, 0, 0));
-
- Iterator ina(input0, win_a);
- Iterator inb(input1, win_b);
- Iterator out(output, window);
-
- // The implementation assumes that the matrix A and Matrix B have been reshaped respectively with NEGEMMInterleave4x4 and NEGEMMTranspose1xW
- // The reshaping of the matrices helps to have a cache friendly implementation and helps to avoid the data re-arrangements needed for computing 16x4 elements per iteration
- // All the values needed for computing a single 32x4 block will be read from consecutive memory positions
- execute_window_loop(window, [&](const Coordinates & id)
- {
- auto mtx_a0 = reinterpret_cast<const qint8_t *>(ina.ptr());
- auto mtx_b0 = reinterpret_cast<const qint8_t *>(inb.ptr());
- auto mtx_b1 = mtx_b0 + in_b_stride;
-
- qint16x8_t acc00_qs16 = vdupq_n_qs16(0);
- qint16x8_t acc10_qs16 = vdupq_n_qs16(0);
- qint16x8_t acc20_qs16 = vdupq_n_qs16(0);
- qint16x8_t acc30_qs16 = vdupq_n_qs16(0);
-
- qint16x8_t acc01_qs16 = vdupq_n_qs16(0);
- qint16x8_t acc11_qs16 = vdupq_n_qs16(0);
- qint16x8_t acc21_qs16 = vdupq_n_qs16(0);
- qint16x8_t acc31_qs16 = vdupq_n_qs16(0);
-
- qint16x8_t acc02_qs16 = vdupq_n_qs16(0);
- qint16x8_t acc12_qs16 = vdupq_n_qs16(0);
- qint16x8_t acc22_qs16 = vdupq_n_qs16(0);
- qint16x8_t acc32_qs16 = vdupq_n_qs16(0);
-
- qint16x8_t acc03_qs16 = vdupq_n_qs16(0);
- qint16x8_t acc13_qs16 = vdupq_n_qs16(0);
- qint16x8_t acc23_qs16 = vdupq_n_qs16(0);
- qint16x8_t acc33_qs16 = vdupq_n_qs16(0);
-
- int k = 0;
- // This for loop performs 2 accumulations
- for(; k <= (num_elems_matrix_b_x - 32); k += 32)
- {
- const qint8x8_t a0 = vld1_dup_qs8(mtx_a0 + 0);
- const qint8x8_t a1 = vld1_dup_qs8(mtx_a0 + 1);
- const qint8x8_t a2 = vld1_dup_qs8(mtx_a0 + 2);
- const qint8x8_t a3 = vld1_dup_qs8(mtx_a0 + 3);
- const qint8x8_t a4 = vld1_dup_qs8(mtx_a0 + 4);
- const qint8x8_t a5 = vld1_dup_qs8(mtx_a0 + 5);
- const qint8x8_t a6 = vld1_dup_qs8(mtx_a0 + 6);
- const qint8x8_t a7 = vld1_dup_qs8(mtx_a0 + 7);
-
- const qint8x8_t b00 = vld1_qs8(mtx_b0 + 0);
- const qint8x8_t b01 = vld1_qs8(mtx_b0 + 8);
- const qint8x8_t b10 = vld1_qs8(mtx_b1 + 0);
- const qint8x8_t b11 = vld1_qs8(mtx_b1 + 8);
-
- // First accumulation
- acc00_qs16 = vqmlal_qs8(acc00_qs16, b00, a0, fixed_point_position);
- acc10_qs16 = vqmlal_qs8(acc10_qs16, b00, a1, fixed_point_position);
- acc20_qs16 = vqmlal_qs8(acc20_qs16, b00, a2, fixed_point_position);
- acc30_qs16 = vqmlal_qs8(acc30_qs16, b00, a3, fixed_point_position);
- acc02_qs16 = vqmlal_qs8(acc02_qs16, b10, a0, fixed_point_position);
- acc12_qs16 = vqmlal_qs8(acc12_qs16, b10, a1, fixed_point_position);
- acc22_qs16 = vqmlal_qs8(acc22_qs16, b10, a2, fixed_point_position);
- acc32_qs16 = vqmlal_qs8(acc32_qs16, b10, a3, fixed_point_position);
-
- const qint8x8_t b02 = vld1_qs8(mtx_b0 + 16);
- const qint8x8_t b03 = vld1_qs8(mtx_b0 + 24);
- const qint8x8_t b12 = vld1_qs8(mtx_b1 + 16);
- const qint8x8_t b13 = vld1_qs8(mtx_b1 + 24);
-
- acc01_qs16 = vqmlal_qs8(acc01_qs16, b01, a0, fixed_point_position);
- acc11_qs16 = vqmlal_qs8(acc11_qs16, b01, a1, fixed_point_position);
- acc21_qs16 = vqmlal_qs8(acc21_qs16, b01, a2, fixed_point_position);
- acc31_qs16 = vqmlal_qs8(acc31_qs16, b01, a3, fixed_point_position);
- acc03_qs16 = vqmlal_qs8(acc03_qs16, b11, a0, fixed_point_position);
- acc13_qs16 = vqmlal_qs8(acc13_qs16, b11, a1, fixed_point_position);
- acc23_qs16 = vqmlal_qs8(acc23_qs16, b11, a2, fixed_point_position);
- acc33_qs16 = vqmlal_qs8(acc33_qs16, b11, a3, fixed_point_position);
-
-#if __arm__
- asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_a0)));
- asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b0)));
- asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b1)));
-#endif /* __arm__ */
-
- // Second accumulation
- acc00_qs16 = vqmlal_qs8(acc00_qs16, b02, a4, fixed_point_position);
- acc10_qs16 = vqmlal_qs8(acc10_qs16, b02, a5, fixed_point_position);
- acc20_qs16 = vqmlal_qs8(acc20_qs16, b02, a6, fixed_point_position);
- acc30_qs16 = vqmlal_qs8(acc30_qs16, b02, a7, fixed_point_position);
- acc01_qs16 = vqmlal_qs8(acc01_qs16, b03, a4, fixed_point_position);
- acc11_qs16 = vqmlal_qs8(acc11_qs16, b03, a5, fixed_point_position);
- acc21_qs16 = vqmlal_qs8(acc21_qs16, b03, a6, fixed_point_position);
- acc31_qs16 = vqmlal_qs8(acc31_qs16, b03, a7, fixed_point_position);
- acc02_qs16 = vqmlal_qs8(acc02_qs16, b12, a4, fixed_point_position);
- acc12_qs16 = vqmlal_qs8(acc12_qs16, b12, a5, fixed_point_position);
- acc22_qs16 = vqmlal_qs8(acc22_qs16, b12, a6, fixed_point_position);
- acc32_qs16 = vqmlal_qs8(acc32_qs16, b12, a7, fixed_point_position);
- acc03_qs16 = vqmlal_qs8(acc03_qs16, b13, a4, fixed_point_position);
- acc13_qs16 = vqmlal_qs8(acc13_qs16, b13, a5, fixed_point_position);
- acc23_qs16 = vqmlal_qs8(acc23_qs16, b13, a6, fixed_point_position);
- acc33_qs16 = vqmlal_qs8(acc33_qs16, b13, a7, fixed_point_position);
-
- mtx_a0 += 8;
- mtx_b0 += 32;
- mtx_b1 += 32;
- }
-
- // This for loop performs the left over accumulations
- for(; k < num_elems_matrix_b_x; k += 16)
- {
- const qint8x8_t a0 = vld1_dup_qs8(mtx_a0 + 0);
- const qint8x8_t a1 = vld1_dup_qs8(mtx_a0 + 1);
- const qint8x8_t a2 = vld1_dup_qs8(mtx_a0 + 2);
- const qint8x8_t a3 = vld1_dup_qs8(mtx_a0 + 3);
-
- const qint8x8_t b00 = vld1_qs8(mtx_b0 + 0);
- const qint8x8_t b01 = vld1_qs8(mtx_b0 + 8);
- const qint8x8_t b10 = vld1_qs8(mtx_b1 + 0);
- const qint8x8_t b11 = vld1_qs8(mtx_b1 + 8);
-
- acc00_qs16 = vqmlal_qs8(acc00_qs16, b00, a0, fixed_point_position);
- acc10_qs16 = vqmlal_qs8(acc10_qs16, b00, a1, fixed_point_position);
- acc20_qs16 = vqmlal_qs8(acc20_qs16, b00, a2, fixed_point_position);
- acc30_qs16 = vqmlal_qs8(acc30_qs16, b00, a3, fixed_point_position);
- acc01_qs16 = vqmlal_qs8(acc01_qs16, b01, a0, fixed_point_position);
- acc11_qs16 = vqmlal_qs8(acc11_qs16, b01, a1, fixed_point_position);
- acc21_qs16 = vqmlal_qs8(acc21_qs16, b01, a2, fixed_point_position);
- acc31_qs16 = vqmlal_qs8(acc31_qs16, b01, a3, fixed_point_position);
- acc02_qs16 = vqmlal_qs8(acc02_qs16, b10, a0, fixed_point_position);
- acc12_qs16 = vqmlal_qs8(acc12_qs16, b10, a1, fixed_point_position);
- acc22_qs16 = vqmlal_qs8(acc22_qs16, b10, a2, fixed_point_position);
- acc32_qs16 = vqmlal_qs8(acc32_qs16, b10, a3, fixed_point_position);
- acc03_qs16 = vqmlal_qs8(acc03_qs16, b11, a0, fixed_point_position);
- acc13_qs16 = vqmlal_qs8(acc13_qs16, b11, a1, fixed_point_position);
- acc23_qs16 = vqmlal_qs8(acc23_qs16, b11, a2, fixed_point_position);
- acc33_qs16 = vqmlal_qs8(acc33_qs16, b11, a3, fixed_point_position);
-
- mtx_a0 += 4;
- mtx_b0 += 16;
- mtx_b1 += 16;
- }
-
- // Convert back to qint8x8_t and saturate
- qint8x8_t acc00_qs8 = vqmovn_qs16(acc00_qs16);
- qint8x8_t acc10_qs8 = vqmovn_qs16(acc10_qs16);
- qint8x8_t acc20_qs8 = vqmovn_qs16(acc20_qs16);
- qint8x8_t acc30_qs8 = vqmovn_qs16(acc30_qs16);
-
- qint8x8_t acc01_qs8 = vqmovn_qs16(acc01_qs16);
- qint8x8_t acc11_qs8 = vqmovn_qs16(acc11_qs16);
- qint8x8_t acc21_qs8 = vqmovn_qs16(acc21_qs16);
- qint8x8_t acc31_qs8 = vqmovn_qs16(acc31_qs16);
-
- qint8x8_t acc02_qs8 = vqmovn_qs16(acc02_qs16);
- qint8x8_t acc12_qs8 = vqmovn_qs16(acc12_qs16);
- qint8x8_t acc22_qs8 = vqmovn_qs16(acc22_qs16);
- qint8x8_t acc32_qs8 = vqmovn_qs16(acc32_qs16);
-
- qint8x8_t acc03_qs8 = vqmovn_qs16(acc03_qs16);
- qint8x8_t acc13_qs8 = vqmovn_qs16(acc13_qs16);
- qint8x8_t acc23_qs8 = vqmovn_qs16(acc23_qs16);
- qint8x8_t acc33_qs8 = vqmovn_qs16(acc33_qs16);
-
- // Multiply by the weight of the matrix product (alpha)
- if(multiply_alpha)
- {
- acc00_qs8 = vqmul_qs8(acc00_qs8, alpha_qs8, fixed_point_position);
- acc10_qs8 = vqmul_qs8(acc10_qs8, alpha_qs8, fixed_point_position);
- acc20_qs8 = vqmul_qs8(acc20_qs8, alpha_qs8, fixed_point_position);
- acc30_qs8 = vqmul_qs8(acc30_qs8, alpha_qs8, fixed_point_position);
- acc01_qs8 = vqmul_qs8(acc01_qs8, alpha_qs8, fixed_point_position);
- acc11_qs8 = vqmul_qs8(acc11_qs8, alpha_qs8, fixed_point_position);
- acc21_qs8 = vqmul_qs8(acc21_qs8, alpha_qs8, fixed_point_position);
- acc31_qs8 = vqmul_qs8(acc31_qs8, alpha_qs8, fixed_point_position);
- acc02_qs8 = vqmul_qs8(acc02_qs8, alpha_qs8, fixed_point_position);
- acc12_qs8 = vqmul_qs8(acc12_qs8, alpha_qs8, fixed_point_position);
- acc22_qs8 = vqmul_qs8(acc22_qs8, alpha_qs8, fixed_point_position);
- acc32_qs8 = vqmul_qs8(acc32_qs8, alpha_qs8, fixed_point_position);
- acc03_qs8 = vqmul_qs8(acc03_qs8, alpha_qs8, fixed_point_position);
- acc13_qs8 = vqmul_qs8(acc13_qs8, alpha_qs8, fixed_point_position);
- acc23_qs8 = vqmul_qs8(acc23_qs8, alpha_qs8, fixed_point_position);
- acc33_qs8 = vqmul_qs8(acc33_qs8, alpha_qs8, fixed_point_position);
- }
-
- const auto mtx_out0 = reinterpret_cast<qint8_t *>(out.ptr());
-
- // Store 32x4 output elements
- vst1_qs8(mtx_out0 + 0, acc00_qs8);
- vst1_qs8(mtx_out0 + 8, acc01_qs8);
- vst1_qs8(mtx_out0 + 16, acc02_qs8);
- vst1_qs8(mtx_out0 + 24, acc03_qs8);
- vst1_qs8(mtx_out0 + out_stride1 + 0, acc10_qs8);
- vst1_qs8(mtx_out0 + out_stride1 + 8, acc11_qs8);
- vst1_qs8(mtx_out0 + out_stride1 + 16, acc12_qs8);
- vst1_qs8(mtx_out0 + out_stride1 + 24, acc13_qs8);
- vst1_qs8(mtx_out0 + out_stride2 + 0, acc20_qs8);
- vst1_qs8(mtx_out0 + out_stride2 + 8, acc21_qs8);
- vst1_qs8(mtx_out0 + out_stride2 + 16, acc22_qs8);
- vst1_qs8(mtx_out0 + out_stride2 + 24, acc23_qs8);
- vst1_qs8(mtx_out0 + out_stride3 + 0, acc30_qs8);
- vst1_qs8(mtx_out0 + out_stride3 + 8, acc31_qs8);
- vst1_qs8(mtx_out0 + out_stride3 + 16, acc32_qs8);
- vst1_qs8(mtx_out0 + out_stride3 + 24, acc33_qs8);
- },
- ina, inb, out);
-}
-
-template <bool multiply_alpha>
-void matrix_matrix_multiply_qs16(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha)
-{
- const size_t in_b_stride = input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type());
- const size_t out_stride1 = output->info()->strides_in_bytes()[1] / data_size_from_type(output->info()->data_type());
- const size_t out_stride2 = out_stride1 * 2;
- const size_t out_stride3 = out_stride1 * 3;
- const int num_elems_matrix_b_x = input1->info()->dimension(0);
- const int fixed_point_position = input0->info()->fixed_point_position();
- const qint16x4_t alpha_qs16 = vdup_n_qs16(sqcvt_qs16_f32(alpha, fixed_point_position));
- ARM_COMPUTE_UNUSED(alpha_qs16);
-
- // Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the output matrix
