ops.cuh

// Copyright (c) Facebook, Inc. and its affiliates.
//
// This source code is licensed under the MIT license found in the
// LICENSE file in the root directory of this source tree.


#ifndef ops_H
#define ops_H

#include <cstdint>
#include <stdio.h>
#include <iostream>
#include <assert.h>

#include <cuda_runtime_api.h>
#include <cuda_fp16.h>
#include <cublas_v2.h>
#include <cublasLt.h>
#include <cusparse.h>
#include <vector>
#include <functional>


#define CUDA_CHECK_RETURN(value) {                      \
  cudaError_t _m_cudaStat = value;                    \
  if (_m_cudaStat != cudaSuccess) {                   \
    fprintf(stderr, "Error %s at line %d in file %s\n",         \
        cudaGetErrorString(_m_cudaStat), __LINE__, __FILE__);   \
    exit(1);                              \
  } }


#define CHECK_CUSPARSE(value) {                      \
  cusparseStatus_t _m_cudaStat = value;                    \
  if (_m_cudaStat != CUSPARSE_STATUS_SUCCESS) {                   \
    fprintf(stderr, "Error %s at line %d in file %s\n",         \
        cusparseGetErrorString(_m_cudaStat), __LINE__, __FILE__);   \
    exit(1);                              \
  } }


inline void checkCudaStatus(cudaError_t status) {
    if (status != cudaSuccess) {
        printf("cuda API failed with status %d: %s\n", status, cudaGetErrorString(status));
        throw std::logic_error("cuda API failed");
    }
}

inline int checkCublasStatus(cublasStatus_t status) {
    if (status != CUBLAS_STATUS_SUCCESS) {
        printf("cuBLAS API failed with status %d\n", status);
        //throw std::logic_error("cuBLAS API failed");
        return 1;
    }
    return 0;
}

typedef enum Operations_t
{
	ksmul = 0,
} Operations_t;

typedef enum Optimizer_t
{
	ADAM = 0,
	MOMENTUM = 1,
  RMSPROP = 2,
  LARS = 3,
  ADAGRAD = 4,
  LION = 5,
  ADEMAMIX = 6
} Optimizer_t;

typedef enum Transform_t
{
	ROW = 0,
	COL = 1,
  COL32 = 2,
  COL_TURING = 3,
  COL_AMPERE = 4,
} Transform_t;

typedef enum DataType_t
{
	General8bit = 0,
	FP4 = 1,
  NF4 = 2,
} DataType_t;

typedef enum Funcs_t
{
	FILL = 0,
	ARANGE = 1,
	_MUL = 2,
} Funcs_t;

class Context
{
    public:
				cublasHandle_t m_handle;

				Context()
				{
					cublasHandle_t handle;
					cublasCreate_v2(&handle);
					m_handle = handle;
				}

};

class ContextLt
{
    public:
				cublasLtHandle_t m_handle;

				ContextLt()
				{
					cublasLtHandle_t handle;
					cublasLtCreate(&handle);
					m_handle = handle;
				}

};

class ContextCusparse
{
    public:
				cusparseHandle_t m_handle;

				ContextCusparse()
				{
					cusparseHandle_t handle;
					cusparseCreate(&handle);
					m_handle = handle;
				}

};


template <typename T> void estimateQuantiles(T *A, float *code, float offset, int n);

void quantize(float *code, float *A, unsigned char *out, int n);
void dequantize(float *code, unsigned char *A, float *out, int n, cudaStream_t stream);
template <typename T, int STOCHASTIC, int DATA_TYPE> void quantizeBlockwise(float * code, T *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
template<typename T, int DATA_TYPE> void dequantizeBlockwise(float *code, unsigned char *A, float *absmax, T *out, int block_size, const int n, cudaStream_t stream);

template<typename T, int OPTIMIZER> void optimizer32bit(T* g, T* p,
                float* state1, float* state2, float *unorm, float max_unorm, float param_norm,
                float beta1, float beta2, float beta3, float alpha, float eps, float weight_decay,
                int step, float lr, const float gnorm_scale, bool skip_zeros, int n);

template<typename T, int OPTIMIZER> void optimizerStatic8bit(T* p, T* g, unsigned char* state1, unsigned char* state2,
                float *unorm, float max_unorm, float param_norm,
                float beta1, float beta2,
                float eps, int step, float lr,
                float* quantiles1, float* quantiles2,
                float* max1, float* max2, float* new_max1, float* new_max2,
                float weight_decay,
                const float gnorm_scale, int n);

template<typename T, int OPTIMIZER> void optimizerStatic8bitBlockwise(T* p, T* g,
                unsigned char* state1, unsigned char* state2, float beta1, float beta2, float beta3, float alpha, float eps, int step, float lr,
                float* quantiles1, float* quantiles2, float* absmax1, float* absmax2, float weight_decay, const float gnorm_scale,
								bool skip_zeros, int n);

template<typename T> void percentileClipping(T * g, float *gnorm_vec, int step, const int n);

void histogramScatterAdd2D(float* histogram, int *index1, int *index2, float *src, int maxidx1, int n);

void gemmex(Context * context, bool transposeA, bool transposeB, int m, int n, int k, void *A, void *B, void *C, int lda, int ldb, int ldc);
void strided_gemmex(Context *context, bool transposeA, bool transposeB, int m, int n, int k, void *A, void *B, void *C, int lda, int ldb, int ldc,
                    long long int strideA, long long int strideB, long long int strideC, int batchCount);

template <int DTYPE_OUT, int SCALE_ROWS> int igemmlt(cublasLtHandle_t ltHandle, int m, int n, int k, const int8_t *A, const int8_t *B, void *C, float *row_scale, int lda, int ldb, int ldc, cudaStream_t stream);

void cutlass_igemm(bool transposeA, bool transposeB, int m, int n, int k, void *A, void *B, void *C, int lda, int ldb, int ldc);
void dequant_mm_int32_fp16(int *A, float *rowStats, float *colStats, half *out, half* bias, int numRows, int numCols, cudaStream_t stream);
void getRowStats(half *A, float *rowStats, float threshold, int rows, int cols, cudaStream_t stream);
void int8VectorQuant(half * __restrict__ A, int8_t *out, float *rowStats, float threshold, int rows, int cols, cudaStream_t stream);

void spmm_coo(cusparseHandle_t handle, int *A_rowidx, int *A_colidx, half *A_vals, int A_nnz, int A_rows, int A_cols, int B_cols, int ldb, half *B, int ldc, half* C, bool transposed_B);

template <typename T, int BITS> void spmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, T *B, half *out, float *dequant_stats, int nnz_rows, int nnz, int rowsA, int rowsB, int colsB);

void matmul4bite(half *A, unsigned char *B, half*out, int lda, int ldb, int rowsA, int colsA, int colsB);

template <typename T> void gemm_host(int m, int n, int k, T * A,  T* B,  T * out,  int lda, int ldb, int ldc, int bits);
template <typename T> void gemm_4bit_inference(int m, int n, int k, T * A,  unsigned char* B,  float *absmax, T * out,  int lda, int ldb, int ldc, int blocksize);
template <typename T, int BITS> void gemm_4bit_inference_naive(int m, int n, int k, T * A,  unsigned char* B,  float *absmax, float *datatype, T * out,  int lda, int ldb, int ldc, int blocksize, cudaStream_t stream);

template <typename T, int FUNC> void func(T *A, T *B, T value, long n);

#endif