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Commit 9268dc9d authored by Tim Dettmers's avatar Tim Dettmers
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Some progress on build script; added multi-cuda install script.

parent 1e88edd8
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Showing with 192 additions and 99 deletions
CHANGELOG.md
View file @ 9268dc9d
...@@ -64,3 +64,6 @@ Features: ...@@ -64,3 +64,6 @@ Features:
- Added 8-bit Linear layers with 8-bit Params that perform memory efficient inference with an option for 8-bit mixed precision matrix decomposition for inference without performance degradation - Added 8-bit Linear layers with 8-bit Params that perform memory efficient inference with an option for 8-bit mixed precision matrix decomposition for inference without performance degradation
- Added quantization methods for "fake" quantization as well as optimized kernels vector-wise quantization and equalization as well as optimized cuBLASLt transformations - Added quantization methods for "fake" quantization as well as optimized kernels vector-wise quantization and equalization as well as optimized cuBLASLt transformations
- CPU only build now available (Thank you, @mryab) - CPU only build now available (Thank you, @mryab)
Deprecated:
- Pre-compiled release for CUDA 9.2, 10.0, 10.2 no longer available
Makefile
View file @ 9268dc9d
...@@ -27,13 +27,14 @@ COMPUTE_CAPABILITY += -gencode arch=compute_60,code=sm_60 # Pascal ...@@ -27,13 +27,14 @@ COMPUTE_CAPABILITY += -gencode arch=compute_60,code=sm_60 # Pascal
COMPUTE_CAPABILITY += -gencode arch=compute_61,code=sm_61 # Pascal COMPUTE_CAPABILITY += -gencode arch=compute_61,code=sm_61 # Pascal
COMPUTE_CAPABILITY += -gencode arch=compute_70,code=sm_70 # Volta COMPUTE_CAPABILITY += -gencode arch=compute_70,code=sm_70 # Volta
COMPUTE_CAPABILITY += -gencode arch=compute_72,code=sm_72 # Volta COMPUTE_CAPABILITY += -gencode arch=compute_72,code=sm_72 # Volta
COMPUTE_CAPABILITY := -gencode arch=compute_75,code=sm_75 # Volta
# CUDA 9.2 supports CC 3.0, but CUDA >= 11.0 does not # CUDA 9.2 supports CC 3.0, but CUDA >= 11.0 does not
CC_CUDA92 := -gencode arch=compute_30,code=sm_30 CC_CUDA92 := -gencode arch=compute_30,code=sm_30
# Later versions of CUDA support the new architectures # Later versions of CUDA support the new architectures
CC_CUDA10x := -gencode arch=compute_30,code=sm_30 CC_CUDA10x := -gencode arch=compute_30,code=sm_30
CC_CUDA10x += -gencode arch=compute_75,code=sm_75 CC_CUDA10x := -gencode arch=compute_75,code=sm_75
CC_CUDA110 := -gencode arch=compute_75,code=sm_75 CC_CUDA110 := -gencode arch=compute_75,code=sm_75
CC_CUDA110 += -gencode arch=compute_80,code=sm_80 CC_CUDA110 += -gencode arch=compute_80,code=sm_80
...@@ -43,12 +44,12 @@ CC_CUDA11x += -gencode arch=compute_80,code=sm_80 ...@@ -43,12 +44,12 @@ CC_CUDA11x += -gencode arch=compute_80,code=sm_80
CC_CUDA11x += -gencode arch=compute_86,code=sm_86 CC_CUDA11x += -gencode arch=compute_86,code=sm_86
all: $(ROOT_DIR)/dependencies/cub $(BUILD_DIR) env all: $(ROOT_DIR)/dependencies/cub $(BUILD_DIR) env
$(NVCC) $(COMPUTE_CAPABILITY) -Xcompiler '-fPIC' --use_fast_math -Xptxas=-v -dc $(FILES_CUDA) $(INCLUDE) $(LIB) --output-directory $(BUILD_DIR) $(NVCC) $(COMPUTE_CAPABILITY) -Xcompiler '-fPIC' --use_fast_math -Xptxas=-v -dc $(FILES_CUDA) $(INCLUDE) $(LIB) --output-directory $(BUILD_DIR) -D NO_CUBLASLT
$(NVCC) $(COMPUTE_CAPABILITY) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o $(NVCC) $(COMPUTE_CAPABILITY) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o
$(GPP) -std=c++14 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes.so $(LIB) $(GPP) -std=c++14 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes.so $(LIB)
cuda92: $(ROOT_DIR)/dependencies/cub $(BUILD_DIR) env cuda92: $(ROOT_DIR)/dependencies/cub $(BUILD_DIR) env
$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA92) -Xcompiler '-fPIC' --use_fast_math -Xptxas=-v -dc $(FILES_CUDA) $(INCLUDE) $(LIB) --output-directory $(BUILD_DIR) $(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA92) -Xcompiler '-fPIC' --use_fast_math -Xptxas=-v -dc $(FILES_CUDA) $(INCLUDE) $(LIB) --output-directory $(BUILD_DIR) -D NO_CUBLASLT
$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA92) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o $(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA92) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o
$(GPP) -std=c++14 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes.so $(LIB) $(GPP) -std=c++14 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes.so $(LIB)
......