- Window win_a(window);
- win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
- win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, std::max(window.y().end() / 4, 1), 1));
-
- Window win_b;
- // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
- // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
- if(input1->info()->num_dimensions() >= 3)
- {
- win_b = window;
- }
- // Set step_x and step_y for matrix B. Scale by a factor of 16 the X range as the input transposed matrix A has 16 times less the cols of the output matrix
- win_b.set(Window::DimX, Window::Dimension(window.x().start() / 8, window.x().end() / 8, in_b_stride));
- win_b.set(Window::DimY, Window::Dimension(0, 0, 0));
-
- Iterator ina(input0, win_a);
- Iterator inb(input1, win_b);
- Iterator out(output, window);
-
- // The implementation assumes that the matrix A and Matrix B have been reshaped respectively with NEGEMMInterleave4x4 and NEGEMMTranspose1xW
- // The reshaping of the matrices helps to have a cache friendly implementation and helps to avoid the data re-arrangements needed for computing 8x4 elements per iteration
- // All the values needed for computing a single 8x4 block will be read from consecutive memory positions
- execute_window_loop(window, [&](const Coordinates & id)
- {
- auto mtx_a0 = reinterpret_cast<const qint16_t *>(ina.ptr());
- auto mtx_b0 = reinterpret_cast<const qint16_t *>(inb.ptr());
- auto mtx_b1 = mtx_b0 + in_b_stride;
-
- qint32x4_t acc00_qs32 = vdupq_n_qs32(0);
- qint32x4_t acc10_qs32 = vdupq_n_qs32(0);
- qint32x4_t acc20_qs32 = vdupq_n_qs32(0);
- qint32x4_t acc30_qs32 = vdupq_n_qs32(0);
-
- qint32x4_t acc01_qs32 = vdupq_n_qs32(0);
- qint32x4_t acc11_qs32 = vdupq_n_qs32(0);
- qint32x4_t acc21_qs32 = vdupq_n_qs32(0);
- qint32x4_t acc31_qs32 = vdupq_n_qs32(0);
-
- // This for loop performs 1 accumulation
- for(int k = 0; k <= (num_elems_matrix_b_x - 8); k += 8)
- {
- const qint16x4_t a0 = vld1_dup_qs16(mtx_a0 + 0);
- const qint16x4_t a1 = vld1_dup_qs16(mtx_a0 + 1);
- const qint16x4_t a2 = vld1_dup_qs16(mtx_a0 + 2);
- const qint16x4_t a3 = vld1_dup_qs16(mtx_a0 + 3);
-
- const qint16x4_t b00 = vld1_qs16(mtx_b0 + 0);
- const qint16x4_t b01 = vld1_qs16(mtx_b0 + 4);
-
- acc00_qs32 = vqmlal_qs16(acc00_qs32, b00, a0, fixed_point_position);
- acc10_qs32 = vqmlal_qs16(acc10_qs32, b00, a1, fixed_point_position);
- acc20_qs32 = vqmlal_qs16(acc20_qs32, b00, a2, fixed_point_position);
- acc30_qs32 = vqmlal_qs16(acc30_qs32, b00, a3, fixed_point_position);
- acc01_qs32 = vqmlal_qs16(acc01_qs32, b01, a0, fixed_point_position);
- acc11_qs32 = vqmlal_qs16(acc11_qs32, b01, a1, fixed_point_position);
- acc21_qs32 = vqmlal_qs16(acc21_qs32, b01, a2, fixed_point_position);
- acc31_qs32 = vqmlal_qs16(acc31_qs32, b01, a3, fixed_point_position);
-
- mtx_a0 += 4;
- mtx_b0 += 8;
- mtx_b1 += 8;
- }
-
- // Convert back to qint16x4_t and saturate
- qint16x4_t acc00_qs16 = vqmovn_qs32(acc00_qs32);
- qint16x4_t acc10_qs16 = vqmovn_qs32(acc10_qs32);
- qint16x4_t acc20_qs16 = vqmovn_qs32(acc20_qs32);
- qint16x4_t acc30_qs16 = vqmovn_qs32(acc30_qs32);
-
- qint16x4_t acc01_qs16 = vqmovn_qs32(acc01_qs32);
- qint16x4_t acc11_qs16 = vqmovn_qs32(acc11_qs32);
- qint16x4_t acc21_qs16 = vqmovn_qs32(acc21_qs32);
- qint16x4_t acc31_qs16 = vqmovn_qs32(acc31_qs32);
-
- // Multiply by the weight of the matrix product (alpha)
- if(multiply_alpha)
- {
- acc00_qs16 = vqmul_qs16(acc00_qs16, alpha_qs16, fixed_point_position);
- acc10_qs16 = vqmul_qs16(acc10_qs16, alpha_qs16, fixed_point_position);
- acc20_qs16 = vqmul_qs16(acc20_qs16, alpha_qs16, fixed_point_position);
- acc30_qs16 = vqmul_qs16(acc30_qs16, alpha_qs16, fixed_point_position);
- acc01_qs16 = vqmul_qs16(acc01_qs16, alpha_qs16, fixed_point_position);
- acc11_qs16 = vqmul_qs16(acc11_qs16, alpha_qs16, fixed_point_position);
- acc21_qs16 = vqmul_qs16(acc21_qs16, alpha_qs16, fixed_point_position);
- acc31_qs16 = vqmul_qs16(acc31_qs16, alpha_qs16, fixed_point_position);
- }
-
- const auto mtx_out0 = reinterpret_cast<qint16_t *>(out.ptr());
-
- // Store 8x4 output elements
- vst1_qs16(mtx_out0 + 0, acc00_qs16);
- vst1_qs16(mtx_out0 + 4, acc01_qs16);
- vst1_qs16(mtx_out0 + out_stride1 + 0, acc10_qs16);
- vst1_qs16(mtx_out0 + out_stride1 + 4, acc11_qs16);
- vst1_qs16(mtx_out0 + out_stride2 + 0, acc20_qs16);
- vst1_qs16(mtx_out0 + out_stride2 + 4, acc21_qs16);
- vst1_qs16(mtx_out0 + out_stride3 + 0, acc30_qs16);
- vst1_qs16(mtx_out0 + out_stride3 + 4, acc31_qs16);
- },
- ina, inb, out);
-}
-
inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info)
{
ARM_COMPUTE_UNUSED(alpha);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32, DataType::QS8, DataType::QS16);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output);
if(!is_interleaved)
{
@@ -1428,7 +822,6 @@
ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != output->dimension(0));
ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != output->dimension(1));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, output);
}
}
else
@@ -1467,7 +860,6 @@
}
ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != static_cast<size_t>(m));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, output);
}
}
@@ -1492,16 +884,6 @@
num_elems_processed_per_iteration_x = 16;
break;
}
- case DataType::QS8:
- {
- num_elems_processed_per_iteration_x = 32;
- break;
- }
- case DataType::QS16:
- {
- num_elems_processed_per_iteration_x = 16;
- break;
- }
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
{
@@ -1539,16 +921,6 @@
num_elems_processed_per_iteration_x = 8;
break;
}
- case DataType::QS8:
- {
- num_elems_processed_per_iteration_x = 32;
- break;
- }
- case DataType::QS16:
- {
- num_elems_processed_per_iteration_x = 8;
- break;
- }
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
{
@@ -1638,18 +1010,6 @@
vector_matrix_multiply_f32<false>(_input0, _input1, _output, window, info, _alpha);
break;
}
- case DataType::QS8:
- {
- multiply_alpha ? vector_matrix_multiply_qs8<true>(_input0, _input1, _output, window, info, _alpha) :
- vector_matrix_multiply_qs8<false>(_input0, _input1, _output, window, info, _alpha);
- break;
- }
- case DataType::QS16:
- {
- multiply_alpha ? vector_matrix_multiply_qs16<true>(_input0, _input1, _output, window, info, _alpha) :
- vector_matrix_multiply_qs16<false>(_input0, _input1, _output, window, info, _alpha);
- break;
- }
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
{
@@ -1675,18 +1035,6 @@
matrix_matrix_multiply_f32<false>(_input0, _input1, _output, window, _alpha);
break;
}
- case DataType::QS8:
- {
- multiply_alpha ? matrix_matrix_multiply_qs8<true>(_input0, _input1, _output, window, _alpha) :
- matrix_matrix_multiply_qs8<false>(_input0, _input1, _output, window, _alpha);
- break;
- }
- case DataType::QS16:
- {
- multiply_alpha ? matrix_matrix_multiply_qs16<true>(_input0, _input1, _output, window, _alpha) :
- matrix_matrix_multiply_qs16<false>(_input0, _input1, _output, window, _alpha);
- break;
- }
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
{
diff --git a/src/core/NEON/kernels/NEGEMMMatrixVectorMultiplyKernel.cpp b/src/core/NEON/kernels/NEGEMMMatrixVectorMultiplyKernel.cpp
index c1e975e..8588f43 100644
--- a/src/core/NEON/kernels/NEGEMMMatrixVectorMultiplyKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMMatrixVectorMultiplyKernel.cpp
@@ -177,7 +177,6 @@
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output);
ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(input0->info()->data_type()) && (output->info()->data_type() != DataType::S32));
ARM_COMPUTE_ERROR_ON(input0->info()->dimension(2) != input1->info()->dimension(1));
diff --git a/src/core/NEON/kernels/NEGEMMTranspose1xWKernel.cpp b/src/core/NEON/kernels/NEGEMMTranspose1xWKernel.cpp
index 5d6163d..4517f46 100644
--- a/src/core/NEON/kernels/NEGEMMTranspose1xWKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMTranspose1xWKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2018 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -54,17 +54,15 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::U8, DataType::S8,
- DataType::QS16, DataType::U16, DataType::S16, DataType::U32, DataType::S32,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::U8, DataType::S8,
+ DataType::U16, DataType::S16, DataType::U32, DataType::S32,
DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), get_output_shape(input));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
@@ -102,7 +100,7 @@
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
// Output tensor auto inizialitation if not yet initialized
- auto_init_if_empty(*output->info(), get_output_shape(input->info()), 1, input->info()->data_type(), input->info()->fixed_point_position());
+ auto_init_if_empty(*output->info(), get_output_shape(input->info()), 1, input->info()->data_type());
// Perform validate step
ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info()));
diff --git a/src/core/NEON/kernels/NEIm2ColKernel.cpp b/src/core/NEON/kernels/NEIm2ColKernel.cpp
index 86e3fd7..f03bc49 100644
--- a/src/core/NEON/kernels/NEIm2ColKernel.cpp
+++ b/src/core/NEON/kernels/NEIm2ColKernel.cpp
@@ -24,7 +24,6 @@
#include "arm_compute/core/NEON/kernels/NEIm2ColKernel.h"
#include "arm_compute/core/Error.h"
-#include "arm_compute/core/FixedPoint.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/Size2D.h"
@@ -47,9 +46,8 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
bool has_bias, bool is_fully_connected, bool is_flatten, const Size2D &dilation)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output);
ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::QASYMM8 && has_bias);
ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1));
@@ -90,7 +88,6 @@
int input_stride_x,
int input_stride_y,
int input_stride_z,
- int fixed_point_position,
int pad_value,
int dilation_x,
int dilation_y)
@@ -171,18 +168,7 @@
// Append 1 if the convolution layer has biases
if(has_bias)
{
- if(std::is_same<T, qint8_t>::value)
- {
- *out_ptr = sqcvt_qs8_f32(1.0f, fixed_point_position);
- }
- else if(std::is_same<T, qint16_t>::value)
- {
- *out_ptr = sqcvt_qs16_f32(1.0f, fixed_point_position);
- }
- else
- {
- *out_ptr = static_cast<T>(1);
- }
+ *out_ptr = static_cast<T>(1);
}
}
} // namespace
@@ -251,7 +237,6 @@
input_stride_x,
input_stride_y,
input_stride_z,
