forked from mrq/bitsandbytes-rocm
181 lines
6.4 KiB
Plaintext
181 lines
6.4 KiB
Plaintext
// Copyright (c) Facebook, Inc. and its affiliates.
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//
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// This source code is licensed under the MIT license found in the
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// LICENSE file in the root directory of this source tree.
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#ifndef ops_H
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#define ops_H
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#include <stdio.h>
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#include <iostream>
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#include <unistd.h>
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#include <assert.h>
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#include <hip/hip_runtime_api.h>
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#include <hip/hip_fp16.h>
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#include <hipblas.h>
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// #include <cublasLt.h>
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#include <hipsparse.h>
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#include <vector>
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#include <functional>
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typedef struct cublasLtContext* cublasLtHandle_t;
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#define CUDA_CHECK_RETURN(value) { \
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hipError_t _m_cudaStat = value; \
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if (_m_cudaStat != hipSuccess) { \
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fprintf(stderr, "Error %s at line %d in file %s\n", \
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hipGetErrorString(_m_cudaStat), __LINE__, __FILE__); \
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exit(1); \
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} }
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#define THREADS_PER_BLOCKS (512)
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#define CHECK_CUSPARSE(value) { \
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hipsparseStatus_t _m_cudaStat = value; \
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if (_m_cudaStat != HIPSPARSE_STATUS_SUCCESS) { \
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fprintf(stderr, "Error <sparse error> at line %d in file %s\n", \
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__LINE__, __FILE__); \
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exit(1); \
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} }
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#define THREADS_PER_BLOCKS (512)
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inline void checkCudaStatus(hipError_t status) {
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if (status != hipSuccess) {
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printf("cuda API failed with status %d: %s\n", status, hipGetErrorString(status));
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throw std::logic_error("cuda API failed");
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}
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}
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inline int checkCublasStatus(hipblasStatus_t status) {
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if (status != HIPBLAS_STATUS_SUCCESS) {
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printf("cuBLAS API failed with status %d\n", status);
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//throw std::logic_error("cuBLAS API failed");
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return 1;
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}
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return 0;
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}
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typedef enum Operations_t
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{
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ksmul = 0,
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} Operations_t;
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typedef enum Optimizer_t
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{
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ADAM = 0,
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MOMENTUM = 1,
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RMSPROP = 2,
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LARS = 3,
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ADAGRAD = 4,
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} Optimizer_t;
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typedef enum Transform_t
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{
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ROW = 0,
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COL = 1,
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COL32 = 2,
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COL_TURING = 3,
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COL_AMPERE = 4,
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} Transform_t;
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class Context
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{
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public:
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hipblasHandle_t m_handle;
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Context()
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{
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hipblasHandle_t handle;
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hipblasCreate(&handle);
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m_handle = handle;
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}
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};
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// class ContextLt
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// {
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// public:
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// cublasLtHandle_t m_handle;
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// ContextLt()
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// {
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// cublasLtHandle_t handle;
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// cublasLtCreate(&handle);
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// m_handle = handle;
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// }
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// };
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class ContextCusparse
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{
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public:
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hipsparseHandle_t m_handle;
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ContextCusparse()
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{
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hipsparseHandle_t handle;
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hipsparseCreate(&handle);
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m_handle = handle;
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}
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};
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template <typename T> void estimateQuantiles(T *A, float *code, float offset, int n);
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void quantize(float *code, float *A, unsigned char *out, int n);
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void dequantize(float *code, unsigned char *A, float *out, int n);
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template <typename T, int STOCHASTIC> void quantizeBlockwise(float * code, T *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
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template<typename T> void dequantizeBlockwise(float *code, unsigned char *A, float *absmax, T *out, int block_size, const int n);
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template<typename T, int OPTIMIZER> void optimizer32bit(T* g, T* p,
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float* state1, float* state2, float *unorm, float max_unorm, float param_norm,
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float beta1, float beta2, float eps, float weight_decay,
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int step, float lr, const float gnorm_scale, bool skip_zeros, int n);
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template<typename T, int OPTIMIZER> void optimizerStatic8bit(T* p, T* g, unsigned char* state1, unsigned char* state2,
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float *unorm, float max_unorm, float param_norm,
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float beta1, float beta2,
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float eps, int step, float lr,
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float* quantiles1, float* quantiles2,
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float* max1, float* max2, float* new_max1, float* new_max2,
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float weight_decay,
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const float gnorm_scale, int n);
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template<typename T, int OPTIMIZER> void optimizerStatic8bitBlockwise(T* p, T* g,
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unsigned char* state1, unsigned char* state2, float beta1, float beta2, float eps, int step, float lr,
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float* quantiles1, float* quantiles2, float* absmax1, float* absmax2, float weight_decay, const float gnorm_scale,
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bool skip_zeros, int n);
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template<typename T> void percentileClipping(T * g, float *gnorm_vec, int step, const int n);
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void histogramScatterAdd2D(float* histogram, int *index1, int *index2, float *src, int maxidx1, int n);
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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);
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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,
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long long int strideA, long long int strideB, long long int strideC, int batchCount);
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template <int FORMATB, 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);
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template <typename T, int SRC, int TARGET, bool transpose, int DTYPE> void transform(cublasLtHandle_t ltHandle, T *A, T *out, int dim1, int dim2);
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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);
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void dequant_mm_int32_fp16(int *A, float *rowStats, float *colStats, half *out, float* newRowStats, float* newcolStats, half* bias, int numRows, int numCols);
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void getColRowStats(half * A, float *rowStats, float *colStats, int *nnz_count_row, float nnz_threshold, int rows, int cols);
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void doubleRowColQuant(half * A, float *rowStats, float *colStats, char *out_col_normed, char *out_row_normed,
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int *rowidx, int *colidx, half *val, int *nnz_block_ptr, float threshold, int rows, int cols);
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template <int FORMAT, int TRANSPOSE> void transformRowToFormat(char * A, char *out, int rows, int cols);
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void spmm_coo(hipsparseHandle_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);
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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);
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template <int FORMAT> void extractOutliers(char * A, int *idx, char *out, int idx_size, int rows, int cols);
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#endif
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