csrc/kernels.cu
View file @ 9268dc9d
...@@ -2166,7 +2166,6 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T ...@@ -2166,7 +2166,6 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T
__shared__ char smem_data[32*33*ITEMS_PER_THREAD]; __shared__ char smem_data[32*33*ITEMS_PER_THREAD];
char local_data[ITEMS_PER_THREAD]; char local_data[ITEMS_PER_THREAD];
typedef cub::BlockExchange<char, THREADS, ITEMS_PER_THREAD> BlockExchange; typedef cub::BlockExchange<char, THREADS, ITEMS_PER_THREAD> BlockExchange;
__shared__ typename BlockExchange::TempStorage temp_storage;
// we load row after row from the base_position // we load row after row from the base_position
// Load data row by row // Load data row by row
...@@ -2446,7 +2445,7 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T ...@@ -2446,7 +2445,7 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T
#define MAX_SPARSE_COUNT 32 #define MAX_SPARSE_COUNT 32
#define SMEM_SIZE 8*256 #define SMEM_SIZE 8*256
template <typename T, int SPMM_ITEMS, int BITS> template <typename T, int SPMM_ITEMS, int BITS>
__global__ void kspmm_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, int rowsA, int rowsB, int colsB) __global__ void kspmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, T *B, half *out, float * __restrict__ const dequant_stats, int nnz, int rowsA, int rowsB, int colsB)
{ {
// 0. load balancing: We process rows with most columns first (count_vec)and we process one row per block // 0. load balancing: We process rows with most columns first (count_vec)and we process one row per block
...@@ -2500,7 +2499,7 @@ __global__ void kspmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *o ...@@ -2500,7 +2499,7 @@ __global__ void kspmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *o
{ {
for(int i = threadIdx.x; i < SMEM_SIZE; i+=blockDim.x) for(int i = threadIdx.x; i < SMEM_SIZE; i+=blockDim.x)
if((idx_col_B+i-local_idx_col_B_offset) < colsB) if((idx_col_B+i-local_idx_col_B_offset) < colsB)
smem_dequant_stats[i] = __ldg(&dequant_stats[idx_col_B+i-local_idx_col_B_offset]); smem_dequant_stats[i] = dequant_stats[idx_col_B+i-local_idx_col_B_offset];
__syncthreads(); __syncthreads();
} }
...@@ -2596,12 +2595,12 @@ __global__ void kspmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *o ...@@ -2596,12 +2595,12 @@ __global__ void kspmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *o
// TEMPLATE DEFINITIONS // TEMPLATE DEFINITIONS
//============================================================== //==============================================================
template __global__ void kspmm_coo_very_sparse_naive<half, 8, 16>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, half *B, half *out, float *dequant_stats, int nnz, int rowsA, int rowsB, int colsB); template __global__ void kspmm_coo_very_sparse_naive<half, 8, 16>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, half *B, half *out, float * __restrict__ const dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
template __global__ void kspmm_coo_very_sparse_naive<half, 16, 16>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, half *B, half *out, float *dequant_stats, int nnz, int rowsA, int rowsB, int colsB); template __global__ void kspmm_coo_very_sparse_naive<half, 16, 16>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, half *B, half *out, float * __restrict__ const dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