- _input->info()->fixed_point_position(),
offset,
_dilation.x(),
_dilation.y());
@@ -294,18 +279,7 @@
// Add bias
if(_has_bias)
{
- if(std::is_same<T, qint8_t>::value)
- {
- *(reinterpret_cast<T *>(out_ptr) + out_width - 1) = sqcvt_qs8_f32(1.0f, _input->info()->fixed_point_position());
- }
- else if(std::is_same<T, qint16_t>::value)
- {
- *(reinterpret_cast<T *>(out_ptr) + out_width - 1) = sqcvt_qs16_f32(1.0f, _input->info()->fixed_point_position());
- }
- else
- {
- *(reinterpret_cast<T *>(out_ptr) + out_width - 1) = static_cast<T>(1);
- }
+ *(reinterpret_cast<T *>(out_ptr) + out_width - 1) = static_cast<T>(1);
}
}
while(in_window.slide_window_slice_3D(in_slice) && out_window.slide_window_slice_1D(out_slice));
@@ -366,12 +340,6 @@
_func = &NEIm2ColKernel::run_reduced<float16_t>;
break;
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
- case DataType::QS8:
- _func = &NEIm2ColKernel::run_reduced<qint8_t>;
- break;
- case DataType::QS16:
- _func = &NEIm2ColKernel::run_reduced<qint16_t>;
- break;
case DataType::QASYMM8:
_func = &NEIm2ColKernel::run_reduced<qasymm8_t>;
break;
@@ -392,12 +360,6 @@
_func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_generic<float16_t, false> : &NEIm2ColKernel::run_generic<float16_t, true>;
break;
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
- case DataType::QS8:
- _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_generic<qint8_t, false> : &NEIm2ColKernel::run_generic<qint8_t, true>;
- break;
- case DataType::QS16:
- _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_generic<qint16_t, false> : &NEIm2ColKernel::run_generic<qint16_t, true>;
- break;
case DataType::QASYMM8:
_func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_generic<qasymm8_t, false> : &NEIm2ColKernel::run_generic<qasymm8_t, true>;
break;
diff --git a/src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp b/src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp
index 91776d8..ed03783 100644
--- a/src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEL2NormalizeLayerKernel.cpp
@@ -103,7 +103,7 @@
Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));
// Output auto initialization if not yet initialized
- auto_init_if_empty(*output, input->tensor_shape(), 1, input->data_type(), input->fixed_point_position());
+ auto_init_if_empty(*output, input->tensor_shape(), 1, input->data_type());
AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
AccessWindowHorizontal sum_access(sum, 0, num_elems_processed_per_iteration_sum);
diff --git a/src/core/NEON/kernels/NEMinMaxLayerKernel.cpp b/src/core/NEON/kernels/NEMinMaxLayerKernel.cpp
index 434f4eb..d93dc09 100644
--- a/src/core/NEON/kernels/NEMinMaxLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEMinMaxLayerKernel.cpp
@@ -68,7 +68,7 @@
TensorShape output_shape = compute_min_max_shape(input);
// Output auto initialization if not yet initialized
- auto_init_if_empty(*output, output_shape, 1, input->data_type(), input->fixed_point_position());
+ auto_init_if_empty(*output, output_shape, 1, input->data_type());
constexpr unsigned int num_elems_processed_per_iteration = 1;
diff --git a/src/core/NEON/kernels/NENormalizationLayerKernel.cpp b/src/core/NEON/kernels/NENormalizationLayerKernel.cpp
index 776cb27..253a93f 100644
--- a/src/core/NEON/kernels/NENormalizationLayerKernel.cpp
+++ b/src/core/NEON/kernels/NENormalizationLayerKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -39,26 +39,17 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *input_squared, const ITensorInfo *output, const NormalizationLayerInfo &norm_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, input_squared, output);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, input_squared);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, input_squared);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(!(norm_info.norm_size() % 2), "Normalization size should be odd");
- if(is_data_type_fixed_point(input->data_type()))
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, input_squared);
- ARM_COMPUTE_RETURN_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.beta(), input);
- ARM_COMPUTE_RETURN_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.kappa(), input);
- ARM_COMPUTE_RETURN_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(norm_info.scale_coeff(), input);
- }
-
// Checks performed when output is configured
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
@@ -162,44 +153,6 @@
}
break;
}
- case DataType::QS8:
- {
- switch(norm_info.type())
- {
- case NormType::IN_MAP_1D:
- _func = &NENormalizationLayerKernel::normalize_fixed_point<DataType::QS8, 0, false>;
- break;
- case NormType::IN_MAP_2D:
- // Normalize over X and Y
- _func = &NENormalizationLayerKernel::normalize_fixed_point<DataType::QS8, 0, true>;
- break;
- case NormType::CROSS_MAP:
- _func = &NENormalizationLayerKernel::normalize_fixed_point<DataType::QS8, 2, false>;
- break;
- default:
- break;
- }
- break;
- }
- case DataType::QS16:
- {
- switch(norm_info.type())
- {
- case NormType::IN_MAP_1D:
- _func = &NENormalizationLayerKernel::normalize_fixed_point<DataType::QS16, 0, false>;
- break;
- case NormType::IN_MAP_2D:
- // Normalize over X and Y
- _func = &NENormalizationLayerKernel::normalize_fixed_point<DataType::QS16, 0, true>;
- break;
- case NormType::CROSS_MAP:
- _func = &NENormalizationLayerKernel::normalize_fixed_point<DataType::QS16, 2, false>;
- break;
- default:
- break;
- }
- break;
- }
default:
ARM_COMPUTE_ERROR("NOT SUPPORTED!");
}
@@ -306,105 +259,6 @@
}
}
-template <DataType dt, unsigned int dim, bool do_2D_norm>
-void NENormalizationLayerKernel::normalize_fixed_point(const Window &window)
-{
- Iterator input(_input, window);
- Iterator input_squared(_input_squared, window);
- Iterator output(_output, window);
-
- const int dim_y = 1;
- const int radius = _norm_info.norm_size() / 2;
- const int total_size = _input->info()->dimension(dim) - 1;
- const int input_squared_stride = _input_squared->info()->strides_in_bytes()[dim];
- // We account padding across X only and we iterate over rows
- const int min_left = (dim == 2) ? 0 : -static_cast<int>(border_size().left);
- const int max_right = (dim == 2) ? total_size : total_size + border_size().left;
- const int min_top = 0;
- const int max_bottom = _input->info()->dimension(dim_y) - 1;
-
- const int fixed_point_position = _input->info()->fixed_point_position();
-
- if(dt == DataType::QS8)
- {
- const qint8x16_t coeff_vec = vdupq_n_qs8_f32(_norm_info.scale_coeff(), fixed_point_position);
- const qint8x16_t beta_vec = vdupq_n_qs8_f32(_norm_info.beta(), fixed_point_position);
- const qint8x16_t kappa_vec = vdupq_n_qs8_f32(_norm_info.kappa(), fixed_point_position);
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- // Get range to normalize
- const int current_row = do_2D_norm ? id[dim_y] : 0;
- const int current_slice = id[dim];
- const int first_row = do_2D_norm ? std::max(current_row - radius, min_top) : 0;
- const int last_row = do_2D_norm ? std::min(current_row + radius, max_bottom) : 0;
- const int first_slice = std::max(current_slice - radius, min_left);
- const int last_slice = std::min(current_slice + radius, max_right);
-
- // Accumulate 2D In-Map values
- qint8x16_t accu = vdupq_n_qs8(0);
- for(int j = first_row; j <= last_row; ++j)
- {
- // Compute row displacement
- const int row = (j - current_row) * _input_squared->info()->strides_in_bytes()[dim_y];
- const uint8_t *const input_squared_ptr = input_squared.ptr() + row - (current_slice * input_squared_stride);
- for(int i = first_slice; i <= last_slice; ++i)
- {
- accu = vqaddq_qs8(accu, vld1q_qs8(reinterpret_cast<const qint8_t *>(input_squared_ptr + i * input_squared_stride)));
- }
- }
-
- // Normalize
- const qint8x16_t accu_scale = vqmlaq_qs8(kappa_vec, coeff_vec, accu, fixed_point_position);
- const qint8x16_t normalized = vqpowq_qs8(accu_scale, beta_vec, fixed_point_position);
- const qint8x16_t normalized_pixel = vdivq_qs8(vld1q_qs8(reinterpret_cast<const qint8_t *>(input.ptr())), normalized, fixed_point_position);
- vst1q_qs8(reinterpret_cast<qint8_t *>(output.ptr()), normalized_pixel);
- },
- input, input_squared, output);
- }
- else if(dt == DataType::QS16)
- {
- const qint16x8_t coeff_vec = vdupq_n_qs16_f32(_norm_info.scale_coeff(), fixed_point_position);
- const qint16x8_t beta_vec = vdupq_n_qs16_f32(_norm_info.beta(), fixed_point_position);
- const qint16x8_t kappa_vec = vdupq_n_qs16_f32(_norm_info.kappa(), fixed_point_position);
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- // Get range to normalize
- const int current_row = do_2D_norm ? id[dim_y] : 0;
- const int current_slice = id[dim];
- const int first_row = do_2D_norm ? std::max(current_row - radius, min_top) : 0;
- const int last_row = do_2D_norm ? std::min(current_row + radius, max_bottom) : 0;
- const int first_slice = std::max(current_slice - radius, min_left);
- const int last_slice = std::min(current_slice + radius, max_right);
-
- // Accumulate 2D In-Map values
- qint16x8_t accu = vdupq_n_qs16(0);
- for(int j = first_row; j <= last_row; ++j)
- {
- // Compute row displacement
- const int row = (j - current_row) * _input_squared->info()->strides_in_bytes()[dim_y];
- const uint8_t *const input_squared_ptr = input_squared.ptr() + row - (current_slice * input_squared_stride);
- for(int i = first_slice; i <= last_slice; ++i)
- {
- accu = vqaddq_qs16(accu, vld1q_qs16(reinterpret_cast<const qint16_t *>(input_squared_ptr + i * input_squared_stride)));
- }
- }
-
- // Normalize
- const qint16x8_t accu_scale = vqmlaq_qs16(kappa_vec, coeff_vec, accu, fixed_point_position);
- const qint16x8_t normalized = vqpowq_qs16(accu_scale, beta_vec, fixed_point_position);
- const qint16x8_t normalized_pixel = vdivq_qs16(vld1q_qs16(reinterpret_cast<const qint16_t *>(input.ptr())), normalized, fixed_point_position);
- vst1q_qs16(reinterpret_cast<qint16_t *>(output.ptr()), normalized_pixel);
- },
- input, input_squared, output);
- }
- else
- {
- ARM_COMPUTE_ERROR("Not supported");
- }
-}
-
Status NENormalizationLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *input_squared, const ITensorInfo *output, const NormalizationLayerInfo norm_info)
{
ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, input_squared, output, norm_info));
