diff options
| -rw-r--r-- | bitsandbytes/autograd/_functions.py | 52 | ||||
| -rw-r--r-- | bitsandbytes/functional.py | 26 | ||||
| -rw-r--r-- | csrc/kernels.cu | 78 | ||||
| -rw-r--r-- | csrc/kernels.cuh | 2 | ||||
| -rw-r--r-- | csrc/ops.cu | 26 | ||||
| -rw-r--r-- | csrc/ops.cuh | 2 | ||||
| -rw-r--r-- | csrc/pythonInterface.c | 6 | ||||
| -rw-r--r-- | tests/test_functional.py | 26 |
8 files changed, 194 insertions, 24 deletions
diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py index 815a4f1..5503749 100644 --- a/bitsandbytes/autograd/_functions.py +++ b/bitsandbytes/autograd/_functions.py @@ -191,24 +191,24 @@ class MatMul8bitLt(torch.autograd.Function): # B in in 8-bit row-major, we can transform it back to 16-bit to extract outlier dimensions # we also need to convert it to the turing/ampere format state.CxB, state.SB = F.transform(state.CB, to_order=formatB) - if state.threshold > 0.0 and coo_tensorA is not None and state.idx is None and state.CB is not None: - # generate outlier index and subB - outlier_idx = torch.unique(coo_tensorA.colidx).long() - state.outlier_pool.add_outliers(outlier_idx, A.shape[-1]) - if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]: - # do not use pool for 2nd FFN layer - state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device) - else: - state.idx = outlier_idx - state.subB = (state.CB[:, state.idx].float().t().contiguous()*(state.SCB/127)).half() - - if state.idx is not None: - # extract outliers - CA[:, state.idx] = 0 - CAt[:, state.idx] = 0 - subA = A[:, state.idx] - else: - subA = None + #if state.threshold > 0.0 and coo_tensorA is not None and state.idx is None and state.CB is not None: + # # generate outlier index and subB + # outlier_idx = torch.unique(coo_tensorA.colidx).long() + # state.outlier_pool.add_outliers(outlier_idx, A.shape[-1]) + # if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]: + # # do not use pool for 2nd FFN layer + # state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device) + # else: + # state.idx = outlier_idx + # state.subB = (state.CB[:, state.idx].float().t().contiguous()*(state.SCB/127)).half() + + #if state.idx is not None: + # # extract outliers + # CA[:, state.idx] = 0 + # CAt[:, state.idx] = 0 + # subA = A[:, state.idx] + #else: + # subA = None else: if not state.has_fp16_weights and state.CxB is None: state.CxB, state.SB = F.transform(state.CB, to_order=formatB) @@ -229,6 +229,22 @@ class MatMul8bitLt(torch.autograd.Function): else: has_grad = False + if coo_tensorA is not None and not state.has_fp16_weights: + # extract outliers + + outlier_idx = torch.unique(coo_tensorA.colidx) + state.outlier_pool.add_outliers(outlier_idx, A.shape[-1]) + if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]: + # do not use pool for 2nd FFN layer + state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device) + else: + state.idx = outlier_idx + outliers = F.extract_outliers(state.CxB, state.SB, outlier_idx).half() + state.subB = (outliers*state.SCB.view(-1, 1).half()/127.0).t().contiguous() + CA[:, state.idx.long()] = 0 + CAt[:, state.idx.long()] = 0 + subA = A[:, state.idx.long()] + shapeB = state.SB[0] if len(input_shape) == 3: diff --git a/bitsandbytes/functional.py b/bitsandbytes/functional.py index 0190a7e..ac85f88 100644 --- a/bitsandbytes/functional.py +++ b/bitsandbytes/functional.py @@ -1404,3 +1404,29 @@ def dequant_min_max(xq, A, B, SA, SB, dtype=torch.half): x *= SA[1]/127 x +=offset return x.to(dtype) + +def extract_outliers(A, SA, idx): + shapeA = SA[0] + formatA = SA[1] + assert formatA in ['col_turing', 'col_ampere'] + assert A.device.type == 'cuda' + + out = torch.zeros((shapeA[0], idx.numel()), dtype=torch.int8, device=A.device) + + idx_size = ct.c_int32(idx.numel()) + rows = ct.c_int32(shapeA[0]) + cols = ct.c_int32(shapeA[1]) + ptrA = get_ptr(A) + ptrIdx = get_ptr(idx) + ptrOut = get_ptr(out) + + if formatA == 'col_turing': + lib.cextractOutliers_turing(ptrA, ptrIdx, ptrOut, idx_size, rows, cols) + elif formatA == 'col_ampere': + lib.cextractOutliers_ampere(ptrA, ptrIdx, ptrOut, idx_size, rows, cols) + + return out + + + + diff --git a/csrc/kernels.cu b/csrc/kernels.cu index 6eca3aa..d4eb56c 100644 --- a/csrc/kernels.cu +++ b/csrc/kernels.cu @@ -2591,16 +2591,82 @@ __global__ void kspmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *o } } +template <int FORMAT> __global__ void kExtractOutliers(char *A, int *idx, char *out, int idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA) +{ + int local_colidx = idx[blockIdx.x]; + + if(FORMAT==COL_TURING) + { + // TURING FORMAT: + // 8*32 tiles with 4*4 subtiles + // the 8*32 subtile has first all 4*4 subtiles of even rows (max 4*4*8 = 128 elements) + // the subsequent 4*4 subtiles are for all odd rows if some rows columns are empty the values are zero + // the tile repeats again after the 8*32 tile in a major column order, meaning: (next 8 rows are A[8:16, 0:32]) + // the next tile is the next 8 rows for the same 32 columns. Once all rows are finished, the column + // index increases by 32 + // columns are grouped in increments of 4, meaning that one has the following rows and columns + // rows: [0 0 0 0, 2 2 2 2, 4 4 4 4, 6 6 6 6, 0 0 0 0 ...] + // cols: [0 1 2 3, 0 1 2 4, 0 1 2 3, 0 1 2 3, 4 5 6 7 ...] + + // each thread reads 1 element = 1 row + for(int row = threadIdx.x; row < rowsA; row+= blockDim.x) + { + int offset_per_col_tile = ((rowsA+7)/8)*32*8; + int tile_offset_rows = (row/8)*32*8; + int tile_offset_cols = (local_colidx/32)*offset_per_col_tile; + int offset = 0; + int subtile_col_idx = local_colidx%32; + int subtile_row_idx = row % 8; + if(row % 2 == 1) + offset += 128 + (subtile_col_idx/4)*16 + (subtile_col_idx%4) + ((subtile_row_idx-1)*2); + else + // even + offset += 0 + (subtile_col_idx/4)*16 + (subtile_col_idx%4) + (subtile_row_idx*2); + + offset += tile_offset_rows + tile_offset_cols; + + char val = A[offset]; + + int out_idx = (row*idx_size) + blockIdx.x; + out[out_idx] = val; + } + } + else if(FORMAT == COL_AMPERE) + { + + for(int row = threadIdx.x; row < rowsA; row+= blockDim.x) + { + // we got 32x32 tiles and we use the magic equation from the cublasLt doc to get the element + // within each tile. + int offset_per_col_tile = ((rowsA+31)/32)*32*32; + int tile_offset_rows = (row/32)*32*32; + int tile_offset_cols = (local_colidx/32)*offset_per_col_tile; + int subtile_col_idx = local_colidx%32; + int subtile_row_idx = row % 32; + // this magic is taken from the cublasLt doc (search for COL32) + int offset = (((subtile_row_idx%8)/2*4+subtile_row_idx/8)*2+subtile_row_idx%2)*32+subtile_col_idx; + offset += tile_offset_cols + tile_offset_rows; + + char val = A[offset]; + int out_idx = (row*idx_size) + blockIdx.x; + out[out_idx] = val; + } + } +} + //============================================================== // 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 * __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 * __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 * __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 * __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 * __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 * __restrict__ const dequant_stats, int nnz, int rowsA, int rowsB, int colsB); +template __global__ void kExtractOutliers<COL_TURING>(char *A, int *idx, char *out, int idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA); +template __global__ void kExtractOutliers<COL_AMPERE>(char *A, int *idx, char *out, int idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA); + +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, 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, 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<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, 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, 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 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); diff --git a/csrc/kernels.cuh b/csrc/kernels.cuh index 4e65e96..2447494 100644 --- a/csrc/kernels.cuh +++ b/csrc/kernels.cuh @@ -118,6 +118,8 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int S template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int TRANSPOSE, int FORMAT> __global__ void kTransformRowToFormat(char *__restrict__ const A, char *out, int rows, int cols, int tiledCols, int outRows, int outCols); +template <int FORMAT> __global__ void kExtractOutliers(char *A, int *idx, char *out, int idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA); + #endif diff --git a/csrc/ops.cu b/csrc/ops.cu index c430d55..952894c 100644 --- a/csrc/ops.cu +++ b/csrc/ops.cu @@ -598,10 +598,36 @@ template <typename T, int BITS> void spmm_coo_very_sparse_naive(int *max_count, CUDA_CHECK_RETURN(cudaPeekAtLastError()); } + +template <int FORMAT> void extractOutliers(char * A, int *idx, char *out, int idx_size, int rows, int cols) +{ + int threads = 256; + // we load 128 column values per