template __global__ void kspmm_coo_very_sparse_naive<half, 32, 16>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, half *B, half *out, float *dequant_stats, int nnz, int rowsA, int rowsB, int colsB); template __global__ void kspmm_coo_very_sparse_naive<half, 32, 16>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, half *B, half *out, float * __restrict__ const dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
template __global__ void kspmm_coo_very_sparse_naive<signed char, 8, 8>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, signed char *B, half *out, float *dequant_stats, int nnz, int rowsA, int rowsB, int colsB); template __global__ void kspmm_coo_very_sparse_naive<signed char, 8, 8>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, signed char *B, half *out, float * __restrict__ const dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
template __global__ void kspmm_coo_very_sparse_naive<signed char, 16, 8>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, signed char *B, half *out, float *dequant_stats, int nnz, int rowsA, int rowsB, int colsB); template __global__ void kspmm_coo_very_sparse_naive<signed char, 16, 8>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, signed char *B, half *out, float * __restrict__ const dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
template __global__ void kspmm_coo_very_sparse_naive<signed char, 32, 8>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, signed char *B, half *out, float *dequant_stats, int nnz, int rowsA, int rowsB, int colsB); template __global__ void kspmm_coo_very_sparse_naive<signed char, 32, 8>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, signed char *B, half *out, float * __restrict__ const dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
template __global__ void kTransformRowToFormat<256, 8, 32, 32*8, 0, COL32>(char *__restrict__ const A, char *out, int rows, int cols, int tiledCols, int outRows, int outCols); template __global__ void kTransformRowToFormat<256, 8, 32, 32*8, 0, COL32>(char *__restrict__ const A, char *out, int rows, int cols, int tiledCols, int outRows, int outCols);
template __global__ void kTransformRowToFormat<256, 8, 32, 32*8, 1, COL32>(char *__restrict__ const A, char *out, int rows, int cols, int tiledCols, int outRows, int outCols); template __global__ void kTransformRowToFormat<256, 8, 32, 32*8, 1, COL32>(char *__restrict__ const A, char *out, int rows, int cols, int tiledCols, int outRows, int outCols);
......
csrc/kernels.cuh
View file @ 9268dc9d
...@@ -107,7 +107,7 @@ template<typename T, int BLOCK_SIZE, int NUM_VALS> __global__ void kPercentileCl ...@@ -107,7 +107,7 @@ template<typename T, int BLOCK_SIZE, int NUM_VALS> __global__ void kPercentileCl
__global__ void kHistogramScatterAdd2D(float* histogram, int *index1, int *index2, float *src, const int maxidx1, const int n); __global__ void kHistogramScatterAdd2D(float* histogram, int *index1, int *index2, float *src, const int maxidx1, const int n);
template <typename T, int SPMM_ITEMS, int BITS> __global__ void kspmm_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, int rowsA, int rowsB, int colsB); template <typename T, int SPMM_ITEMS, int BITS> __global__ void kspmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, T *B, half *out, float * __restrict__ const dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
template <int ITEMS_PER_THREAD, int SUBTILE_ROWS, int THREADS>__global__ void kdequant_mm_int32_fp16( template <int ITEMS_PER_THREAD, int SUBTILE_ROWS, int THREADS>__global__ void kdequant_mm_int32_fp16(
int *__restrict__ const A, float *__restrict__ const rowStats, float *__restrict__ const colStats, int *__restrict__ const A, float *__restrict__ const rowStats, float *__restrict__ const colStats,
......