diff --git a/src/core/NEON/kernels/NEPermuteKernel.cpp b/src/core/NEON/kernels/NEPermuteKernel.cpp
index ae1d48c..e9bc8ef 100644
--- a/src/core/NEON/kernels/NEPermuteKernel.cpp
+++ b/src/core/NEON/kernels/NEPermuteKernel.cpp
@@ -45,8 +45,8 @@
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const PermutationVector &perm)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8,
- DataType::U16, DataType::S16, DataType::QS16,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8,
+ DataType::U16, DataType::S16,
DataType::U32, DataType::S32,
DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG((perm.num_dimensions() == 3 && !(perm[0] == 2 && perm[1] == 0 && perm[2] == 1) && !(perm[0] == 1 && perm[1] == 2 && perm[2] == 0)),
@@ -59,7 +59,6 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
diff --git a/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp b/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp
index 193ca37..0ec7e82 100644
--- a/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp
+++ b/src/core/NEON/kernels/NEPixelWiseMultiplicationKernel.cpp
@@ -61,9 +61,9 @@
ARM_COMPUTE_UNUSED(overflow_policy);
ARM_COMPUTE_UNUSED(rounding_policy);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::QS8, DataType::QS16, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->data_type() == DataType::U8 && (input1->data_type() != DataType::U8 || input2->data_type() != DataType::U8),
"Output can only be U8 if both inputs are U8");
@@ -71,14 +71,6 @@
ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), "Wrong shape for output");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
- if(is_data_type_fixed_point(input1->data_type()) || is_data_type_fixed_point(input2->data_type()) || is_data_type_fixed_point(output->data_type()))
- {
- // Check that all data types are the same and all fixed-point positions are the same
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input1, input2, output);
- // Check if scale is representable in fixed-point with the provided settings
- ARM_COMPUTE_RETURN_ERROR_ON_VALUE_NOT_REPRESENTABLE_IN_FIXED_POINT(scale, input1);
- }
-
if(std::abs(scale - scale255_constant) < 0.00001f)
{
ARM_COMPUTE_RETURN_ERROR_ON(rounding_policy != RoundingPolicy::TO_NEAREST_UP && rounding_policy != RoundingPolicy::TO_NEAREST_EVEN);
@@ -120,11 +112,6 @@
{
set_format_if_unknown(*output, Format::F16);
}
- else if(input1->data_type() == DataType::QS8 && input2->data_type() == DataType::QS8)
- {
- set_data_type_if_unknown(*output, DataType::QS8);
- set_fixed_point_position_if_zero(*output, input1->fixed_point_position());
- }
}
// Configure kernel window
@@ -220,105 +207,6 @@
}
template <bool is_scale255, bool is_sat>
-void mul_QS8_QS8_QS8_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n, int fixed_point_position)
-{
- const auto output = static_cast<qint8_t *__restrict>(output_ptr);
-
- const qint8x16_t ta1 = vld1q_qs8(static_cast<const qint8_t *__restrict>(input1_ptr));
- const qint8x16_t ta2 = vld1q_qs8(static_cast<const qint8_t *__restrict>(input2_ptr));
-
- if(is_scale255)
- {
- qint16x8_t tmp1_high = vmovl_s8(vget_high_s8(ta1));
- qint16x8_t tmp1_low = vmovl_s8(vget_low_s8(ta1));
- const qint16x8_t tmp2_high = vmovl_s8(vget_high_s8(ta2));
- const qint16x8_t tmp2_low = vmovl_s8(vget_low_s8(ta2));
-
- const float32x4x2_t scale255_f32 =
- {
- {
- scale255_constant_f32q,
- scale255_constant_f32q
- }
- };
- const qint16x8_t scale255 = vqcvtq_qs16_f32(scale255_f32, fixed_point_position);
-
- tmp1_high = vmulq_qs16(tmp1_high, tmp2_high, fixed_point_position);
- tmp1_low = vmulq_qs16(tmp1_low, tmp2_low, fixed_point_position);
- tmp1_high = vmulq_qs16(tmp1_high, scale255, fixed_point_position);
- tmp1_low = vmulq_qs16(tmp1_low, scale255, fixed_point_position);
-
- if(is_sat)
- {
- vst1q_qs8(output, vcombine_s8(vqmovn_s16(tmp1_low), vqmovn_s16(tmp1_high)));
- }
- else
- {
- vst1q_qs8(output, vcombine_s8(vmovn_s16(tmp1_low), vmovn_s16(tmp1_high)));
- }
- }
- else
- {
- const qint8x16_t vn = vdupq_n_s8(-n);
- qint8x16_t res = ta2;
-
- if(is_sat)
- {
- res = vqshlq_s8(vqmulq_qs8(ta1, res, fixed_point_position), vn);
- }
- else
- {
- res = vshlq_s8(vmulq_qs8(ta1, res, fixed_point_position), vn);
- }
- vst1q_qs8(output, res);
- }
-}
-
-template <bool is_scale255, bool is_sat>
-void mul_QS16_QS16_QS16_n(const void *__restrict input1_ptr, const void *__restrict input2_ptr, void *__restrict output_ptr, int n, int fixed_point_position)
-{
- const qint16x8x2_t ta1 = vld2q_qs16(static_cast<const qint16_t *__restrict>(input1_ptr));
- qint16x8x2_t res = vld2q_qs16(static_cast<const qint16_t *__restrict>(input2_ptr));
-
- if(is_scale255)
- {
- const float32x4x2_t scale255_f32 =
- {
- {
- scale255_constant_f32q,
- scale255_constant_f32q
- }
- };
- const qint16x8_t scale255 = vqcvtq_qs16_f32(scale255_f32, fixed_point_position);
- if(is_sat)
- {
- res.val[0] = vqmulq_qs16(vqmulq_qs16(ta1.val[0], res.val[0], fixed_point_position), scale255, fixed_point_position);
- res.val[1] = vqmulq_qs16(vqmulq_qs16(ta1.val[1], res.val[1], fixed_point_position), scale255, fixed_point_position);
- }
- else
- {
- res.val[0] = vmulq_qs16(vmulq_qs16(ta1.val[0], res.val[0], fixed_point_position), scale255, fixed_point_position);
- res.val[1] = vmulq_qs16(vmulq_qs16(ta1.val[1], res.val[1], fixed_point_position), scale255, fixed_point_position);
- }
- }
- else
- {
- const qint16x8_t vn = vdupq_n_s16(-n);
- if(is_sat)
- {
- res.val[0] = vqshlq_s16(vqmulq_qs16(ta1.val[0], res.val[0], fixed_point_position), vn);
- res.val[1] = vqshlq_s16(vqmulq_qs16(ta1.val[1], res.val[1], fixed_point_position), vn);
- }
- else
- {
- res.val[0] = vshlq_s16(vmulq_qs16(ta1.val[0], res.val[0], fixed_point_position), vn);
- res.val[1] = vshlq_s16(vmulq_qs16(ta1.val[1], res.val[1], fixed_point_position), vn);
- }
- }
- vst2q_s16(static_cast<qint16_t *__restrict>(output_ptr), res);
-}
-
-template <bool is_scale255, bool is_sat>
inline int16x8_t mul_S16_S16_S16_n_loop(const int16x8_t &input1, const int16x8_t &input2, int n)
{
int32x4_t tmp1_high = vmovl_s16(vget_high_s16(input1));
@@ -529,7 +417,7 @@
} // namespace
NEPixelWiseMultiplicationKernel::NEPixelWiseMultiplicationKernel()
- : _func_float(nullptr), _func_int(nullptr), _func_q_int(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _scale{ 0 }, _scale_exponent{ 0 }
+ : _func_float(nullptr), _func_int(nullptr), _input1(nullptr), _input2(nullptr), _output(nullptr), _scale{ 0 }, _scale_exponent{ 0 }
{
}
@@ -550,7 +438,6 @@
_scale = scale;
_scale_exponent = 0;
_func_int = nullptr;
- _func_q_int = nullptr;
_func_float = nullptr;
bool is_scale_255 = false;
@@ -630,28 +517,6 @@
_func_int = is_sat ? &mul_U8_U8_S16_n<false, true> : &mul_U8_U8_S16_n<false, false>;
}
}
- else if(DataType::QS8 == dt_input1 && DataType::QS8 == dt_input2 && DataType::QS8 == dt_output)
- {
- if(is_scale_255)
- {
- _func_q_int = is_sat ? &mul_QS8_QS8_QS8_n<true, true> : &mul_QS8_QS8_QS8_n<true, false>;
- }
- else
- {
- _func_q_int = is_sat ? &mul_QS8_QS8_QS8_n<false, true> : &mul_QS8_QS8_QS8_n<false, false>;
- }
- }
- else if(DataType::QS16 == dt_input1 && DataType::QS16 == dt_input2 && DataType::QS16 == dt_output)
- {
- if(is_scale_255)
- {
- _func_q_int = is_sat ? &mul_QS16_QS16_QS16_n<true, true> : &mul_QS16_QS16_QS16_n<true, false>;
- }
- else
- {
- _func_q_int = is_sat ? &mul_QS16_QS16_QS16_n<false, true> : &mul_QS16_QS16_QS16_n<false, false>;
- }
- }
else if(DataType::F16 == dt_input1 && DataType::F16 == dt_input2 && DataType::F16 == dt_output)
{
_func_float = &mul_F16_F16_F16_n<false, false>;
@@ -724,17 +589,6 @@
},
input1, input2, output);
}
- else if(_func_q_int != nullptr)
- {
- int fixed_point_position = _input1->info()->fixed_point_position();
- execute_window_loop(collapsed, [&](const Coordinates & id)
- {
- (*_func_q_int)(input1.ptr(), input2.ptr(), output.ptr(), _scale_exponent, fixed_point_position);
- collapsed.slide_window_slice_3D(slice_input1);
- collapsed.slide_window_slice_3D(slice_input2);
- },
- input1, input2, output);
- }
else
{
ARM_COMPUTE_ERROR_ON(_func_float == nullptr);
diff --git a/src/core/NEON/kernels/NEPoolingLayerKernel.cpp b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp
index 7877cf5..e586b72 100644
--- a/src/core/NEON/kernels/NEPoolingLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEPoolingLayerKernel.cpp
@@ -25,7 +25,6 @@
#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/Error.h"
-#include "arm_compute/core/FixedPoint.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/NEAsymm.h"
@@ -79,32 +78,6 @@
return 1.f / ((end_y - start_y) * (end_x - start_x));
}
-inline qint8_t calculate_avg_scale_q8(const Coordinates &id, int pool_size, int upper_bound_w, int upper_bound_h,
- int pad_x, int pad_y, int stride_x, int stride_y, int fixed_point_position)
-{
- static const std::array<qint8_t, 10> scale_values_q8 =
- { { 0x0, 0x0, 0x40, 0x2A, 0x20, 0x19, 0x15, 0x12, 0x10, 0xE } };
- const int start_x = id.x() * stride_x - pad_x;
- const int start_y = id.y() * stride_y - pad_y;
- const int end_x = std::min(start_x + pool_size, upper_bound_w);
- const int end_y = std::min(start_y + pool_size, upper_bound_h);
- const int val = ((end_y - start_y) * (end_x - start_x));
- return sshr_qs8(scale_values_q8[val], (7 - fixed_point_position));
-}
-
-inline qint16_t calculate_avg_scale_q16(const Coordinates &id, int pool_size, int upper_bound_w, int upper_bound_h,
- int pad_x, int pad_y, int stride_x, int stride_y, int fixed_point_position)
-{
- static std::array<qint16_t, 10> scale_values_q16 =
- { { 0x0, 0x0, 0x4000, 0x2AAB, 0x2000, 0x199A, 0x1555, 0x1249, 0x1000, 0xE38 } };
- const int start_x = id.x() * stride_x - pad_x;
- const int start_y = id.y() * stride_y - pad_y;
- const int end_x = std::min(start_x + pool_size, upper_bound_w);
- const int end_y = std::min(start_y + pool_size, upper_bound_h);
- const int val = ((end_y - start_y) * (end_x - start_x));
- return sshr_qs16(scale_values_q16[val], (15 - fixed_point_position));
-}
-
template <bool exclude_padding>
inline void scale_vector_s16x8(uint16x8_t &v, const Coordinates &id, int id_offset, int step,
const int pool_size, const int upper_bound_w, const int upper_bound_h,
@@ -163,22 +136,18 @@
int pool_stride_y = 0;
PoolingType pool_type = pool_info.pool_type();
const PadStrideInfo pad_stride_info = pool_info.pad_stride_info();
