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author | Gian Marco <gianmarco.iodice@arm.com> | 2017-12-07 09:26:56 +0000 |
---|---|---|
committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:42:17 +0000 |
commit | 7f0f790ae7f5dd044a5d7564492583b8df974a11 (patch) | |
tree | b3d6b7bdb0c6efb1dc4b9547dcc0f1fbcf1b4ee3 /src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp | |
parent | f2ad401d1db79373f1c6d1167ae10c94d706cce8 (diff) | |
download | ComputeLibrary-7f0f790ae7f5dd044a5d7564492583b8df974a11.tar.gz |
COMPMID-731 - Remove padding requirements for NEGEMMLowpOutputStage
Used a left-over for loop in:
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel
- NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel
in order to remove the padding requirements for AndroidNN
Change-Id: I8ef529fc3d1adecf15fbe42002d99bc0030f131f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/112266
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
Diffstat (limited to 'src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp')
-rw-r--r-- | src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp | 143 |
1 files changed, 101 insertions, 42 deletions
diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp index 7f351020b9..54513d8cdb 100644 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp +++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp @@ -65,7 +65,10 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, con std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *bias, ITensorInfo *output) { - constexpr unsigned int num_elems_processed_per_iteration = 16; + // Note: This kernel performs 16 elements per iteration. + // However, since we use a left-over for loop, we cannot have any read or write out of memory + // For this reason num_elems_processed_per_iteration is set to 1 + constexpr unsigned int num_elems_processed_per_iteration = 1; // Configure kernel window Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); @@ -85,7 +88,7 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen if(bias != nullptr) { - AccessWindowStatic bias_access(bias, 0, 0, ceil_to_multiple(bias->dimension(0), num_elems_processed_per_iteration), bias->tensor_shape()[1]); + AccessWindowStatic bias_access(bias, 0, 0, bias->dimension(0), bias->dimension(1)); window_changed = window_changed || update_window_and_padding(win, bias_access); } @@ -163,69 +166,125 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run(const Window &window) ARM_COMPUTE_UNUSED(min_u8); ARM_COMPUTE_UNUSED(max_u8); - Iterator in(_input, window); - Iterator out(_output, window); + const int window_step_x = 16; + const auto window_start_x = static_cast<int>(window.x().start()); + const auto window_end_x = static_cast<int>(window.x().end()); + + Window win(window); + win.set(Window::DimX, Window::Dimension(0, 1, 1)); + + Iterator in(_input, win); + Iterator out(_output, win); if(_bias != nullptr) { Window win_biases; - win_biases.set(Window::DimX, Window::Dimension(window.x().start(), window.x().end(), window.x().step())); + win_biases.set(Window::DimX, Window::Dimension(0, 1, 1)); win_biases.set(Window::DimY, Window::Dimension(0, 1, 1)); Iterator bias(_bias, win_biases); - execute_window_loop(window, [&](const Coordinates & id) + execute_window_loop(win, [&](const Coordinates & id) { - int32x4x4_t in_s32 = + // Compute 16 elements per iteration + int x = window_start_x; + for(; x <= (window_end_x - window_step_x); x += window_step_x) { + int32x4x4_t in_s32 = { - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 4), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 8), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 12) - } - }; - - const int32x4x4_t bias_s32 = - { + { + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0), + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4), + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8), + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12) + } + }; + + const int32x4x4_t bias_s32 = { - vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 4), - vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 8), - vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + 12) - } - }; - - // Add the bias to GEMM's result - in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]); - in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]); - in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]); - in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]); + { + vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 0), + vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 4), + vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 8), + vld1q_s32(reinterpret_cast<const int32_t *>(bias.ptr()) + x + 12) + } + }; + + // Add the bias to GEMM's result + in_s32.val[0] = vaddq_s32(in_s32.val[0], bias_s32.val[0]); + in_s32.val[1] = vaddq_s32(in_s32.val[1], bias_s32.val[1]); + in_s32.val[2] = vaddq_s32(in_s32.val[2], bias_s32.val[2]); + in_s32.val[3] = vaddq_s32(in_s32.val[3], bias_s32.val[3]); + + // Add the offset terms to GEMM's result and multiply by result_mult_int + scale_input(in_s32, result_offset_s32, _result_mult_int); + + vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8)); + } + + // Compute left-over elements + for(; x < window_end_x; ++x) + { + const int32_t bias_value = *(reinterpret_cast<const int32_t *>(bias.ptr()) + x); + int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x); - // Add the offset terms to GEMM's result and multiply by result_mult_int - scale_input(in_s32, result_offset_s32, _result_mult_int); + // Quantize + in_value = ((in_value + bias_value + _result_offset) * _result_mult_int) >> _result_shift; - vst1q_u8(out.ptr(), finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8)); + // Finalize and store the result + if(is_bounded_relu) + { + *(out.ptr() + x) = static_cast<uint8_t>(std::max(_min, std::min(_max, in_value))); + } + else + { + *(out.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value))); + } + } }, in, bias, out); } else { - execute_window_loop(window, [&](const Coordinates & id) + execute_window_loop(win, [&](const Coordinates & id) { - int32x4x4_t in_s32 = + // Compute 16 elements per iteration + int x = window_start_x; + for(; x <= (window_end_x - window_step_x); x += window_step_x) { + int32x4x4_t in_s32 = { - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 0), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 4), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 8), - vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + 12) - } - }; + { + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 0), + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 4), + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 8), + vld1q_s32(reinterpret_cast<const int32_t *>(in.ptr()) + x + 12) + } + }; + + // Add the offset terms to GEMM's result and multiply by result_mult_int + scale_input(in_s32, result_offset_s32, _result_mult_int); + + vst1q_u8(out.ptr() + x, finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8)); + } + + // Compute left-over elements + for(; x < window_end_x; ++x) + { + int32_t in_value = *(reinterpret_cast<const int32_t *>(in.ptr()) + x); - // Add the offset terms to GEMM's result and multiply by result_mult_int - scale_input(in_s32, result_offset_s32, _result_mult_int); + // Quantize + in_value = ((in_value + _result_offset) * _result_mult_int) >> _result_shift; - vst1q_u8(out.ptr(), finalize_quantization<is_bounded_relu>(in_s32, result_shift_s32, min_u8, max_u8)); + // Finalize and store the result + if(is_bounded_relu) + { + *(out.ptr() + x) = static_cast<uint8_t>(std::max(_min, std::min(_max, in_value))); + } + else + { + *(out.ptr() + x) = static_cast<uint8_t>(std::max(0, std::min(255, in_value))); + } + } }, in, out); } |