csrc/ops.cu
View file @ 9268dc9d
...@@ -247,6 +247,8 @@ int roundoff(int v, int d) { ...@@ -247,6 +247,8 @@ int roundoff(int v, int d) {
} }
#ifdef NO_CUBLASLT
#else
template<int ORDER> cublasLtOrder_t get_order() template<int ORDER> cublasLtOrder_t get_order()
{ {
switch(ORDER) switch(ORDER)
...@@ -266,7 +268,11 @@ template<int ORDER> cublasLtOrder_t get_order() ...@@ -266,7 +268,11 @@ template<int ORDER> cublasLtOrder_t get_order()
case COL_AMPERE: case COL_AMPERE:
return CUBLASLT_ORDER_COL32_2R_4R4; return CUBLASLT_ORDER_COL32_2R_4R4;
break; break;
default:
break;
} }
return CUBLASLT_ORDER_ROW;
} }
template cublasLtOrder_t get_order<ROW>(); template cublasLtOrder_t get_order<ROW>();
...@@ -274,6 +280,7 @@ template cublasLtOrder_t get_order<COL>(); ...@@ -274,6 +280,7 @@ template cublasLtOrder_t get_order<COL>();
template cublasLtOrder_t get_order<COL32>(); template cublasLtOrder_t get_order<COL32>();
template cublasLtOrder_t get_order<COL_TURING>(); template cublasLtOrder_t get_order<COL_TURING>();
template cublasLtOrder_t get_order<COL_AMPERE>(); template cublasLtOrder_t get_order<COL_AMPERE>();
#endif
template<int ORDER> int get_leading_dim(int dim1, int dim2) template<int ORDER> int get_leading_dim(int dim1, int dim2)
...@@ -297,6 +304,9 @@ template<int ORDER> int get_leading_dim(int dim1, int dim2) ...@@ -297,6 +304,9 @@ template<int ORDER> int get_leading_dim(int dim1, int dim2)
// 32*32 tiles // 32*32 tiles
return 32*roundoff(dim1, 32); return 32*roundoff(dim1, 32);
break; break;
default:
return 0;
break;
} }
} }
...@@ -306,7 +316,8 @@ template int get_leading_dim<COL32>(int dim1, int dim2); ...@@ -306,7 +316,8 @@ template int get_leading_dim<COL32>(int dim1, int dim2);
template <typename T, int SRC, int TARGET, bool transpose, int DTYPE> void transform(cublasLtHandle_t ltHandle, T *A, T *out, int dim1, int dim2) template <typename T, int SRC, int TARGET, bool transpose, int DTYPE> void transform(cublasLtHandle_t ltHandle, T *A, T *out, int dim1, int dim2)
{ {
#ifdef NO_CUBLASLT
#else
cublasLtOrder_t orderA = get_order<SRC>(); cublasLtOrder_t orderA = get_order<SRC>();
cublasLtOrder_t orderOut = get_order<TARGET>(); cublasLtOrder_t orderOut = get_order<TARGET>();
int ldA = get_leading_dim<SRC>(dim1, dim2); int ldA = get_leading_dim<SRC>(dim1, dim2);
...@@ -345,6 +356,7 @@ template <typename T, int SRC, int TARGET, bool transpose, int DTYPE> void trans ...@@ -345,6 +356,7 @@ template <typename T, int SRC, int TARGET, bool transpose, int DTYPE> void trans
if (A_desc) checkCublasStatus(cublasLtMatrixLayoutDestroy(A_desc)); if (A_desc) checkCublasStatus(cublasLtMatrixLayoutDestroy(A_desc));
if (out_desc) checkCublasStatus(cublasLtMatrixLayoutDestroy(out_desc)); if (out_desc) checkCublasStatus(cublasLtMatrixLayoutDestroy(out_desc));
if (A2Out_desc) checkCublasStatus(cublasLtMatrixTransformDescDestroy(A2Out_desc)); if (A2Out_desc) checkCublasStatus(cublasLtMatrixTransformDescDestroy(A2Out_desc));
#endif
} }
template void transform<int8_t, ROW, COL, false, 8>(cublasLtHandle_t ltHandle, int8_t *A, int8_t *out, int dim1, int dim2); template void transform<int8_t, ROW, COL, false, 8>(cublasLtHandle_t ltHandle, int8_t *A, int8_t *out, int dim1, int dim2);
...@@ -358,6 +370,9 @@ template void transform<int32_t, COL32, ROW, false, 32>(cublasLtHandle_t ltHandl ...@@ -358,6 +370,9 @@ template void transform<int32_t, COL32, ROW, false, 32>(cublasLtHandle_t ltHandl
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) 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)
{ {
#ifdef NO_CUBLASLT
return 0;
#else
int has_error = 0; int has_error = 0;
cublasLtMatmulDesc_t matmulDesc = NULL; cublasLtMatmulDesc_t matmulDesc = NULL;
cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL; cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL;
...@@ -412,6 +427,7 @@ template <int FORMATB, int DTYPE_OUT, int SCALE_ROWS> int igemmlt(cublasLtHandle ...@@ -412,6 +427,7 @@ template <int FORMATB, int DTYPE_OUT, int SCALE_ROWS> int igemmlt(cublasLtHandle
printf("error detected"); printf("error detected");
return has_error; return has_error;
#endif
} }
int fill_up_to_nearest_multiple(int value, int multiple) int fill_up_to_nearest_multiple(int value, int multiple)
...@@ -523,6 +539,9 @@ template <int FORMAT, int TRANSPOSE> void transformRowToFormat(char * A, char *o ...@@ -523,6 +539,9 @@ template <int FORMAT, int TRANSPOSE> void transformRowToFormat(char * A, char *o
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) 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)
{ {
#ifdef NO_CUBLASLT
#else
cusparseSpMatDescr_t descA; cusparseSpMatDescr_t descA;
cusparseDnMatDescr_t descB, descC; cusparseDnMatDescr_t descB, descC;
...@@ -569,6 +588,7 @@ void spmm_coo(cusparseHandle_t handle, int *A_rowidx, int *A_colidx, half *A_val ...@@ -569,6 +588,7 @@ void spmm_coo(cusparseHandle_t handle, int *A_rowidx, int *A_colidx, half *A_val
CHECK_CUSPARSE( cusparseDestroyDnMat(descB) ); CHECK_CUSPARSE( cusparseDestroyDnMat(descB) );
CHECK_CUSPARSE( cusparseDestroyDnMat(descC) ); CHECK_CUSPARSE( cusparseDestroyDnMat(descC) );
CUDA_CHECK_RETURN( cudaFree(dBuffer) ); CUDA_CHECK_RETURN( cudaFree(dBuffer) );
#endif
} }
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) 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)
......
cuda_install.sh 0 → 100644
View file @ 9268dc9d
URL92=https://developer.nvidia.com/compute/cuda/9.2/Prod2/local_installers/cuda_9.2.148_396.37_linux
URL100=https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda_10.0.130_410.48_linux
URL101=https://developer.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.105_418.39_linux.run
URL102=https://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run
URL110=https://developer.download.nvidia.com/compute/cuda/11.0.3/local_installers/cuda_11.0.3_450.51.06_linux.run
URL111=https://developer.download.nvidia.com/compute/cuda/11.1.1/local_installers/cuda_11.1.1_455.32.00_linux.run
URL112=https://developer.download.nvidia.com/compute/cuda/11.2.2/local_installers/cuda_11.2.2_460.32.03_linux.run
URL113=https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.19.01_linux.run
URL114=https://developer.download.nvidia.com/compute/cuda/11.4.4/local_installers/cuda_11.4.4_470.82.01_linux.run
URL115=https://developer.download.nvidia.com/compute/cuda/11.5.2/local_installers/cuda_11.5.2_495.29.05_linux.run
URL116=https://developer.download.nvidia.com/compute/cuda/11.6.2/local_installers/cuda_11.6.2_510.47.03_linux.run
URL117=https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda_11.7.0_515.43.04_linux.run
CUDA_VERSION=$1
BASE_PATH=$2
if [[ -n "$CUDA_VERSION" ]]; then
if [[ "$CUDA_VERSION" -eq "92" ]]; then
URL=$URL92
FOLDER=cuda-9.2
elif [[ "$CUDA_VERSION" -eq "100" ]]; then
URL=$URL100
FOLDER=cuda-10.0
elif [[ "$CUDA_VERSION" -eq "101" ]]; then
URL=$URL101
FOLDER=cuda-10.1
elif [[ "$CUDA_VERSION" -eq "102" ]]; then
URL=$URL102
FOLDER=cuda-10.2
elif [[ "$CUDA_VERSION" -eq "110" ]]; then
URL=$URL110
FOLDER=cuda-11.0
elif [[ "$CUDA_VERSION" -eq "111" ]]; then
URL=$URL111
FOLDER=cuda-11.1