- const bool exclude_padding = pool_info.exclude_padding();
std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
static const std::set<int> supported_pool_sizes = { 2, 3 };
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON(pool_type == PoolingType::L2 && is_data_type_quantized(input->data_type()));
ARM_COMPUTE_RETURN_ERROR_ON((supported_pool_sizes.find(pool_size_x) == supported_pool_sizes.end()) && ((input->data_type() != DataType::F32) && (input->data_type() != DataType::QASYMM8))
&& (pool_type != PoolingType::MAX));
- ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_fixed_point(input->data_type()) && pool_stride_x > 2);
- ARM_COMPUTE_RETURN_ERROR_ON(exclude_padding && is_data_type_fixed_point(input->data_type()));
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
ARM_COMPUTE_RETURN_ERROR_ON((output->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH)) != pooled_w)
|| (output->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT)) != pooled_h));
@@ -236,22 +205,6 @@
{
switch(input->data_type())
{
- case DataType::QS8:
- num_elems_read_per_iteration = 16;
- switch(pool_size_x)
- {
- case 2:
- num_elems_horizontal_window = (pool_stride_x == 2) ? 8 : 16;
- num_elems_processed_per_iteration = (pool_stride_x == 2) ? 8 : 15;
- break;
- case 3:
- num_elems_horizontal_window = (pool_stride_x == 2) ? 8 : 16;
- num_elems_processed_per_iteration = (pool_stride_x == 2) ? 7 : 14;
- break;
- default:
- break;
- }
- break;
case DataType::QASYMM8:
if(is_nhwc)
{
@@ -274,22 +227,6 @@
break;
}
break;
- case DataType::QS16:
- num_elems_read_per_iteration = 8;
- switch(pool_size_x)
- {
- case 2:
- num_elems_horizontal_window = (pool_stride_x == 2) ? 4 : 8;
- num_elems_processed_per_iteration = (pool_stride_x == 2) ? 4 : 7;
- break;
- case 3:
- num_elems_horizontal_window = (pool_stride_x == 2) ? 4 : 8;
- num_elems_processed_per_iteration = (pool_stride_x == 2) ? 3 : 6;
- break;
- default:
- break;
- }
- break;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
if(is_nhwc)
@@ -462,64 +399,7 @@
const DataType data_type = input->info()->data_type();
const bool is_nchw = data_layout == DataLayout::NCHW;
- // Select appropriate function
- if(data_type == DataType::QS8)
- {
- if(_is_square)
- {
- switch(pool_size_x)
- {
- case 2:
- switch(pool_type)
- {
- case PoolingType::AVG:
- _func = &NEPoolingLayerKernel::pooling2_q8_nchw<PoolingType::AVG>;
- break;
- case PoolingType::MAX:
- _func = &NEPoolingLayerKernel::pooling2_q8_nchw<PoolingType::MAX>;
- break;
- default:
- ARM_COMPUTE_ERROR("Unsupported pooling type!");
- }
- break;
- case 3:
- switch(pool_type)
- {
- case PoolingType::AVG:
- _func = &NEPoolingLayerKernel::pooling3_q8_nchw<PoolingType::AVG>;
- break;
- case PoolingType::MAX:
- _func = &NEPoolingLayerKernel::pooling3_q8_nchw<PoolingType::MAX>;
- break;
- default:
- ARM_COMPUTE_ERROR("Unsupported pooling type!");
- }
- break;
- default:
- switch(pool_type)
- {
- case PoolingType::MAX:
- _func = &NEPoolingLayerKernel::poolingMxN_q8_nchw<PoolingType::MAX>;
- break;
- default:
- ARM_COMPUTE_ERROR("Unsupported pooling type!");
- }
- break;
- }
- }
- else
- {
- switch(pool_type)
- {
- case PoolingType::MAX:
- _func = &NEPoolingLayerKernel::poolingMxN_q8_nchw<PoolingType::MAX>;
- break;
- default:
- ARM_COMPUTE_ERROR("Unsupported pooling type!");
- }
- }
- }
- else if(data_type == DataType::QASYMM8)
+ if(data_type == DataType::QASYMM8)
{
if(pool_size_x == 2 && pool_stride_x < 3 && _is_square)
{
@@ -606,62 +486,6 @@
}
}
}
- else if(data_type == DataType::QS16)
- {
- if(_is_square)
- {
- switch(pool_size_x)
- {
- case 2:
- switch(pool_type)
- {
- case PoolingType::AVG:
- _func = &NEPoolingLayerKernel::pooling2_q16_nchw<PoolingType::AVG>;
- break;
- case PoolingType::MAX:
- _func = &NEPoolingLayerKernel::pooling2_q16_nchw<PoolingType::MAX>;
- break;
- default:
- ARM_COMPUTE_ERROR("Unsupported pooling type!");
- }
- break;
- case 3:
- switch(pool_type)
- {
- case PoolingType::AVG:
- _func = &NEPoolingLayerKernel::pooling3_q16_nchw<PoolingType::AVG>;
- break;
- case PoolingType::MAX:
- _func = &NEPoolingLayerKernel::pooling3_q16_nchw<PoolingType::MAX>;
- break;
- default:
- ARM_COMPUTE_ERROR("Unsupported pooling type!");
- }
- break;
- default:
- switch(pool_type)
- {
- case PoolingType::MAX:
- _func = &NEPoolingLayerKernel::poolingMxN_q16_nchw<PoolingType::MAX>;
- break;
- default:
- ARM_COMPUTE_ERROR("Unsupported pooling type!");
- }
- break;
- }
- }
- else
- {
- switch(pool_type)
- {
- case PoolingType::MAX:
- _func = &NEPoolingLayerKernel::poolingMxN_q16_nchw<PoolingType::MAX>;
- break;
- default:
- ARM_COMPUTE_ERROR("Unsupported pooling type!");
- }
- }
- }
else if(data_type == DataType::F16)
{
if(_is_square)
@@ -1022,71 +846,6 @@
INEKernel::configure(win_config.second);
}
-template <PoolingType pooling_type>
-void NEPoolingLayerKernel::pooling2_q8_nchw(const Window &window_input, const Window &window)
-{
- Iterator input(_input, window_input);
- Iterator output(_output, window);
-
- const int fixed_point_position = _input->info()->fixed_point_position();
- constexpr int pool_size = 2;
- int pool_stride_x = 0;
- int pool_stride_y = 0;
- const int pool_pad_right = _pool_info.pad_stride_info().pad_right();
- const int pool_pad_top = _pool_info.pad_stride_info().pad_top();
- const int pool_pad_left = _pool_info.pad_stride_info().pad_left();
- const int pool_pad_bottom = _pool_info.pad_stride_info().pad_bottom();
- std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
- const int upper_bound_w = _input->info()->dimension(0) + pool_pad_right;
- const int upper_bound_h = _input->info()->dimension(1) + pool_pad_bottom;
-
- const uint8_t *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)));
- const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1));
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const auto top_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_top_ptr + input.offset()));
- const auto bottom_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_bottom_ptr + input.offset()));
- qint8x8_t lower_res = {};
- qint8x8_t upper_res = {};
- if(pooling_type == PoolingType::AVG)
- {
- // Calculate scale
- const qint8_t scale = calculate_avg_scale_q8(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y, fixed_point_position);
- const qint8x8_t scale_vec = vdup_n_qs8(scale);
-
- // Perform pooling
- const qint8x16_t sum_data = vqaddq_qs8(top_data, bottom_data);
- lower_res = vqmul_qs8(vpadd_s8(vget_low_s8(sum_data), vget_high_s8(sum_data)), scale_vec, fixed_point_position);
- if(pool_stride_x == 1)
- {
- const qint8x16_t sum_data_shifted = vextq_s8(sum_data, sum_data, 1);
- upper_res = vqmul_qs8(vpadd_s8(vget_low_s8(sum_data_shifted), vget_high_s8(sum_data_shifted)), scale_vec, fixed_point_position);
- }
- }
- else
- {
- const qint8x16_t max_data = vmaxq_s8(top_data, bottom_data);
- lower_res = vpmax_s8(vget_low_s8(max_data), vget_high_s8(max_data));
- if(pool_stride_x == 1)
- {
- const qint8x16_t max_data_shifted = vextq_s8(max_data, max_data, 1);
- upper_res = vpmax_s8(vget_low_s8(max_data_shifted), vget_high_s8(max_data_shifted));
- }
- }
- if(pool_stride_x == 1)
- {
- const qint8x8x2_t res = { { lower_res, upper_res } };
- vst2_s8(reinterpret_cast<qint8_t *>(output.ptr()), res);
- }
- else
- {
- vst1_qs8(reinterpret_cast<qint8_t *>(output.ptr()), lower_res);
- }
- },
- input, output);
-}
-
template <PoolingType pooling_type, bool exclude_padding>
void NEPoolingLayerKernel::pooling2_qasymm8_nchw(const Window &window_input, const Window &window)
{
@@ -1201,71 +960,6 @@
input, output);
}
-template <PoolingType pooling_type>
-void NEPoolingLayerKernel::pooling2_q16_nchw(const Window &window_input, const Window &window)
-{
- Iterator input(_input, window_input);
- Iterator output(_output, window);
-
- const int fixed_point_position = _input->info()->fixed_point_position();
- constexpr int pool_size = 2;
- const int pool_pad_right = _pool_info.pad_stride_info().pad_right();
- const int pool_pad_top = _pool_info.pad_stride_info().pad_top();
- const int pool_pad_left = _pool_info.pad_stride_info().pad_left();
- const int pool_pad_bottom = _pool_info.pad_stride_info().pad_bottom();
- int pool_stride_x = 0;
- int pool_stride_y = 0;
- std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
- const int upper_bound_w = _input->info()->dimension(0) + pool_pad_right;
- const int upper_bound_h = _input->info()->dimension(1) + pool_pad_bottom;
-
- const unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)));
- const unsigned char *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1));
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const auto top_data = vld1q_qs16(reinterpret_cast<const qint16_t *>(input_top_ptr + input.offset()));
- const auto bottom_data = vld1q_qs16(reinterpret_cast<const qint16_t *>(input_bottom_ptr + input.offset()));
- qint16x4_t lower_res = {};
- qint16x4_t upper_res = {};
- if(pooling_type == PoolingType::AVG)
- {
- // Calculate scale
- const qint16_t scale = calculate_avg_scale_q16(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y, fixed_point_position);
- const qint16x4_t scale_vec = vdup_n_qs16(scale);
-
- // Perform pooling
- const qint16x8_t sum_data = vqaddq_qs16(top_data, bottom_data);
- lower_res = vqmul_qs16(vpadd_s16(vget_low_s16(sum_data), vget_high_s16(sum_data)), scale_vec, fixed_point_position);
- if(pool_stride_x == 1)
- {
- const qint16x8_t sum_data_shifted = vextq_s16(sum_data, sum_data, 1);
- upper_res = vqmul_qs16(vpadd_s16(vget_low_s16(sum_data_shifted), vget_high_s16(sum_data_shifted)), scale_vec, fixed_point_position);
- }
- }
- else
- {
- const qint16x8_t max_data = vmaxq_s16(top_data, bottom_data);
- lower_res = vpmax_s16(vget_low_s16(max_data), vget_high_s16(max_data));
- if(pool_stride_x == 1)
- {
- const qint16x8_t max_data_shifted = vextq_s16(max_data, max_data, 1);
- upper_res = vpmax_s16(vget_low_s16(max_data_shifted), vget_high_s16(max_data_shifted));
- }
- }
- if(pool_stride_x == 1)
- {
- const qint16x4x2_t res = { { lower_res, upper_res } };
- vst2_s16(reinterpret_cast<qint16_t *>(output.ptr()), res);
- }
- else
- {
- vst1_qs16(reinterpret_cast<qint16_t *>(output.ptr()), lower_res);
- }
- },
- input, output);
-}
-
template <PoolingType pooling_type, bool exclude_padding>
