ncnn / src /layer /loongarch /cast_loongarch.cpp

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be903e2 about 3 years ago

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// yala is pleased to support the open source community by making ncnn available.
//
//
// Copyright (C) 2022 yala <zhaojunchao@loongson.cn>;<junchao82@qq.com>. All rights reserved.
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#include "cast_loongarch.h"

#if __loongarch_sx
#include <lsxintrin.h>
#endif // __loongarch_sx

namespace ncnn {

Cast_loongarch::Cast_loongarch()
{
    support_packing = true;
}

int Cast_loongarch::forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
{
    if (type_from == type_to)
    {
        top_blob = bottom_blob;
        return 0;
    }

    int w = bottom_blob.w;
    int h = bottom_blob.h;
    int d = bottom_blob.d;
    int channels = bottom_blob.c;
    int dims = bottom_blob.dims;
    size_t elemsize = bottom_blob.elemsize;
    int elempack = bottom_blob.elempack;

    size_t out_elemsize = elemsize;
    if (type_to == 1)
    {
        if (type_from == 3)
        {
            Cast::forward(bottom_blob, top_blob, opt);
        }

        // float32
        out_elemsize = 4 * elempack;
    }
    else if (type_to == 2)
    {
        // float16
        out_elemsize = 2 * elempack;
    }
    else if (type_to == 3)
    {
        // int8
        out_elemsize = elempack;
    }
    else if (type_to == 4)
    {
        // bfloat16
        out_elemsize = 2 * elempack;
    }

    if (dims == 1)
    {
        top_blob.create(w, out_elemsize, elempack, opt.blob_allocator);
    }
    else if (dims == 2)
    {
        top_blob.create(w, h, out_elemsize, elempack, opt.blob_allocator);
    }
    else if (dims == 3)
    {
        top_blob.create(w, h, channels, out_elemsize, elempack, opt.blob_allocator);
    }
    else if (dims == 4)
    {
        top_blob.create(w, h, d, channels, out_elemsize, elempack, opt.blob_allocator);
    }
    if (top_blob.empty())
        return -100;

    int size = w * h * d * elempack;

    if (type_from == 1 && type_to == 2)
    {
        #pragma omp parallel for num_threads(opt.num_threads)
        for (int q = 0; q < channels; q++)
        {
            const float* ptr = bottom_blob.channel(q);
            unsigned short* outptr = top_blob.channel(q);

            int i = 0;
#if __loongarch_sx
            for (; i + 7 < size; i += 8)
            {
                __builtin_prefetch(ptr + 16);
                __m128 _p0 = (__m128)__lsx_vld(ptr, 0);
                __m128 _p1 = (__m128)__lsx_vld(ptr + 4, 0);
                __m128i _p = __lsx_vfcvt_h_s(_p1, _p0);
                __lsx_vst(_p, outptr, 0);

                ptr += 8;
                outptr += 8;
            }
#endif // __loongarch_sx
            for (; i < size; i++)
            {
                *outptr = float32_to_float16(*ptr);
                outptr++;
                ptr++;
            }
        }
    }

    if (type_from == 2 && type_to == 1)
    {
        #pragma omp parallel for num_threads(opt.num_threads)
        for (int q = 0; q < channels; q++)
        {
            const unsigned short* ptr = bottom_blob.channel(q);
            float* outptr = top_blob.channel(q);

            int i = 0;
#if __loongarch_sx
            for (; i + 7 < size; i += 8)
            {
                __builtin_prefetch(ptr + 16);
                __m128i _p = __lsx_vld(ptr, 0);
                __m128 _p0 = __lsx_vfcvtl_s_h(_p);
                __m128 _p1 = __lsx_vfcvth_s_h(_p);
                __lsx_vst(_p0, outptr, 0);
                __lsx_vst(_p1, outptr + 4, 0);

                ptr += 8;
                outptr += 8;
            }
#endif // __loongarch_sx
            for (; i < size; i++)
            {
                *outptr = float16_to_float32(*ptr);
                outptr++;
                ptr++;
            }
        }
    }

    if (type_from == 3 && type_to == 1)
    {
        #pragma omp parallel for num_threads(opt.num_threads)
        for (int q = 0; q < channels; q++)
        {
            const signed char* ptr = bottom_blob.channel(q);
            float* outptr = top_blob.channel(q);

            for (int i = 0; i < size; i++)
            {
                outptr[i] = (float)ptr[i];
            }
        }
    }

    if (type_from == 4 && type_to == 1)
    {
        #pragma omp parallel for num_threads(opt.num_threads)
        for (int q = 0; q < channels; q++)
        {
            const unsigned short* ptr = bottom_blob.channel(q);
            float* outptr = top_blob.channel(q);

            int i = 0;
            for (; i < size; i++)
            {
                *outptr = bfloat16_to_float32(*ptr);
                outptr++;
                ptr++;
            }
        }
    }

    if (type_from == 1 && type_to == 4)
    {
        #pragma omp parallel for num_threads(opt.num_threads)
        for (int q = 0; q < channels; q++)
        {
            const float* ptr = bottom_blob.channel(q);
            unsigned short* outptr = top_blob.channel(q);

            int i = 0;
            for (; i < size; i++)
            {
                *outptr = float32_to_bfloat16(*ptr);
                outptr++;
                ptr++;
            }
        }
    }

    return 0;
}

} // namespace ncnn

	// yala is pleased to support the open source community by making ncnn available.
	//
	//
	// Copyright (C) 2022 yala <zhaojunchao@loongson.cn>;<junchao82@qq.com>. All rights reserved.
	// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
	// in compliance with the License. You may obtain a copy of the License at
	//
	// https://opensource.org/licenses/BSD-3-Clause
	//
	// Unless required by applicable law or agreed to in writing, software distributed
	// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
	// CONDITIONS OF ANY KIND, either express or implied. See the License for the
	// specific language governing permissions and limitations under the License.

