/* NiuTrans.Tensor - an open-source tensor library
 * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. 
 * All rights reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * 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.
 */

/*
 * $Created by: LI Yinqiao (li.yin.qiao.2012@hotmail.com) 2018-7-11
 */

#include "../../XTensor.h"
#include "../../XDevice.h"
#include "ConvertDataType.cuh"

namespace nts { // namespace nts(NiuTrans.Tensor)

#ifdef USE_CUDA
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <cuda_fp16.h>

__global__ 
void KernelFloatToFloat16(float * s, __half * t, int size)
{
    int i = blockDim.x * blockIdx.x + threadIdx.x;

    if (i < size){
        t[i] = __float2half(s[i]);
    }
}

__global__ 
void KernelFloat16ToFloat(__half * s, float * t, int size)
{
    int i = blockDim.x * blockIdx.x + threadIdx.x;

    if (i < size){
        t[i] = __half2float(s[i]);
    }
}

__global__ 
void KernelFloatToInt(float * inputData, int * outputData, int size)
{
    int i = blockDim.x * blockIdx.x + threadIdx.x;

    if (i < size){
        outputData[i] = (int)(inputData[i]);
    }
}

__global__ 
void KernelIntToFloat(int * inputData, float * outputData, int size)
{
    int i = blockDim.x * blockIdx.x + threadIdx.x;

    if (i < size){
        outputData[i] = (float)(inputData[i]);
    }
}

/* 
data conversion (cuda code) 
>> devID - device id
>> s - source data array
>> typeS - source data type
>> t - target data array
>> typeT - target data type
>> size - number of the items in s (and t)
*/
void _CudaConvertDataType(int devID, void * s, TENSOR_DATA_TYPE typeS, void * t, TENSOR_DATA_TYPE typeT, int size)
{
    CheckNTErrors((devID >= 0), "This code must be run on GPUs!");
    if(typeS == typeT)
        return;
    int gridSize[3];
    int blockSize[3];
    GDevs.GetCudaThread(devID, size, gridSize, blockSize);
    dim3 blocks(gridSize[0]);
    dim3 threads(blockSize[0]);
    int devIDBackup;
    ProtectCudaDev(devID, devIDBackup);
    if(typeS == X_FLOAT && typeT == X_FLOAT16)
        KernelFloatToFloat16<<<blocks, threads>>>((float*)s, (__half*)t, size);
    else if(typeS == X_FLOAT16 && typeT == X_FLOAT)
        KernelFloat16ToFloat<<<blocks, threads>>>((__half*)s, (float*)t, size);
    else{
        ShowNTErrors("Unsupported data types for conversion!");
    }
    ProtectCudaDev(devID, devIDBackup);
}
/*
convert data type (cuda code) 
>> input - input tensor
>> output - output tensor
*/
void _CudaConvertDataType(const XTensor * input, XTensor * output)
{
    if (input->dataType == output->dataType)
        return;

    int gridSize[3];
    int blockSize[3];

    GDevs.GetCudaThread(input->devID, input->unitNum, gridSize, blockSize);

    dim3 blocks(gridSize[0]);
    dim3 threads(blockSize[0]);

    int devIDBackup;
    ProtectCudaDev(input->devID, devIDBackup);

    if(input->dataType == X_FLOAT && output->dataType == X_INT)
        KernelFloatToInt<<<blocks, threads>>>
                         ((float*)input->data, (int*)output->data, input->unitNum);
    else if(input->dataType == X_INT && output->dataType == X_FLOAT)
        KernelIntToFloat<<<blocks, threads>>>
                         ((int*)input->data, (float*)output->data, input->unitNum);
    else if(input->dataType == X_FLOAT && output->dataType == X_FLOAT16)
        KernelFloatToFloat16<<<blocks, threads>>>
                             ((float*)input->data, (__half*)output->data, input->unitNum);
    else if(input->dataType == X_FLOAT16 && output->dataType == X_FLOAT)
        KernelFloat16ToFloat<<<blocks, threads>>>
                             ((__half*)input->data, (float*)output->data, input->unitNum);
    else{
        ShowNTErrors("Unsupported data types for conversion!");
    }

