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>
diff --git a/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp
index 1352f34..5f052f7 100644
--- a/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMLowpMatrixMultiplyKernel.cpp
@@ -52,7 +52,7 @@
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 @@
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 @@
},
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;
+ }
+ }
+}