void NEPoolingLayerKernel::pooling3_f16_nchw(const Window &window_input, const Window &window)
{
@@ -1461,82 +1155,6 @@
input, output);
}
-template <PoolingType pooling_type>
-void NEPoolingLayerKernel::pooling3_q8_nchw(const Window &window_input, const Window &window)
-{
- Iterator input(_input, window_input);
- Iterator output(_output, window);
-
- const int fixed_point_position = _input->info()->fixed_point_position();
- constexpr int pool_size = 3;
- const int pool_pad_right = _pool_info.pad_stride_info().pad_right();
- const int pool_pad_top = _pool_info.pad_stride_info().pad_top();
- const int pool_pad_left = _pool_info.pad_stride_info().pad_left();
- const int pool_pad_bottom = _pool_info.pad_stride_info().pad_bottom();
- int pool_stride_x = 0;
- int pool_stride_y = 0;
- std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
- const int upper_bound_w = _input->info()->dimension(0) + pool_pad_right;
- const int upper_bound_h = _input->info()->dimension(1) + pool_pad_bottom;
-
- const uint8_t *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)));
- const uint8_t *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1));
- const uint8_t *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 2));
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const auto top_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_top_ptr + input.offset()));
- const auto middle_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_middle_ptr + input.offset()));
- const auto bottom_data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input_bottom_ptr + input.offset()));
- qint8x8_t res = {};
- if(pooling_type == PoolingType::AVG)
- {
- // Calculate scale
- const qint8_t scale = calculate_avg_scale_q8(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y, fixed_point_position);
-
- // Perform pooling for stride 2
- const qint8x16_t sum_data = vqaddq_qs8(vqaddq_qs8(top_data, bottom_data), middle_data);
- const qint8x16_t sum_data2 = vextq_s8(sum_data, sum_data, 1);
- const qint8x16_t sum_data3 = vextq_s8(sum_data, sum_data, 2);
- const qint8x16_t final_sum = vqaddq_qs8(vqaddq_qs8(sum_data, sum_data2), sum_data3);
- if(pool_stride_x == 2)
- {
- const qint8x8x2_t table = { { vget_low_s8(final_sum), vget_high_s8(final_sum) } };
- static const qint8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 };
- const qint8x8_t scale_vec = vdup_n_qs8(scale);
- res = vtbl2_s8(table, lookup_val);
- res = vqmul_qs8(res, scale_vec, fixed_point_position);
- vst1_qs8(reinterpret_cast<qint8_t *>(output.ptr()), res);
- }
- else
- {
- const qint8x16_t scale_vec = vdupq_n_qs8(scale);
- vst1q_qs8(reinterpret_cast<qint8_t *>(output.ptr()), vqmulq_qs8(final_sum, scale_vec, fixed_point_position));
- }
- }
- else
- {
- const qint8x16_t max_data = vmaxq_s8(vmaxq_s8(top_data, bottom_data), middle_data);
- const qint8x16_t max_data2 = vextq_s8(max_data, max_data, 1);
- const qint8x16_t max_data3 = vextq_s8(max_data, max_data, 2);
- const qint8x16_t final_max = vmaxq_s8(vmaxq_s8(max_data, max_data2), max_data3);
-
- if(pool_stride_x == 2)
- {
- const qint8x8x2_t table = { { vget_low_s8(final_max), vget_high_s8(final_max) } };
- static const qint8x8_t lookup_val = { 0, 2, 4, 6, 8, 10, 12, 14 };
- res = vtbl2_s8(table, lookup_val);
- vst1_qs8(reinterpret_cast<qint8_t *>(output.ptr()), res);
- }
- else
- {
- vst1q_qs8(reinterpret_cast<qint8_t *>(output.ptr()), final_max);
- }
- }
- },
- input, output);
-}
-
template <PoolingType pooling_type, bool exclude_padding>
void NEPoolingLayerKernel::pooling3_qasymm8_nchw(const Window &window_input, const Window &window)
{
@@ -1657,77 +1275,6 @@
input, output);
}
-template <PoolingType pooling_type>
-void NEPoolingLayerKernel::pooling3_q16_nchw(const Window &window_input, const Window &window)
-{
- Iterator input(_input, window_input);
- Iterator output(_output, window);
-
- const int fixed_point_position = _input->info()->fixed_point_position();
- constexpr int pool_size = 3;
- const int pool_pad_right = _pool_info.pad_stride_info().pad_right();
- const int pool_pad_top = _pool_info.pad_stride_info().pad_top();
- const int pool_pad_left = _pool_info.pad_stride_info().pad_left();
- const int pool_pad_bottom = _pool_info.pad_stride_info().pad_bottom();
- int pool_stride_x = 0;
- int pool_stride_y = 0;
- std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
- const int upper_bound_w = _input->info()->dimension(0) + pool_pad_right;
- const int upper_bound_h = _input->info()->dimension(1) + pool_pad_bottom;
-
- const unsigned char *const input_top_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top)));
- const unsigned char *const input_middle_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 1));
- const unsigned char *const input_bottom_ptr = _input->ptr_to_element(Coordinates(-static_cast<int>(pool_pad_left), -static_cast<int>(pool_pad_top) + 2));
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- const auto top_data = vld1q_qs16(reinterpret_cast<const qint16_t *>(input_top_ptr + input.offset()));
- const auto middle_data = vld1q_qs16(reinterpret_cast<const qint16_t *>(input_middle_ptr + input.offset()));
- const auto bottom_data = vld1q_qs16(reinterpret_cast<const qint16_t *>(input_bottom_ptr + input.offset()));
-
- if(pooling_type == PoolingType::AVG)
- {
- // Calculate scale
- const qint16_t scale = calculate_avg_scale_q16(id, pool_size, upper_bound_w, upper_bound_h, pool_pad_left, pool_pad_top, pool_stride_x, pool_stride_y, fixed_point_position);
-
- // Perform pooling for stride 2
- const qint16x8_t sum_data = vqaddq_qs16(vqaddq_qs16(top_data, bottom_data), middle_data);
- const qint16x8_t sum_data2 = vextq_s16(sum_data, sum_data, 1);
- const qint16x8_t sum_data3 = vextq_s16(sum_data, sum_data, 2);
- const qint16x8_t final_sum = vqaddq_qs16(vqaddq_qs16(sum_data, sum_data2), sum_data3);
- if(pool_stride_x == 2)
- {
- const qint16x4_t tmp = { vgetq_lane_s16(final_sum, 0), vgetq_lane_s16(final_sum, 2), vgetq_lane_s16(final_sum, 4), vgetq_lane_s16(final_sum, 6) };
- const qint16x4_t scale_vec = vdup_n_qs16(scale);
- vst1_qs16(reinterpret_cast<qint16_t *>(output.ptr()), vqmul_qs16(tmp, scale_vec, fixed_point_position));
- }
- else
- {
- const qint16x8_t scale_vec = vdupq_n_qs16(scale);
- vst1q_qs16(reinterpret_cast<qint16_t *>(output.ptr()), vqmulq_qs16(final_sum, scale_vec, fixed_point_position));
- }
- }
- else
- {
- const qint16x8_t max_data = vmaxq_s16(vmaxq_s16(top_data, bottom_data), middle_data);
- const qint16x8_t max_data2 = vextq_s16(max_data, max_data, 1);
- const qint16x8_t max_data3 = vextq_s16(max_data, max_data, 2);
- const qint16x8_t final_max = vmaxq_s16(vmaxq_s16(max_data, max_data2), max_data3);
-
- if(pool_stride_x == 2)
- {
- const qint16x4_t tmp = { vgetq_lane_s16(final_max, 0), vgetq_lane_s16(final_max, 2), vgetq_lane_s16(final_max, 4), vgetq_lane_s16(final_max, 6) };
- vst1_qs16(reinterpret_cast<qint16_t *>(output.ptr()), tmp);
- }
- else
- {
- vst1q_qs16(reinterpret_cast<qint16_t *>(output.ptr()), final_max);
- }
- }
- },
- input, output);
-}
-
template <PoolingType pooling_type, bool exclude_padding>
void NEPoolingLayerKernel::pooling3_f32_nchw(const Window &window_input, const Window &window)
{
@@ -1879,110 +1426,6 @@
input, output);
}
-template <PoolingType pooling_type>
-void NEPoolingLayerKernel::poolingMxN_q8_nchw(const Window &window_input, const Window &window)
-{
- Iterator input(_input, window_input);
- Iterator output(_output, window);
-
- const int pool_size_x = _pool_info.is_global_pooling() ? _input->info()->tensor_shape().x() : _pool_info.pool_size().width;
- const int pool_size_y = _pool_info.is_global_pooling() ? _input->info()->tensor_shape().y() : _pool_info.pool_size().height;
- const int pool_pad_top = _pool_info.pad_stride_info().pad_top();
- const int pool_pad_left = _pool_info.pad_stride_info().pad_left();
- int pool_stride_x = 0;
- int pool_stride_y = 0;
- std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- qint8x16_t vres = {};
- qint8_t res = {};
-
- //PoolingType::MAX
- for(int y = 0; y < pool_size_y; ++y)
- {
- int x = 0;
- for(; x <= (pool_size_x - 16); x += 16)
- {
- const qint8x16_t data = vld1q_qs8(reinterpret_cast<const qint8_t *>(input.ptr() + (x - pool_pad_left) * _input->info()->strides_in_bytes().x() +
- (y - pool_pad_top) * _input->info()->strides_in_bytes().y()));
- vres = vmaxq_s8(vres, data);
- }
-
- // Leftover for loop
- for(; x < pool_size_x; ++x)
- {
- qint8_t data = *(reinterpret_cast<const qint8_t *>(input.ptr() + (x - pool_pad_left) * _input->info()->strides_in_bytes().x() + (y - pool_pad_top) * _input->info()->strides_in_bytes().y()));
- res = std::max(res, data);
- }
- }
- //Reduce
- const qint8x8_t half_vres = vpmax_s8(vget_low_s8(vres), vget_high_s8(vres));
- res = std::max(res, vget_lane_s8(half_vres, 0));
- res = std::max(res, vget_lane_s8(half_vres, 1));
- res = std::max(res, vget_lane_s8(half_vres, 2));
- res = std::max(res, vget_lane_s8(half_vres, 3));
- res = std::max(res, vget_lane_s8(half_vres, 4));
- res = std::max(res, vget_lane_s8(half_vres, 5));
- res = std::max(res, vget_lane_s8(half_vres, 6));
- res = std::max(res, vget_lane_s8(half_vres, 7));
-
- // Store result
- *(reinterpret_cast<qint8_t *>(output.ptr())) = res;
- },
- input, output);
-}
-
-template <PoolingType pooling_type>
-void NEPoolingLayerKernel::poolingMxN_q16_nchw(const Window &window_input, const Window &window)
-{
- Iterator input(_input, window_input);
- Iterator output(_output, window);
-
- const int pool_size_x = _pool_info.is_global_pooling() ? _input->info()->tensor_shape().x() : _pool_info.pool_size().width;
- const int pool_size_y = _pool_info.is_global_pooling() ? _input->info()->tensor_shape().y() : _pool_info.pool_size().height;
- const int pool_pad_top = _pool_info.pad_stride_info().pad_top();
- const int pool_pad_left = _pool_info.pad_stride_info().pad_left();
- int pool_stride_x = 0;
- int pool_stride_y = 0;
- std::tie(pool_stride_x, pool_stride_y) = _pool_info.pad_stride_info().stride();
-
- execute_window_loop(window, [&](const Coordinates & id)
- {
- qint16x8_t vres = {};
- qint16_t res = {};
-
- //PoolingType::MAX
- for(int y = 0; y < pool_size_y; ++y)
- {
- int x = 0;
- for(; x <= (pool_size_x - 8); x += 8)
- {
- const qint16x8_t data = vld1q_qs16(reinterpret_cast<const qint16_t *>(input.ptr() + (x - pool_pad_left) * _input->info()->strides_in_bytes().x() +