	#include "cast_loongarch.h"

	#if __loongarch_sx
	#include <lsxintrin.h>
	#endif // __loongarch_sx

	namespace ncnn {

	Cast_loongarch::Cast_loongarch()
	{
	support_packing = true;
	}

	int Cast_loongarch::forward(const Mat& bottom_blob, Mat& top_blob, const Option& opt) const
	{
	if (type_from == type_to)
	{
	top_blob = bottom_blob;
	return 0;
	}

	int w = bottom_blob.w;
	int h = bottom_blob.h;
	int d = bottom_blob.d;
	int channels = bottom_blob.c;
	int dims = bottom_blob.dims;
	size_t elemsize = bottom_blob.elemsize;
	int elempack = bottom_blob.elempack;

	size_t out_elemsize = elemsize;
	if (type_to == 1)
	{
	if (type_from == 3)
	{
	Cast::forward(bottom_blob, top_blob, opt);
	}

	// float32
	out_elemsize = 4 * elempack;
	}
	else if (type_to == 2)
	{
	// float16
	out_elemsize = 2 * elempack;
	}
	else if (type_to == 3)
	{
	// int8
	out_elemsize = elempack;
	}
	else if (type_to == 4)
	{
	// bfloat16
	out_elemsize = 2 * elempack;
	}

	if (dims == 1)
	{
	top_blob.create(w, out_elemsize, elempack, opt.blob_allocator);
	}
	else if (dims == 2)
	{
	top_blob.create(w, h, out_elemsize, elempack, opt.blob_allocator);
	}
	else if (dims == 3)
	{
	top_blob.create(w, h, channels, out_elemsize, elempack, opt.blob_allocator);
	}
	else if (dims == 4)
	{
	top_blob.create(w, h, d, channels, out_elemsize, elempack, opt.blob_allocator);
	}
	if (top_blob.empty())
	return -100;

	int size = w * h * d * elempack;

	if (type_from == 1 && type_to == 2)
	{
	#pragma omp parallel for num_threads(opt.num_threads)
	for (int q = 0; q < channels; q++)
	{
	const float* ptr = bottom_blob.channel(q);
	unsigned short* outptr = top_blob.channel(q);

	int i = 0;
	#if __loongarch_sx
	for (; i + 7 < size; i += 8)
	{
	__builtin_prefetch(ptr + 16);
	__m128 _p0 = (__m128)__lsx_vld(ptr, 0);
	__m128 _p1 = (__m128)__lsx_vld(ptr + 4, 0);
	__m128i _p = __lsx_vfcvt_h_s(_p1, _p0);
	__lsx_vst(_p, outptr, 0);

	ptr += 8;
	outptr += 8;
	}
	#endif // __loongarch_sx
	for (; i < size; i++)
	{
	outptr = float32_to_float16(ptr);
	outptr++;
	ptr++;
	}
	}
	}

	if (type_from == 2 && type_to == 1)
	{
	#pragma omp parallel for num_threads(opt.num_threads)
	for (int q = 0; q < channels; q++)
	{
	const unsigned short* ptr = bottom_blob.channel(q);
	float* outptr = top_blob.channel(q);

	int i = 0;
	#if __loongarch_sx
	for (; i + 7 < size; i += 8)
	{
	__builtin_prefetch(ptr + 16);
	__m128i _p = __lsx_vld(ptr, 0);
	__m128 _p0 = __lsx_vfcvtl_s_h(_p);
	__m128 _p1 = __lsx_vfcvth_s_h(_p);
	__lsx_vst(_p0, outptr, 0);
	__lsx_vst(_p1, outptr + 4, 0);

	ptr += 8;
	outptr += 8;
	}
	#endif // __loongarch_sx
	for (; i < size; i++)
	{
	outptr = float16_to_float32(ptr);
	outptr++;
	ptr++;
	}
	}
	}

	if (type_from == 3 && type_to == 1)
	{
	#pragma omp parallel for num_threads(opt.num_threads)
	for (int q = 0; q < channels; q++)
	{
	const signed char* ptr = bottom_blob.channel(q);
	float* outptr = top_blob.channel(q);

	for (int i = 0; i < size; i++)
	{
	outptr[i] = (float)ptr[i];
	}
	}
	}

	if (type_from == 4 && type_to == 1)
	{
	#pragma omp parallel for num_threads(opt.num_threads)
	for (int q = 0; q < channels; q++)
	{
	const unsigned short* ptr = bottom_blob.channel(q);
	float* outptr = top_blob.channel(q);

	int i = 0;
	for (; i < size; i++)
	{
	outptr = bfloat16_to_float32(ptr);
	outptr++;
	ptr++;
	}
	}
	}

	if (type_from == 1 && type_to == 4)
	{
	#pragma omp parallel for num_threads(opt.num_threads)
	for (int q = 0; q < channels; q++)
	{
	const float* ptr = bottom_blob.channel(q);
	unsigned short* outptr = top_blob.channel(q);

	int i = 0;
	for (; i < size; i++)
	{
	outptr = float32_to_bfloat16(ptr);
	outptr++;
	ptr++;
	}
	}
	}

	return 0;
	}

	} // namespace ncnn