    ProtectCudaDev(input->devID, devIDBackup);
}

#endif // USE_CUDA

} // namespace nts(NiuTrans.Tensor)
	.file	"tmpxft_002c24b3_00000000-6_ConvertDataType.cudafe1.cpp"
	.text
#APP

#NO_APP
	.type	_ZL26__cudaUnregisterBinaryUtilv, @function
_ZL26__cudaUnregisterBinaryUtilv:
.LFB3060:
	.cfi_startproc
	endbr64
	movq	_ZL20__cudaFatCubinHandle(%rip), %rdi
	jmp	__cudaUnregisterFatBinary@PLT
	.cfi_endproc
.LFE3060:
	.size	_ZL26__cudaUnregisterBinaryUtilv, .-_ZL26__cudaUnregisterBinaryUtilv
	.section	.text.startup,"ax",@progbits
	.type	_ZL24__sti____cudaRegisterAllv, @function
_ZL24__sti____cudaRegisterAllv:
.LFB3083:
	.cfi_startproc
	endbr64
	pushq	%rax
	.cfi_def_cfa_offset 16
	leaq	_ZL15__fatDeviceText(%rip), %rdi
	call	__cudaRegisterFatBinary@PLT
	movq	%rax, %rdi
	movq	%rax, _ZL20__cudaFatCubinHandle(%rip)
	call	__cudaRegisterFatBinaryEnd@PLT
	leaq	_ZL26__cudaUnregisterBinaryUtilv(%rip), %rdi
	popq	%rdx
	.cfi_def_cfa_offset 8
	jmp	atexit@PLT
	.cfi_endproc
.LFE3083:
	.size	_ZL24__sti____cudaRegisterAllv, .-_ZL24__sti____cudaRegisterAllv
	.section	.init_array,"aw"
	.align 8
	.quad	_ZL24__sti____cudaRegisterAllv
	.section	.nvFatBinSegment,"aw"
	.align 8
	.type	_ZL15__fatDeviceText, @object
	.size	_ZL15__fatDeviceText, 24
_ZL15__fatDeviceText:
	.long	1180844977
	.long	1
	.quad	fatbinData
	.quad	0
	.local	_ZL20__cudaFatCubinHandle
	.comm	_ZL20__cudaFatCubinHandle,8,8
	.ident	"GCC: (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0"
	.section	.note.GNU-stack,"",@progbits
	.section	.note.gnu.property,"a"
	.align 8
	.long	1f - 0f
	.long	4f - 1f
	.long	5
0:
	.string	"GNU"
1:
	.align 8
	.long	0xc0000002
	.long	3f - 2f
2:
	.long	0x3
3:
	.align 8
4:

/* NiuTrans.Tensor - an open-source tensor library
 * Copyright (C) 2017, Natural Language Processing Lab, Northeastern University. 
 * All rights reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * 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.
 */

/*
 * $Created by: LI Yinqiao (li.yin.qiao.2012@hotmail.com) 2018-7-11
 */

#include "../../XTensor.h"
#include "../../XDevice.h"
#include "ConvertDataType.cuh"

namespace nts { // namespace nts(NiuTrans.Tensor)

#ifdef USE_CUDA
#include <hip/hip_runtime.h>
#include <hipblas.h>
#include <hip/hip_fp16.h>

__global__ 
void KernelFloatToFloat16(float * s, __half * t, int size)
{
    int i = blockDim.x * blockIdx.x + threadIdx.x;

    if (i < size){
        t[i] = __float2half(s[i]);
    }
}

__global__ 
void KernelFloat16ToFloat(__half * s, float * t, int size)
{
    int i = blockDim.x * blockIdx.x + threadIdx.x;

    if (i < size){
        t[i] = __half2float(s[i]);
    }
}

__global__ 
void KernelFloatToInt(float * inputData, int * outputData, int size)
{
    int i = blockDim.x * blockIdx.x + threadIdx.x;

    if (i < size){
        outputData[i] = (int)(inputData[i]);
    }
}

__global__ 
void KernelIntToFloat(int * inputData, float * outputData, int size)
{
    int i = blockDim.x * blockIdx.x + threadIdx.x;