- (y - pool_pad_top) * _input->info()->strides_in_bytes().y()));
- vres = vmaxq_s16(vres, data);
- }
-
- // Leftover for loop
- for(; x < pool_size_x; ++x)
- {
- qint16_t data = *(reinterpret_cast<const qint16_t *>(input.ptr() + (x - pool_pad_left) * _input->info()->strides_in_bytes().x() + (y - pool_pad_top) * _input->info()->strides_in_bytes().y()));
- res = std::max(res, data);
- }
- }
- //Reduce
- const qint16x4_t half_vres = vpmax_s16(vget_low_s16(vres), vget_high_s16(vres));
- res = std::max(res, vget_lane_s16(half_vres, 0));
- res = std::max(res, vget_lane_s16(half_vres, 1));
- res = std::max(res, vget_lane_s16(half_vres, 2));
- res = std::max(res, vget_lane_s16(half_vres, 3));
-
- // Store result
- *(reinterpret_cast<qint16_t *>(output.ptr())) = res;
- },
- input, output);
-}
-
template <PoolingType pooling_type, bool exclude_padding>
void NEPoolingLayerKernel::poolingMxN_f16_nchw(const Window &window_input, const Window &window)
{
@@ -2688,8 +2131,6 @@
unsigned int window_x_inc = 0;
switch(_input->info()->data_type())
{
- case DataType::QS8:
- case DataType::QS16:
case DataType::F16:
{
window_x_inc = (pool_stride_x == 2) ? _num_elems_processed_per_iteration * 2 : _num_elems_processed_per_iteration;
diff --git a/src/core/NEON/kernels/NEQuantizationLayerKernel.cpp b/src/core/NEON/kernels/NEQuantizationLayerKernel.cpp
index ee23e76..b49400a 100644
--- a/src/core/NEON/kernels/NEQuantizationLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEQuantizationLayerKernel.cpp
@@ -54,7 +54,7 @@
std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, ITensorInfo *min_max)
{
// Output tensor auto initialization if not yet initialized
- auto_init_if_empty(*output, input->tensor_shape(), 1, DataType::U8, 0);
+ auto_init_if_empty(*output, input->tensor_shape(), 1, DataType::U8);
constexpr unsigned int num_elems_processed_per_iteration = 8;
diff --git a/src/core/NEON/kernels/NEROIPoolingLayerKernel.cpp b/src/core/NEON/kernels/NEROIPoolingLayerKernel.cpp
index a209a52..4d908db 100644
--- a/src/core/NEON/kernels/NEROIPoolingLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEROIPoolingLayerKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -51,7 +51,7 @@
// Output auto inizialitation if not yet initialized
TensorShape output_shape(pool_info.pooled_width(), pool_info.pooled_height(), input->info()->dimension(2), rois->num_values());
- auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position());
+ auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type());
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pool_info.pooled_width()) || (output->info()->dimension(1) != pool_info.pooled_height()));
diff --git a/src/core/NEON/kernels/NEReductionOperationKernel.cpp b/src/core/NEON/kernels/NEReductionOperationKernel.cpp
index 30d42fa..30f21bb 100644
--- a/src/core/NEON/kernels/NEReductionOperationKernel.cpp
+++ b/src/core/NEON/kernels/NEReductionOperationKernel.cpp
@@ -134,7 +134,7 @@
const TensorShape output_shape = calculate_output_shape(input->tensor_shape(), axis);
// Output auto initialization if not yet initialized
- auto_init_if_empty(*output, output_shape, 1, input->data_type(), input->fixed_point_position());
+ auto_init_if_empty(*output, output_shape, 1, input->data_type());
unsigned int num_elems_processed_per_iteration = 16 / data_size_from_type(input->data_type());
diff --git a/src/core/NEON/kernels/NEReshapeLayerKernel.cpp b/src/core/NEON/kernels/NEReshapeLayerKernel.cpp
index 45ba68d..d6f4704 100644
--- a/src/core/NEON/kernels/NEReshapeLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEReshapeLayerKernel.cpp
@@ -59,11 +59,10 @@
void NEReshapeLayerKernel::configure(const ITensor *input, ITensor *output)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8, DataType::QS8, DataType::U16, DataType::S16, DataType::QS16,
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8, DataType::U16, DataType::S16,
DataType::U32, DataType::S32, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_NULLPTR(output);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
ARM_COMPUTE_ERROR_ON(input->info()->tensor_shape().total_size() != output->info()->tensor_shape().total_size());
_input = input;
@@ -94,12 +93,10 @@
case DataType::U8:
case DataType::S8:
case DataType::QASYMM8:
- case DataType::QS8:
reshape_tensor<uint8_t>(window, _input, _output);
break;
case DataType::U16:
case DataType::S16:
- case DataType::QS16:
case DataType::F16:
reshape_tensor<uint16_t>(window, _input, _output);
break;
diff --git a/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp b/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp
index d91efd2..9946f00 100644
--- a/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp
+++ b/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp
@@ -194,56 +194,7 @@
template <typename T>
T sqsub(T a, T b);
template <typename T>
-T sqmul(T a, T b, int fixed_point_position);
-
-#define DECLARE_NEON_FUNCTIONS_FOR_FIXED_POINT(TYPET, TYPEU, TAGT, TAGU) \
- inline vec_8_byte_t<TYPET> vqsub(vec_8_byte_t<TYPET> a, vec_8_byte_t<TYPET> b) \
- { \
- return vqsub_##TAGT(a, b); \
- } \
- inline vec_8_byte_t<TYPEU> vqadd(vec_8_byte_t<TYPEU> a, vec_8_byte_t<TYPEU> b) \
- { \
- return vqadd_##TAGU(a, b); \
- } \
- inline vec_16_byte_t<TYPEU> vqadd(vec_16_byte_t<TYPEU> a, vec_16_byte_t<TYPEU> b) \
- { \
- return vqaddq_##TAGU(a, b); \
- } \
- inline vec_8_byte_t<TYPET> vqexp(vec_8_byte_t<TYPET> vec, int fixed_point_position) \
- { \
- return vqexp_q##TAGT(vec, fixed_point_position); \
- } \
- inline auto vmovl(vec_8_byte_t<TYPET> vec)->decltype(vmovl_##TAGT(vec)) \
- { \
- return vmovl_##TAGT(vec); \
- } \
- inline vec_16_byte_t<TYPET> vqrecip(vec_16_byte_t<TYPET> vec, int fixed_point_position) \
- { \
- return vqrecipq_q##TAGT(vec, fixed_point_position); \
- } \
- inline vec_16_byte_t<TYPET> vqmul(vec_16_byte_t<TYPET> a, vec_16_byte_t<TYPET> b, int fixed_point_position) \
- { \
- return vqmulq_q##TAGT(a, b, fixed_point_position); \
- } \
- template <> \
- inline TYPEU sqadd<TYPEU>(TYPEU a, TYPEU b) \
- { \
- return sqadd_q##TAGU(a, b); \
- } \
- inline TYPET sqexp(TYPET val, int fixed_point_position) \
- { \
- return sqexp_q##TAGT(val, fixed_point_position); \
- } \
- template <> \
- inline TYPET sqsub<TYPET>(TYPET a, TYPET b) \
- { \
- return sqsub_q##TAGT(a, b); \
- } \
- template <> \
- inline TYPET sqmul<TYPET>(TYPET a, TYPET b, int fixed_point_position) \
- { \
- return sqmul_q##TAGT(a, b, fixed_point_position); \
- }
+T sqmul(T a, T b);
#define DECLARE_NEON_FUNCTIONS_FOR_FLOAT(TYPE, TAG) \
inline vec_8_byte_t<TYPE> vadd(vec_8_byte_t<TYPE> a, vec_8_byte_t<TYPE> b) \
@@ -278,9 +229,6 @@
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
DECLARE_NEON_FUNCTIONS_FOR_TYPE(float, f32)
-DECLARE_NEON_FUNCTIONS_FOR_FIXED_POINT(int8_t, int16_t, s8, s16)
-DECLARE_NEON_FUNCTIONS_FOR_FIXED_POINT(int16_t, int32_t, s16, s32)
-
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
DECLARE_NEON_FUNCTIONS_FOR_FLOAT(float16_t, f16)
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
@@ -373,16 +321,15 @@
Status validate_arguments_logits_1d_max(const ITensorInfo &input, const ITensorInfo &output)
{
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::QS8, DataType::QS16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::F32);
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
// Validate in case of configured output
if(output.total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(&input, &output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&input, &output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output.tensor_shape(), TensorShape(input.tensor_shape()).set(0, 1));
}
@@ -395,7 +342,7 @@
// Softmax across the x dimension
const TensorShape output_shape = TensorShape(input.tensor_shape()).set(0, 1);
// Output auto initialization if not yet initialized
- auto_init_if_empty(output, output_shape, 1, input.data_type(), input.fixed_point_position(), input.quantization_info());
+ auto_init_if_empty(output, output_shape, 1, input.data_type(), input.quantization_info());
// Configure kernel window
const int input_width = input.valid_region().shape.x();
@@ -488,12 +435,6 @@
case DataType::QASYMM8:
_func = &logits_1d_max<qasymm8_t>;
break;
- case DataType::QS8:
- _func = &logits_1d_max<qint8_t>;
- break;
- case DataType::QS16:
- _func = &logits_1d_max<qint16_t>;
- break;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
_func = &logits_1d_max<float16_t>;
@@ -543,11 +484,12 @@
Status validate_arguments_logits_softmax(const ITensorInfo &input, const ITensorInfo &max,
const ITensorInfo &output, const float beta, const ITensorInfo &tmp)
{
+ ARM_COMPUTE_UNUSED(beta);
// Check input
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
#else /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::QS8, DataType::QS16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::F32);
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(input.data_type());
@@ -555,7 +497,6 @@
// Check max
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &max);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(TensorShape(input.tensor_shape()).set(0, 1), max.tensor_shape());
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(&input, &max);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&input, &max);
// Check output if configured
@@ -564,19 +505,14 @@
const QuantizationInfo output_quantization = is_quantized_asymmetric ? QuantizationInfo(1.f / 256.f, 0) : output.quantization_info();
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&input, &output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(&input, &output);
ARM_COMPUTE_RETURN_ERROR_ON(output.quantization_info() != output_quantization);
}
- // Check beta
- ARM_COMPUTE_RETURN_ERROR_ON((beta != 1.0f) && is_data_type_fixed_point(input.data_type()));
-
// Check tmp if configured
if(tmp.total_size() != 0)
{
const DataType tmp_data_type = is_quantized_asymmetric ? DataType::F32 : input.data_type();
ARM_COMPUTE_RETURN_ERROR_ON(tmp.data_type() != tmp_data_type);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(&input, &tmp);
// We could potentially reduce tmp memory if we could predict or make an assumption
// on the maximum number of threads that will run in parallel.
