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- /**
- * \file dnn/src/naive/lowbit_utils.cpp
- * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
- *
- * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
- * implied.
- */
-
- #include "src/naive/lowbit_utils.h"
-
- // =================================quint4======================================
- void megdnn::naive::uint4_to_uint8(const TensorND& in, const TensorND& out) {
- auto in_ptr = static_cast<uint8_t*>(in.raw_ptr) + in.layout.span().low_byte;
- auto out_ptr = out.compatible_ptr<uint8_t>() + out.layout.span().low_byte;
- const auto& ly = in.layout;
- auto dim_in = ly.shape[ly.ndim - 1];
- auto elems = ly.total_nr_elems();
- auto dim_out = elems / dim_in;
- auto stride_out = div_ceil(dim_in, 2_z);
- for (size_t i = 0; i < dim_out; ++i) {
- for (size_t j = 0; j < dim_in; j += 2) {
- uint8_t val = in_ptr[j / 2];
- out_ptr[j] = val & 0xF;
- if (j + 1 < dim_in)
- out_ptr[j + 1] = (val >> 4) & 0xF;
- }
- in_ptr += stride_out;
- out_ptr += dim_in;
- }
- }
-
- void megdnn::naive::uint8_to_uint4(const TensorND& in, const TensorND& out) {
- auto in_ptr = static_cast<uint8_t*>(in.raw_ptr) + in.layout.span().low_byte;
- auto out_ptr =
- static_cast<uint8_t*>(out.raw_ptr) + out.layout.span().low_byte;
- const auto& ly = in.layout;
- auto dim_in = ly.shape[ly.ndim - 1];
- auto elems = ly.total_nr_elems();
- auto dim_out = elems / dim_in;
- auto stride_out = div_ceil(dim_in, 2_z);
- for (size_t i = 0; i < dim_out; ++i) {
- for (size_t j = 0; j < dim_in; j += 2) {
- uint8_t a = in_ptr[j];
- uint8_t b = 0;
- if (j + 1 < dim_in)
- b = in_ptr[j + 1];
- a = std::min(a, DTypeTrait<dtype::Quantized4Asymm>::max());
- b = std::min(b, DTypeTrait<dtype::Quantized4Asymm>::max());
- out_ptr[j / 2] = a + (b << 4);
- }
- in_ptr += dim_in;
- out_ptr += stride_out;
- }
- }
-
- void megdnn::naive::uint4_to_int8(const TensorND& in, const TensorND& out) {
- auto in_ptr = static_cast<uint8_t*>(in.raw_ptr) + in.layout.span().low_byte;
- auto out_ptr = out.compatible_ptr<int8_t>() + out.layout.span().low_byte;
- const auto& ly = in.layout;
- int8_t zero_point =
- (int8_t)ly.dtype.param<dtype::Quantized4Asymm>().zero_point;
- auto dim_in = ly.shape[ly.ndim - 1];
- auto elems = ly.total_nr_elems();
- auto dim_out = elems / dim_in;
- auto stride_out = div_ceil(dim_in, 2_z);
- for (size_t i = 0; i < dim_out; ++i) {
- for (size_t j = 0; j < dim_in; j += 2) {
- uint8_t val = in_ptr[j / 2];
- out_ptr[j] = (int8_t)(val & 0xF) - zero_point;
- if (j + 1 < dim_in)
- out_ptr[j + 1] = (int8_t)((val >> 4) & 0xF) - zero_point;
- }
- in_ptr += stride_out;
- out_ptr += dim_in;
- }
- }
-
- void megdnn::naive::int8_to_uint4(const TensorND& in, const TensorND& out) {
- auto in_ptr = static_cast<int8_t*>(in.raw_ptr) + in.layout.span().low_byte;
- auto out_ptr =
- static_cast<uint8_t*>(out.raw_ptr) + out.layout.span().low_byte;
- auto zero_point =
- out.layout.dtype.param<dtype::Quantized4Asymm>().zero_point;
- const auto& ly = in.layout;
- auto dim_in = ly.shape[ly.ndim - 1];
- auto elems = ly.total_nr_elems();
- auto dim_out = elems / dim_in;
- auto stride_out = div_ceil(dim_in, 2_z);
- for (size_t i = 0; i < dim_out; ++i) {
- for (size_t j = 0; j < dim_in; j += 2) {
- uint8_t a = (uint8_t)std::max((int32_t)in_ptr[j] + zero_point, 0);
- uint8_t b = 0;
- if (j + 1 < dim_in)
- b = (uint8_t)std::max((int32_t)in_ptr[j + 1] + zero_point, 0);
- a = std::min(a, DTypeTrait<dtype::Quantized4Asymm>::max());
- b = std::min(b, DTypeTrait<dtype::Quantized4Asymm>::max());
- out_ptr[j / 2] = a + (b << 4);
- }
- in_ptr += dim_in;
- out_ptr += stride_out;
- }
- }
-
- // ==================================qint4======================================
- void megdnn::naive::int4_to_int8(const TensorND& in, const TensorND& out) {
- auto in_ptr = static_cast<int8_t*>(in.raw_ptr) + in.layout.span().low_byte;
- auto out_ptr =
- static_cast<int8_t*>(out.raw_ptr) + out.layout.span().low_byte;
- const auto& ly = in.layout;
- auto dim_in = ly.shape[ly.ndim - 1];
- auto elems = ly.total_nr_elems();
- auto dim_out = elems / dim_in;
- auto stride_out = div_ceil(dim_in, 2_z);
- for (size_t i = 0; i < dim_out; ++i) {
- for (size_t j = 0; j < dim_in; j += 2) {
- int8_t cur = in_ptr[j / 2];
- out_ptr[j] = cur << 4;
- out_ptr[j] = out_ptr[j] >> 4;
- if (j + 1 < dim_in)
- out_ptr[j + 1] = cur >> 4;
- }
- in_ptr += stride_out;
- out_ptr += dim_in;
- }
- }
-
- void megdnn::naive::int8_to_int4(const TensorND& in, const TensorND& out) {
- auto in_ptr = static_cast<int8_t*>(in.raw_ptr) + in.layout.span().low_byte;
- auto out_ptr =
- static_cast<int8_t*>(out.raw_ptr) + out.layout.span().low_byte;
- const auto& ly = in.layout;
- auto dim_in = ly.shape[ly.ndim - 1];
- auto elems = ly.total_nr_elems();
- auto dim_out = elems / dim_in;
- auto stride_out = div_ceil(dim_in, 2_z);
- for (size_t i = 0; i < dim_out; ++i) {
- for (size_t j = 0; j < dim_in; j += 2) {
- int8_t a = in_ptr[j];
- int8_t b = 0;
- if (j + 1 < dim_in)
- b = in_ptr[j + 1];
- a = std::min(a, DTypeTrait<dtype::QuantizedS4>::max());
- a = std::max(a, DTypeTrait<dtype::QuantizedS4>::min());
- b = std::min(b, DTypeTrait<dtype::QuantizedS4>::max());
- b = std::max(b, DTypeTrait<dtype::QuantizedS4>::min());
- out_ptr[j / 2] = (a & 0xF) | (b << 4);
- }
- in_ptr += dim_in;
- out_ptr += stride_out;
- }
- }
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