elif [[ "$CUDA_VERSION" -eq "112" ]]; then
URL=$URL112
FOLDER=cuda-11.2
elif [[ "$CUDA_VERSION" -eq "113" ]]; then
URL=$URL113
FOLDER=cuda-11.3
elif [[ "$CUDA_VERSION" -eq "114" ]]; then
URL=$URL114
FOLDER=cuda-11.4
elif [[ "$CUDA_VERSION" -eq "115" ]]; then
URL=$URL115
FOLDER=cuda-11.5
elif [[ "$CUDA_VERSION" -eq "116" ]]; then
URL=$URL116
FOLDER=cuda-11.6
elif [[ "$CUDA_VERSION" -eq "117" ]]; then
URL=$URL117
FOLDER=cuda-11.7
else
echo "argument error: No cuda version passed as input. Choose among: {111, 115}"
fi
else
echo "argument error: No cuda version passed as input. Choose among: {111, 115}"
fi
FILE=$(basename $URL)
if [[ -n "$CUDA_VERSION" ]]; then
echo $URL
echo $FILE
wget $URL
bash $FILE --no-drm --no-man-page --override --installpath=~/local --librarypath=$BASE_PATH/lib --toolkitpath=$BASE_PATH/$FOLDER/ --toolkit --silent
echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$BASE_PATH/$FOLDER/lib64/" >> ~/.bashrc
echo "export PATH=$PATH:$BASE_PATH/$FOLDER/bin/" >> ~/.bashrc
source ~/.bashrc
else
echo ""
fi
cuda_install_111.sh deleted 100644 → 0
View file @ 1e88edd8
FILE115=:cuda_11.5.1_495.29.05_linux.run
FILE111=:cuda_11.1.1_455.32.00_linux.run
URL115=:https://developer.download.nvidia.com/compute/cuda/11.5.1/local_installers/cuda_11.5.1_495.29.05_linux.run
URL111=:https://developer.download.nvidia.com/compute/cuda/11.1.1/local_installers/cuda_11.1.1_455.32.00_linux.run
CUDA_VERSION=$1
if [[ -n "$CUDA_VERSION" ]]; then
if [[ "$CUDA_VERSION" -eq "111" ]]; then
FILE=cuda_11.1.1_455.32.00_linux.run
URL=https://developer.download.nvidia.com/compute/cuda/11.1.1/local_installers/cuda_11.1.1_455.32.00_linux.run
FOLDER=cuda-11.1
elif [[ "$CUDA_VERSION" -eq "115" ]]; then
FILE=cuda_11.5.1_495.29.05_linux.run
URL=https://developer.download.nvidia.com/compute/cuda/11.5.1/local_installers/cuda_11.5.1_495.29.05_linux.run
FOLDER=cuda-11.5
else
echo "argument error: No cuda version passed as input. Choose among: {111, 115}"
fi
else
echo "argument error: No cuda version passed as input. Choose among: {111, 115}"
fi
if [[ -n "$CUDA_VERSION" ]]; then
echo $URL
echo $FILE
wget $URL
bash $FILE --no-drm --no-man-page --override --installpath=~/local --librarypath=~/local/lib --toolkitpath=~/local/$FOLDER/ --toolkit --silent
echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/local/$FOLDER/lib64/" >> ~/.bashrc
echo "export PATH=$PATH:~/local/$FOLDER/bin/" >> ~/.bashrc
source ~/.bashrc
else
echo ""
fi
deploy_from_slurm.sh
View file @ 9268dc9d
#!/bin/bash #!/bin/bash
BASE_PATH=$1
module unload cuda module unload cuda
module unload gcc module unload gcc
rm -rf dist build rm -rf dist build
make clean make clean
make cleaneggs make cleaneggs
module load cuda/9.2 export CUDA_HOME=$BASE_PATH/cuda-11.0
module load gcc/7.3.0 make cuda110
CUDA_HOME=/public/apps/cuda/9.2
make
CUDA_VERSION=92 python -m build
python -m twine upload dist/* --verbose
module unload cuda
if [ ! -f "./bitsandbytes/libbitsandbytes.so" ]; then
# Control will enter here if $DIRECTORY doesn't exist.
echo "Compilation unsuccessul!" 1>&2
exit 64
fi
#CUDA_VERSION=110 python -m build
#python -m twine upload dist/* --verbose
rm -rf dist build rm -rf dist build
make clean make clean
make cleaneggs make cleaneggs
module load cuda/10.0 export CUDA_HOME=$BASE_PATH/cuda-11.1
CUDA_HOME=/public/apps/cuda/10.0 make cuda11x
make cuda10x
CUDA_VERSION=100 python -m build if [ ! -f "./bitsandbytes/libbitsandbytes.so" ]; then
python -m twine upload dist/* --verbose # Control will enter here if $DIRECTORY doesn't exist.
module unload cuda echo "Compilation unsuccessul!" 1>&2
module unload gcc exit 64
module load gcc/8.4 fi
#CUDA_VERSION=111 python -m build
#python -m twine upload dist/* --verbose
rm -rf dist build rm -rf dist build
make clean make clean
make cleaneggs make cleaneggs
module load cuda/10.1 export CUDA_HOME=$BASE_PATH/cuda-11.2
CUDA_HOME=/public/apps/cuda/10.1 make cuda11x
make cuda10x
CUDA_VERSION=101 python -m build if [ ! -f "./bitsandbytes/libbitsandbytes.so" ]; then
python -m twine upload dist/* --verbose # Control will enter here if $DIRECTORY doesn't exist.
module unload cuda echo "Compilation unsuccessul!" 1>&2
exit 64
fi
#CUDA_VERSION=112 python -m build
#python -m twine upload dist/* --verbose
rm -rf dist build rm -rf dist build
make clean make clean
make cleaneggs make cleaneggs
module load cuda/10.2 export CUDA_HOME=$BASE_PATH/cuda-11.3
CUDA_HOME=/public/apps/cuda/10.2/ make cuda11x
make cuda10x
CUDA_VERSION=102 python -m build
python -m twine upload dist/* --verbose
module unload cuda
if [ ! -f "./bitsandbytes/libbitsandbytes.so" ]; then
# Control will enter here if $DIRECTORY doesn't exist.
echo "Compilation unsuccessul!" 1>&2
exit 64
fi
#CUDA_VERSION=113 python -m build
#python -m twine upload dist/* --verbose
rm -rf dist build rm -rf dist build
make clean make clean
make cleaneggs make cleaneggs
module load cuda/11.0 export CUDA_HOME=$BASE_PATH/cuda-11.4
CUDA_HOME=/public/apps/cuda/11.0 make cuda11x
make cuda110
CUDA_VERSION=110 python -m build if [ ! -f "./bitsandbytes/libbitsandbytes.so" ]; then
python -m twine upload dist/* --verbose # Control will enter here if $DIRECTORY doesn't exist.
module unload cuda echo "Compilation unsuccessul!" 1>&2
exit 64
fi
#CUDA_VERSION=114 python -m build
##python -m twine upload dist/* --verbose
rm -rf dist build rm -rf dist build
make clean make clean
make cleaneggs make cleaneggs
module load cuda/11.1 export CUDA_HOME=$BASE_PATH/cuda-11.5
CUDA_HOME=/public/apps/cuda/11.1
make cuda11x make cuda11x
CUDA_VERSION=111 python -m build
python -m twine upload dist/* --verbose if [ ! -f "./bitsandbytes/libbitsandbytes.so" ]; then
module unload cuda # Control will enter here if $DIRECTORY doesn't exist.
echo "Compilation unsuccessul!" 1>&2
exit 64
fi
#CUDA_VERSION=115 python -m build
#python -m twine upload dist/* --verbose
rm -rf dist build rm -rf dist build
make clean make clean
make cleaneggs make cleaneggs
module load cuda/11.2 export CUDA_HOME=$BASE_PATH/cuda-11.6
CUDA_HOME=/public/apps/cuda/11.2
make cuda11x make cuda11x
CUDA_VERSION=112 python -m build if [ ! -f "./bitsandbytes/libbitsandbytes.so" ]; then
python -m twine upload dist/* --verbose # Control will enter here if $DIRECTORY doesn't exist.
module unload cuda echo "Compilation unsuccessul!" 1>&2
exit 64
fi
#CUDA_VERSION=116 python -m build
#python -m twine upload dist/* --verbose
rm -rf dist build rm -rf dist build
make clean make clean
make cleaneggs make cleaneggs
CUDA_HOME=/private/home/timdettmers/git/autoswap/local/cuda-11.3 make cuda11x export CUDA_HOME=$BASE_PATH/cuda-11.7
CUDA_VERSION=113 python -m build make cuda11x
python -m twine upload dist/* --verbose
module unload cuda if [ ! -f "./bitsandbytes/libbitsandbytes.so" ]; then
# Control will enter here if $DIRECTORY doesn't exist.
echo "Compilation unsuccessul!" 1>&2
exit 64
fi
#CUDA_VERSION=117 python -m build
#python -m twine upload dist/* --verbose
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