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&input, &tmp);
@@ -727,88 +663,6 @@
in_it, max_it, out_it);
}
-template <typename T, typename U>
-void logits_1d_softmax_fixed_point(const ITensor &in, const ITensor &max, void *const tmp,
- ITensor &out, const float /*beta*/, const Window &window)
-{
- const int start_x = in.info()->valid_region().anchor.x();
- const int input_width = in.info()->valid_region().shape.x();
-
- const int fixed_point_position = in.info()->fixed_point_position();
-
- Iterator in_it(&in, window);
- Iterator max_it(&max, window);
- Iterator out_it(&out, window);
-
- execute_window_loop(window, [&](const Coordinates &)
- {
- /* Get pointers */
- const auto in_ptr = reinterpret_cast<const T *>(in_it.ptr()) + start_x;
- const auto out_ptr = reinterpret_cast<T *>(out_it.ptr()) + start_x;
- const auto tmp_ptr = reinterpret_cast<T *>(tmp);
-
- vec_16_byte_t<T> vec_sum_inversed;
-
- /* Compute exponentials and sum */
- {
- /* Get max value */
- const auto max_val = *reinterpret_cast<const T *>(max_it.ptr());
- const auto vec_max = vdup_n<vec_8_byte_t<T>>(max_val);
-
- /* Init sum to zero */
- auto vec_sum = vdup_n<vec_16_byte_t<U>>(0);
-
- /* Loop over row and compute exponentials and sum */
- int i = 0;
- constexpr int vec_size = vec_size_of(vec_sum);
- for(; i <= (input_width - vec_size); i += vec_size)
- {
- auto vec_elements = vld<vec_8_byte_t<T>>(in_ptr + i);
- vec_elements = vqsub(vec_elements, vec_max);
- vec_elements = vqexp(vec_elements, fixed_point_position);
- vec_sum = vqadd(vec_sum, vmovl(vec_elements));
- vst(tmp_ptr + i, vec_elements);
- }
- /* Reduce sum */
- const vec_8_byte_t<U> sum_8_byte = vqadd(vget_high(vec_sum), vget_low(vec_sum));
- U sum = reduce_add(sqadd<U>, sum_8_byte);
-
- /* Run remaining elements */
- for(; i < input_width; ++i)
- {
- T element = sqexp(sqsub(in_ptr[i], max_val), fixed_point_position);
- sum = sqadd<U>(sum, element);
- tmp_ptr[i] = element;
- }
-
- const auto qsum = utility::saturate_cast<T>(sum);
- vec_sum_inversed = vqrecip(vdup_n<vec_16_byte_t<T>>(qsum), fixed_point_position);
- }
-
- /* Normalize exponentials */
- {
- /* Loop over row and compute softmax */
- int i = 0;
- constexpr int vec_size = vec_size_of(vec_sum_inversed);
- for(; i <= (input_width - vec_size); i += vec_size)
- {
- const auto vec_in = vld<vec_16_byte_t<T>>(tmp_ptr + i);
- const vec_16_byte_t<T> normalized_value = vqmul(vec_in, vec_sum_inversed, fixed_point_position);
- vst(out_ptr + i, normalized_value);
- }
-
- const T sum_inversed = vget_lane<0>(vec_sum_inversed);
-
- /* Run remaining elements */
- for(; i < input_width; ++i)
- {
- out_ptr[i] = sqmul(tmp_ptr[i], sum_inversed, fixed_point_position);
- }
- }
- },
- in_it, max_it, out_it);
-}
-
template <typename T>
void logits_1d_softmax_float(const ITensor &in, const ITensor &max, void *const tmp,
ITensor &out, const float beta, const Window &window)
@@ -908,12 +762,6 @@
case DataType::QASYMM8:
_func = &logits_1d_softmax_qasymm8;
break;
- case DataType::QS8:
- _func = &logits_1d_softmax_fixed_point<qint8_t, qint16_t>;
- break;
- case DataType::QS16:
- _func = &logits_1d_softmax_fixed_point<qint16_t, qint32_t>;
- break;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
case DataType::F16:
_func = &logits_1d_softmax_float<float16_t>;
diff --git a/src/core/NEON/kernels/NETransposeKernel.cpp b/src/core/NEON/kernels/NETransposeKernel.cpp
index 9227137..2630159 100644
--- a/src/core/NEON/kernels/NETransposeKernel.cpp
+++ b/src/core/NEON/kernels/NETransposeKernel.cpp
@@ -74,7 +74,7 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QS8, DataType::QASYMM8, DataType::U16, DataType::S16, DataType::QS16, DataType::U32, DataType::S32,
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::S8, DataType::QASYMM8, DataType::U16, DataType::S16, DataType::U32, DataType::S32,
DataType::F16,
DataType::F32);
@@ -84,7 +84,6 @@
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
diff --git a/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp b/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp
index 3031a87..f398409 100644
--- a/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp
+++ b/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp
@@ -105,14 +105,13 @@
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *biases, const ITensorInfo *output)
{
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
if(biases != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input->data_type()));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases);
ARM_COMPUTE_RETURN_ERROR_ON((input->num_dimensions() == 4) && (biases->num_dimensions() != 1));
ARM_COMPUTE_RETURN_ERROR_ON((input->num_dimensions() == 5) && (biases->num_dimensions() != 2));
ARM_COMPUTE_RETURN_ERROR_ON((input->num_dimensions() == 4) && (biases->dimension(0) != input->tensor_shape()[3]));
@@ -124,7 +123,6 @@
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), get_output_shape(input, biases != nullptr));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
- ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
}
return Status{};
diff --git a/src/core/TensorInfo.cpp b/src/core/TensorInfo.cpp
index 676938a..b77a47e 100644
--- a/src/core/TensorInfo.cpp
+++ b/src/core/TensorInfo.cpp
@@ -33,8 +33,8 @@
using namespace arm_compute;
TensorInfo::TensorInfo()
- : _total_size(0), _fixed_point_position(0), _offset_first_element_in_bytes(0), _strides_in_bytes(), _num_channels(0), _tensor_shape(), _data_type(DataType::UNKNOWN), _format(Format::UNKNOWN),
- _is_resizable{ true }, _valid_region{ Coordinates(), _tensor_shape }, _padding{ 0 }, _quantization_info(), _data_layout(DataLayout::NCHW)
+ : _total_size(0), _offset_first_element_in_bytes(0), _strides_in_bytes(), _num_channels(0), _tensor_shape(), _data_type(DataType::UNKNOWN), _format(Format::UNKNOWN), _is_resizable{ true },
+ _valid_region{ Coordinates(), _tensor_shape }, _padding{ 0 }, _quantization_info(), _data_layout(DataLayout::NCHW)
{
}
@@ -42,7 +42,6 @@
: TensorInfo()
{
_total_size = info.total_size();
- _fixed_point_position = info.fixed_point_position();
_offset_first_element_in_bytes = info.offset_first_element_in_bytes();
_strides_in_bytes = info.strides_in_bytes();
_num_channels = info.num_channels();
@@ -72,22 +71,22 @@
init(tensor_shape, format);
}
-TensorInfo::TensorInfo(size_t num_channels, DataType data_type, size_t fixed_point_position)
+TensorInfo::TensorInfo(size_t num_channels, DataType data_type)
: TensorInfo()
{
- init(TensorShape(), num_channels, data_type, fixed_point_position);
+ init(TensorShape(), num_channels, data_type);
}
-TensorInfo::TensorInfo(const TensorShape &tensor_shape, size_t num_channels, DataType data_type, int fixed_point_position)
+TensorInfo::TensorInfo(const TensorShape &tensor_shape, size_t num_channels, DataType data_type)
: TensorInfo()
{
- init(tensor_shape, num_channels, data_type, fixed_point_position);
+ init(tensor_shape, num_channels, data_type);
}
TensorInfo::TensorInfo(const TensorShape &tensor_shape, size_t num_channels, DataType data_type, QuantizationInfo quantization_info)
: TensorInfo()
{
- init(tensor_shape, num_channels, data_type, 0);
+ init(tensor_shape, num_channels, data_type);
_quantization_info = quantization_info;
}
@@ -124,34 +123,28 @@
_format = format;
}
-void TensorInfo::init(size_t num_channels, DataType data_type, size_t fixed_point_position)
+void TensorInfo::init(size_t num_channels, DataType data_type)
{
- init(TensorShape(), num_channels, data_type, fixed_point_position);
+ init(TensorShape(), num_channels, data_type);
}
-void TensorInfo::init(const TensorShape &tensor_shape, size_t num_channels, DataType data_type, int fixed_point_position)
+void TensorInfo::init(const TensorShape &tensor_shape, size_t num_channels, DataType data_type)
{
ARM_COMPUTE_ERROR_ON(num_channels == 0);
- ARM_COMPUTE_ERROR_ON(data_type == DataType::QS8 && (fixed_point_position < 1 || fixed_point_position > 6));
- ARM_COMPUTE_ERROR_ON(data_type == DataType::QS16 && (fixed_point_position < 1 || fixed_point_position > 14));
- _fixed_point_position = fixed_point_position;
- _data_type = data_type;
- _num_channels = num_channels;
- _format = Format::UNKNOWN;
+ _data_type = data_type;
+ _num_channels = num_channels;
+ _format = Format::UNKNOWN;
set_tensor_shape(tensor_shape);
}
void TensorInfo::init(const TensorShape &tensor_shape, size_t num_channels, DataType data_type,
const Strides &strides_in_bytes, size_t offset_first_element_in_bytes,
- size_t total_size_in_bytes, int fixed_point_position)
+ size_t total_size_in_bytes)
{
ARM_COMPUTE_ERROR_ON(num_channels == 0);
- ARM_COMPUTE_ERROR_ON(data_type == DataType::QS8 && (fixed_point_position < 1 || fixed_point_position > 6));
- ARM_COMPUTE_ERROR_ON(data_type == DataType::QS16 && (fixed_point_position < 1 || fixed_point_position > 14));
- _fixed_point_position = fixed_point_position;
_data_type = data_type;
_num_channels = num_channels;
_format = Format::UNKNOWN;
@@ -188,17 +181,14 @@
return total_size;
}
-size_t TensorInfo::init_auto_padding(const TensorShape &tensor_shape, size_t num_channels, DataType data_type, int fixed_point_position)
+size_t TensorInfo::init_auto_padding(const TensorShape &tensor_shape, size_t num_channels, DataType data_type)
{
ARM_COMPUTE_ERROR_ON(num_channels == 0);
- ARM_COMPUTE_ERROR_ON(data_type == DataType::QS8 && (fixed_point_position < 1 || fixed_point_position > 6));
- ARM_COMPUTE_ERROR_ON(data_type == DataType::QS16 && (fixed_point_position < 1 || fixed_point_position > 14));
- _fixed_point_position = fixed_point_position;
- _data_type = data_type;
- _num_channels = num_channels;
- _format = Format::UNKNOWN;
- _tensor_shape = tensor_shape;
+ _data_type = data_type;
+ _num_channels = num_channels;
+ _format = Format::UNKNOWN;
+ _tensor_shape = tensor_shape;
_valid_region = ValidRegion{ Coordinates(), _tensor_shape };
@@ -371,14 +361,6 @@
return *this;
}
-ITensorInfo &TensorInfo::set_fixed_point_position(int fixed_point_position)
-{
- ARM_COMPUTE_ERROR_ON(_data_type == DataType::QS8 && (fixed_point_position < 1 || fixed_point_position > 6));
- ARM_COMPUTE_ERROR_ON(_data_type == DataType::QS16 && (fixed_point_position < 1 || fixed_point_position > 14));
- _fixed_point_position = fixed_point_position;
- return *this;
-}
-
ITensorInfo &TensorInfo::set_quantization_info(const QuantizationInfo &quantization_info)
{
_quantization_info = quantization_info;
diff --git a/src/core/Utils.cpp b/src/core/Utils.cpp
index b1c5992..11bdbda 100644
--- a/src/core/Utils.cpp
+++ b/src/core/Utils.cpp
@@ -24,8 +24,6 @@
#include "arm_compute/core/Utils.h"
-#include "arm_compute/core/FixedPoint.h"
-
#include "support/ToolchainSupport.h"
#include <algorithm>
@@ -145,10 +143,8 @@
{ DataType::UNKNOWN, "UNKNOWN" },
{ DataType::S8, "S8" },
{ DataType::U8, "U8" },
- { DataType::QS8, "QS8" },
{ DataType::S16, "S16" },
{ DataType::U16, "U16" },
- { DataType::QS16, "QS16" },
{ DataType::S32, "S32" },
{ DataType::U32, "U32" },
{ DataType::S64, "S64" },
@@ -353,14 +349,12 @@
case DataType::U8:
print_consecutive_elements_impl<uint8_t>(s, ptr, n, stream_width, element_delim);
break;
- case DataType::QS8:
case DataType::S8:
print_consecutive_elements_impl<int8_t>(s, reinterpret_cast<const int8_t *>(ptr), n, stream_width, element_delim);
break;
case DataType::U16:
print_consecutive_elements_impl<uint16_t>(s, reinterpret_cast<const uint16_t *>(ptr), n, stream_width, element_delim);
break;
- case DataType::QS16:
case DataType::S16:
print_consecutive_elements_impl<int16_t>(s, reinterpret_cast<const int16_t *>(ptr), n, stream_width, element_delim);
break;
@@ -388,12 +382,10 @@
case DataType::QASYMM8:
case DataType::U8:
return max_consecutive_elements_display_width_impl<uint8_t>(s, ptr, n);
- case DataType::QS8:
case DataType::S8:
return max_consecutive_elements_display_width_impl<int8_t>(s, reinterpret_cast<const int8_t *>(ptr), n);
case DataType::U16:
return max_consecutive_elements_display_width_impl<uint16_t>(s, reinterpret_cast<const uint16_t *>(ptr), n);
- case DataType::QS16:
case DataType::S16:
return max_consecutive_elements_display_width_impl<int16_t>(s, reinterpret_cast<const int16_t *>(ptr), n);
case DataType::U32: