COMPMID-675: NEGEMMLowp Assembly Integration

Added support for S8 input in NEGEMMLowp Matrix Multiply Kernel.
Added a new function to run assembly kernels such that A*B=C (no offsets involved)
Added new tests for the assembly gemmlowp kernels (no offsets)
Integrated the assembly kernel for the A57

Change-Id: Ib3e39c1f3f7f1baa0d39be69485f61cd18e3c9b3
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/95864
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

1 files changed, 174 insertions, 35 deletions

diff --git a/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp
index 1352f34e3c..5f052f797d 100644
--- a/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp
@@ -52,7 +52,7 @@ NEGEMMLowpMatrixMultiplyKernel::NEGEMMLowpMatrixMultiplyKernel()
 
 void NEGEMMLowpMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8);
+    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::S8);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
 
@@ -90,41 +90,8 @@ void NEGEMMLowpMatrixMultiplyKernel::configure(const ITensor *input0, const ITen
     INEKernel::configure(win);
 }
 
-void NEGEMMLowpMatrixMultiplyKernel::run(const Window &window, const ThreadInfo &info)
+void inline matrix_multiply_u8(Iterator &ina, Iterator &inb, Iterator &out, int width_b, size_t out_stride, const Window &window)
 {
-    ARM_COMPUTE_UNUSED(info);
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
-
-    const size_t in_b_stride = _input1->info()->strides_in_bytes()[1];
-    const size_t out_stride  = _output->info()->strides_in_bytes()[1] / _output->info()->element_size();
-
-    // 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, window.y().end() / 4, 1));
-
-    // 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 columns of the output matrix
-    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(_slide_matrix_b)
-    {
-        win_b = window;
-    }
-    win_b.set(Window::DimX, Window::Dimension(window.x().start() / 16, window.x().end() / 16, in_b_stride));
-    win_b.set(Window::DimY, Window::Dimension(0, 0, 0));
-
-    // The step x and step y for the output matrix has been already set using in configure()
-    Iterator ina(_input0, win_a);
-    Iterator inb(_input1, win_b);
-    Iterator out(_output, window);
-
-    const int width_b = _input1->info()->dimension(0);
-
-    // 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 4x4 block will be read from consecutive memory positions
     execute_window_loop(window, [&](const Coordinates & id)
     {
         const uint8_t *mtx_a0 = ina.ptr();
@@ -239,3 +206,175 @@ void NEGEMMLowpMatrixMultiplyKernel::run(const Window &window, const ThreadInfo
     },
     ina, inb, out);
 }
+
+void inline matrix_multiply_s8(Iterator &ina, Iterator &inb, Iterator &out, int width_b, size_t out_stride, const Window &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 4x4 block will be read from consecutive memory positions
+    execute_window_loop(window, [&](const Coordinates & id)
+    {
+        auto *mtx_a0 = reinterpret_cast<const int8_t *>(ina.ptr());
+        auto *mtx_b0 = reinterpret_cast<const int8_t *>(inb.ptr());
+
+        // Note: Since the input are all positives, we can use uint32_t
+        // Accumulators for the block 0
+        int32x4x4_t c0 =
+        {
+            {
+                vdupq_n_s32(0),
+                vdupq_n_s32(0),
+                vdupq_n_s32(0),
+                vdupq_n_s32(0)
+            }
+        };
+
+        // Accumulators for the block 1
+        int32x4x4_t c1 =
+        {
+            {
+                vdupq_n_s32(0),
+                vdupq_n_s32(0),
+                vdupq_n_s32(0),
+                vdupq_n_s32(0)
+            }
+        };
+
+        // Accumulators for the block 2
+        int32x4x4_t c2 =
+        {
+            {
+                vdupq_n_s32(0),
+                vdupq_n_s32(0),
+                vdupq_n_s32(0),
+                vdupq_n_s32(0)
+            }
+        };
+
+        // Accumulators for the block 3
+        int32x4x4_t c3 =
+        {
+            {
+                vdupq_n_s32(0),
+                vdupq_n_s32(0),
+                vdupq_n_s32(0),
+                vdupq_n_s32(0)
+            }
+        };
+
+        for(int k = 0; k < width_b; k += 16, mtx_a0 += 4, mtx_b0 += 16)
+        {
+            const int8x8_t  a00_s8 = vld1_s8(mtx_a0);
+            const int8x16_t b00_s8 = vld1q_s8(mtx_b0);
+
+            // Convert a00_s8 to uint16_t and get the lower part
+            const int16x4_t a00_s16 = vget_low_s16(vmovl_s8(a00_s8));
+
+            // Convert b00_s8 to int16_t
+            const int16x4x4_t b00_s16 =
+            {
+                {
+                    vget_low_s16(vmovl_s8(vget_low_s8(b00_s8))),
+                    vget_high_s16(vmovl_s8(vget_low_s8(b00_s8))),
+                    vget_low_s16(vmovl_s8(vget_high_s8(b00_s8))),
+                    vget_high_s16(vmovl_s8(vget_high_s8(b00_s8)))
+                }
+            };
+
+            // 4x4 block 0
+            c0.val[0] = vmlal_lane_s16(c0.val[0], b00_s16.val[0], a00_s16, 0);
+            c0.val[1] = vmlal_lane_s16(c0.val[1], b00_s16.val[1], a00_s16, 0);
+            c0.val[2] = vmlal_lane_s16(c0.val[2], b00_s16.val[2], a00_s16, 0);
+            c0.val[3] = vmlal_lane_s16(c0.val[3], b00_s16.val[3], a00_s16, 0);
+
+            // 4x4 block 1
+            c1.val[0] = vmlal_lane_s16(c1.val[0], b00_s16.val[0], a00_s16, 1);
+            c1.val[1] = vmlal_lane_s16(c1.val[1], b00_s16.val[1], a00_s16, 1);
+            c1.val[2] = vmlal_lane_s16(c1.val[2], b00_s16.val[2], a00_s16, 1);
+            c1.val[3] = vmlal_lane_s16(c1.val[3], b00_s16.val[3], a00_s16, 1);
+
+            // 4x4 block 2
+            c2.val[0] = vmlal_lane_s16(c2.val[0], b00_s16.val[0], a00_s16, 2);
+            c2.val[1] = vmlal_lane_s16(c2.val[1], b00_s16.val[1], a00_s16, 2);
+            c2.val[2] = vmlal_lane_s16(c2.val[2], b00_s16.val[2], a00_s16, 2);
+            c2.val[3] = vmlal_lane_s16(c2.val[3], b00_s16.val[3], a00_s16, 2);
+
+            // 4x4 block 3
+            c3.val[0] = vmlal_lane_s16(c3.val[0], b00_s16.val[0], a00_s16, 3);
+            c3.val[1] = vmlal_lane_s16(c3.val[1], b00_s16.val[1], a00_s16, 3);
+            c3.val[2] = vmlal_lane_s16(c3.val[2], b00_s16.val[2], a00_s16, 3);
+            c3.val[3] = vmlal_lane_s16(c3.val[3], b00_s16.val[3], a00_s16, 3);
+        }
+
+        auto mtx_out = reinterpret_cast<int32_t *>(out.ptr());
+        vst1q_s32(mtx_out + 0 * out_stride + 0, c0.val[0]);
+        vst1q_s32(mtx_out + 0 * out_stride + 4, c0.val[1]);
+        vst1q_s32(mtx_out + 0 * out_stride + 8, c0.val[2]);
+        vst1q_s32(mtx_out + 0 * out_stride + 12, c0.val[3]);
+        vst1q_s32(mtx_out + 1 * out_stride + 0, c1.val[0]);
+        vst1q_s32(mtx_out + 1 * out_stride + 4, c1.val[1]);
+        vst1q_s32(mtx_out + 1 * out_stride + 8, c1.val[2]);
+        vst1q_s32(mtx_out + 1 * out_stride + 12, c1.val[3]);
+        vst1q_s32(mtx_out + 2 * out_stride + 0, c2.val[0]);
+        vst1q_s32(mtx_out + 2 * out_stride + 4, c2.val[1]);
+        vst1q_s32(mtx_out + 2 * out_stride + 8, c2.val[2]);
+        vst1q_s32(mtx_out + 2 * out_stride + 12, c2.val[3]);
+        vst1q_s32(mtx_out + 3 * out_stride + 0, c3.val[0]);
+        vst1q_s32(mtx_out + 3 * out_stride + 4, c3.val[1]);
+        vst1q_s32(mtx_out + 3 * out_stride + 8, c3.val[2]);
+        vst1q_s32(mtx_out + 3 * out_stride + 12, c3.val[3]);
+    },
+    ina, inb, out);
+}
+
+void NEGEMMLowpMatrixMultiplyKernel::run(const Window &window, const ThreadInfo &info)
+{
+    ARM_COMPUTE_UNUSED(info);
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
+
+    const size_t in_b_stride = _input1->info()->strides_in_bytes()[1];
+    const size_t out_stride  = _output->info()->strides_in_bytes()[1] / _output->info()->element_size();
+
+    // 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, window.y().end() / 4, 1));
+
+    // 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 columns of the output matrix
+    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(_slide_matrix_b)
+    {
+        win_b = window;
+    }
+    win_b.set(Window::DimX, Window::Dimension(window.x().start() / 16, window.x().end() / 16, in_b_stride));
+    win_b.set(Window::DimY, Window::Dimension(0, 0, 0));
+
+    // The step x and step y for the output matrix has been already set using in configure()
+    Iterator ina(_input0, win_a);
+    Iterator inb(_input1, win_b);
+    Iterator out(_output, window);
+
+    const int width_b = _input1->info()->dimension(0);
+    switch(_input0->info()->data_type())
+    {
+        case DataType::S8:
+        {
+            matrix_multiply_s8(ina, inb, out, width_b, out_stride, window);
+            break;
+        }
+        case DataType::U8:
+        case DataType::QASYMM8:
+        {
+            matrix_multiply_u8(ina, inb, out, width_b, out_stride, window);
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_ERROR("Not supported");
+            break;
+        }
+    }
+}