warp + int tiledCols = tiledCols = fill_up_to_nearest_multiple(cols, 32); + int tiledRows = 0; + + int num_blocks = idx_size; + + if(FORMAT == COL_TURING) + { + tiledRows = fill_up_to_nearest_multiple(rows, 8); + } + else if(FORMAT == COL_AMPERE) + { + tiledRows = fill_up_to_nearest_multiple(rows, 32); + } + + kExtractOutliers<FORMAT><<<num_blocks, threads>>>(A, idx, out, idx_size, rows, cols, tiledRows, tiledCols); + CUDA_CHECK_RETURN(cudaPeekAtLastError()); +} + //============================================================== // TEMPLATE DEFINITIONS //============================================================== +template void extractOutliers<COL_TURING>(char * A, int *idx, char *out, int idx_size, int rows, int cols); +template void extractOutliers<COL_AMPERE>(char * A, int *idx, char *out, int idx_size, int rows, int cols); + template void spmm_coo_very_sparse_naive<half, 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_rows, int nnz, int rowsA, int rowsB, int colsB); template void spmm_coo_very_sparse_naive<signed char, 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_rows, int nnz, int rowsA, int rowsB, int colsB); diff --git a/csrc/ops.cuh b/csrc/ops.cuh index 4e719df..4b09ecf 100644 --- a/csrc/ops.cuh +++ b/csrc/ops.cuh @@ -174,4 +174,6 @@ void spmm_coo(cusparseHandle_t handle, int *A_rowidx, int *A_colidx, half *A_val 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 <int FORMAT> void extractOutliers(char * A, int *idx, char *out, int idx_size, int rows, int cols); + #endif diff --git a/csrc/pythonInterface.c b/csrc/pythonInterface.c index a6a4b13..7356c11 100644 --- a/csrc/pythonInterface.c +++ b/csrc/pythonInterface.c @@ -105,6 +105,9 @@ void transform_row2turingT(char * A, char *out, int rows, int cols){ transformRo void transform_row2ampere(char * A, char *out, int rows, int cols){ transformRowToFormat<COL_AMPERE, 0>(A, out, rows, cols); } void transform_row2ampereT(char * A, char *out, int rows, int cols){ transformRowToFormat<COL_AMPERE, 1>(A, out, rows, cols); } +void extractOutliers_turing(char * A, int *idx, char *out, int idx_size, int rows, int cols){ extractOutliers<COL_TURING>(A, idx, out, idx_size, rows, cols); } +void extractOutliers_ampere(char * A, int *idx, char *out, int idx_size, int rows, int cols){ extractOutliers<COL_AMPERE>(A, idx, out, idx_size, rows, cols); } + int igemmlt_turing_32(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) { return igemmlt<COL_TURING, 32, 0>(ltHandle, m, n, k, A, B, C, row_scale, lda, ldb, ldc); } @@ -280,6 +283,9 @@ extern "C" void cspmm_coo_very_sparse_naive_int8(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_rows, int nnz, int rowsA, int rowsB, int colsB) { spmm_coo_very_sparse_naive_int8(max_count, max_idx, offset_rowidx, rowidx, colidx, values, B, out, dequant_stats, nnz_rows, nnz, rowsA, rowsB, colsB); } + void cextractOutliers_turing(char * A, int *idx, char *out, int idx_size, int rows, int cols){ extractOutliers_turing(A, idx, out, idx_size, rows, cols); } + void cextractOutliers_ampere(char * A, int *idx, char *out, int idx_size, int rows, int cols){ extractOutliers_ampere(A, idx, out, idx_size, rows, cols); } + #endif void cquantize_blockwise_cpu_fp32(float *code, float *A, float *absmax, unsigned char *out, const int n){ quantize_cpu(code, A, absmax, out, n); } void cdequantize_blockwise_cpu_fp32(float *code, unsigned char *A, float *absmax, float *out, const int n){ dequantize_cpu(code, A, absmax, out, n); } diff --git a/tests/test_functional.py b/tests/test_functional.py index d80a4f9..bfc3e28 100644 --- a/tests/test_functional.py +++ b/tests/test_functional.py @@ -1859,3 +1859,29 @@ def test_zp(): print(err1, err2, err3, err4, err5, err6) + +def test_extract_outliers(): + for i in range(k): + shapeA = (4096, 4096*4) + idx = torch.unique(torch.randint(0, shapeA[1], size=(10,)).int()).cuda() + #idx = torch.Tensor([0]).int().cuda() + A = torch.randint(-128, 127, size=shapeA, device='cuda').to(torch.int8) + outliers1 = A[:, idx.long()] + + CA, SA = F.transform(A, 'col_turing') + + outliers2 = F.extract_outliers(CA, SA, idx) + + assert outliers2.shape[0] == shapeA[0] + assert outliers2.shape[1] == idx.numel() + + torch.testing.assert_allclose(outliers1, outliers2) + + CA, SA = F.transform(A, 'col_ampere') + + outliers2 = F.extract_outliers(CA, SA, idx) + + assert outliers2.shape[0] == shapeA[0] + assert outliers2.shape[1] == idx.numel() + + torch.testing.assert_allclose(outliers1, outliers2) |