    if (i < size){
        outputData[i] = (float)(inputData[i]);
    }
}

/* 
data conversion (cuda code) 
>> devID - device id
>> s - source data array
>> typeS - source data type
>> t - target data array
>> typeT - target data type
>> size - number of the items in s (and t)
*/
void _CudaConvertDataType(int devID, void * s, TENSOR_DATA_TYPE typeS, void * t, TENSOR_DATA_TYPE typeT, int size)
{
    CheckNTErrors((devID >= 0), "This code must be run on GPUs!");
    if(typeS == typeT)
        return;
    int gridSize[3];
    int blockSize[3];
    GDevs.GetCudaThread(devID, size, gridSize, blockSize);
    dim3 blocks(gridSize[0]);
    dim3 threads(blockSize[0]);
    int devIDBackup;
    ProtectCudaDev(devID, devIDBackup);
    if(typeS == X_FLOAT && typeT == X_FLOAT16)
        KernelFloatToFloat16<<<blocks, threads>>>((float*)s, (__half*)t, size);
    else if(typeS == X_FLOAT16 && typeT == X_FLOAT)
        KernelFloat16ToFloat<<<blocks, threads>>>((__half*)s, (float*)t, size);
    else{
        ShowNTErrors("Unsupported data types for conversion!");
    }
    ProtectCudaDev(devID, devIDBackup);
}
/*
convert data type (cuda code) 
>> input - input tensor
>> output - output tensor
*/
void _CudaConvertDataType(const XTensor * input, XTensor * output)
{
    if (input->dataType == output->dataType)
        return;

    int gridSize[3];
    int blockSize[3];

    GDevs.GetCudaThread(input->devID, input->unitNum, gridSize, blockSize);

    dim3 blocks(gridSize[0]);
    dim3 threads(blockSize[0]);

    int devIDBackup;
    ProtectCudaDev(input->devID, devIDBackup);

    if(input->dataType == X_FLOAT && output->dataType == X_INT)
        KernelFloatToInt<<<blocks, threads>>>
                         ((float*)input->data, (int*)output->data, input->unitNum);
    else if(input->dataType == X_INT && output->dataType == X_FLOAT)
        KernelIntToFloat<<<blocks, threads>>>
                         ((int*)input->data, (float*)output->data, input->unitNum);
    else if(input->dataType == X_FLOAT && output->dataType == X_FLOAT16)
        KernelFloatToFloat16<<<blocks, threads>>>
                             ((float*)input->data, (__half*)output->data, input->unitNum);
    else if(input->dataType == X_FLOAT16 && output->dataType == X_FLOAT)
        KernelFloat16ToFloat<<<blocks, threads>>>
                             ((__half*)input->data, (float*)output->data, input->unitNum);
    else{
        ShowNTErrors("Unsupported data types for conversion!");
    }

    ProtectCudaDev(input->devID, devIDBackup);
}

#endif // USE_CUDA

} // namespace nts(NiuTrans.Tensor)
	.text
	.file	"ConvertDataType.hip"
	.type	__hip_cuid_,@object             # @__hip_cuid_
	.bss
	.globl	__hip_cuid_
__hip_cuid_:
	.byte	0                               # 0x0
	.size	__hip_cuid_, 1

	.ident	"AMD clang version 18.0.0git (https://github.com/RadeonOpenCompute/llvm-project roc-6.3.2 25012 e5bf7e55c91490b07c49d8960fa7983d864936c4)"
	.section	".note.GNU-stack","",@progbits
	.addrsig
	.addrsig_sym __hip_cuid_

	.text
	.p2alignl 7, 3214868480
	.fill 96, 4, 3214868480
	.type	__hip_cuid_,@object             ; @__hip_cuid_
	.section	.bss,"aw",@nobits
	.globl	__hip_cuid_
__hip_cuid_:
	.byte	0                               ; 0x0
	.size	__hip_cuid_, 1

	.ident	"AMD clang version 18.0.0git (https://github.com/RadeonOpenCompute/llvm-project roc-6.3.2 25012 e5bf7e55c91490b07c49d8960fa7983d864936c4)"
	.section	".note.GNU-stack","",@progbits
	.addrsig
	.addrsig_sym __hip_cuid_
	.amdgcn_target "amdgcn-amd-amdhsa--gfx1100"
	.amdgpu_metadata
---
amdhsa.kernels:  []
amdhsa.target:   amdgcn-amd-amdhsa--gfx1100
amdhsa.version:
  - 1
  - 2
...

	.end_amdgpu_metadata
