diff options
Diffstat (limited to 'bitsandbytes/functional.py')
| -rw-r--r-- | bitsandbytes/functional.py | 24 |
1 files changed, 24 insertions, 0 deletions
diff --git a/bitsandbytes/functional.py b/bitsandbytes/functional.py index 0190a7e..0a2d557 100644 --- a/bitsandbytes/functional.py +++ b/bitsandbytes/functional.py @@ -141,6 +141,14 @@ def get_special_format_str(): elif major == 8: return 'col_ampere' else: return 'col_turing' + +def is_on_gpu(tensors): + on_gpu = True + for t in tensors: + if t is None: continue # NULL pointers are fine + on_gpu &= t.device.type == 'cuda' + return on_gpu + def get_ptr(A: Tensor) -> ct.c_void_p: ''' Get the ctypes pointer from a PyTorch Tensor. @@ -284,6 +292,7 @@ def estimate_quantiles(A: Tensor, out: Tensor=None, offset: float=1/512) -> Tens The 256 quantiles in float32 datatype. ''' if out is None: out = torch.zeros((256,), dtype=torch.float32, device=A.device) + is_on_gpu([A, out]) if A.dtype == torch.float32: lib.cestimate_quantiles_fp32(get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel())) elif A.dtype == torch.float16: @@ -337,6 +346,7 @@ def quantize_blockwise(A: Tensor, code: Tensor=None, absmax: Tensor=None, rand=N if A.device.type != 'cpu': + is_on_gpu([code, A, absmax, out, rand]) if rand is not None: assert rand.numel() >= 1024 rand_offset = random.randint(0, 1023) @@ -401,6 +411,7 @@ def dequantize_blockwise(A: Tensor, quant_state: Tuple[Tensor, Tensor]=None, raise ValueError(f'The blockwise of {blocksize} is not supported. Supported values: [2048 4096]') if A.device.type != 'cpu': + is_on_gpu([A, out]) if out.dtype == torch.float32: lib.cdequantize_blockwise_fp32(get_ptr(quant_state[1]), get_ptr(A), get_ptr(quant_state[0]), get_ptr(out), ct.c_int(blocksize), ct.c_int(A.numel())) elif out.dtype == torch.float16: @@ -458,6 +469,7 @@ def quantize_no_absmax(A: Tensor, code: Tensor, out: Tensor=None) -> Tensor: Quantized 8-bit tensor. ''' if out is None: out = torch.zeros_like(A, dtype=torch.uint8) + is_on_gpu([A, out]) lib.cquantize(get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel())) return out @@ -483,6 +495,7 @@ def dequantize_no_absmax(A: Tensor, code: Tensor, out: Tensor=None) -> Tensor: 32-bit output tensor. ''' if out is None: out = torch.zeros_like(A, dtype=torch.float32) + is_on_gpu([code, A, out]) lib.cdequantize(get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel())) return out @@ -662,6 +675,7 @@ def percentile_clipping(grad: Tensor, gnorm_vec: Tensor, step: int, percentile: The current optimiation steps (number of past gradient norms). """ + is_on_gpu([grad, gnorm_vec]) if grad.dtype == torch.float32: lib.cpercentile_clipping_g32(get_ptr(grad), get_ptr(gnorm_vec), ct.c_int32(step), ct.c_int32(grad.numel())) elif grad.dtype == torch.float16: @@ -694,6 +708,7 @@ def histogram_scatter_add_2d(histogram: Tensor, index1: Tensor, index2: Tensor, maxdim1 = ct.c_int32(histogram.shape[0]) n = ct.c_int32(index1.numel()) + is_on_gpu([histogram, index1, index2d, source]) lib.chistogram_scatter_add_2d(get_ptr(histogram), get_ptr(index1), get_ptr(index2), get_ptr(source), maxdim1, n) def check_matmul(A, B, out, transposed_A, transposed_B, expected_type=torch.int8): @@ -820,6 +835,7 @@ def igemm(A: Tensor, B: Tensor, out: Tensor=None, transposed_A=False, transposed # B^T @ A^T = C^T # [km, nk -> mn] + is_on_gpu([B, A, out]) lib.cigemm(ptr, ct.c_bool(transposed_B), ct.c_bool(transposed_A), ct.c_int32(m), ct.c_int32(n), ct.c_int32(k), get_ptr(B), get_ptr(A), get_ptr(out), ct.c_int32(lda), ct.c_int32(ldb), ct.c_int32(ldc)) return out @@ -892,6 +908,7 @@ def batched_igemm(A: Tensor, B: Tensor, out: Tensor=None, transposed_A=False, tr ptr = CUBLAS_Context.get_instance().get_context(A.device) + is_on_gpu([B, A, out]) lib.cbatched_igemm(ptr, ct.c_bool(transposed_B), ct.c_bool(transposed_A), ct.c_int32(m), ct.c_int32(n), ct.c_int32(k), get_ptr(B), get_ptr(A), get_ptr(out), ct.c_int32(lda), ct.c_int32(ldb), ct.c_int32(ldc), ct.c_long(strideA), ct.c_long(strideB), ct.c_long(strideC), ct.c_uint32(num_batch)) @@ -954,6 +971,7 @@ def igemmlt(A, B, SA, SB, out=None, Sout=None, dtype=torch.int32): has_error = 0 ptrRowScale = get_ptr(None) + is_on_gpu([A, B, out]) if formatB == 'col_turing': if dtype == torch.int32: has_error = lib.cigemmlt_turing_32(ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc) @@ -994,6 +1012,7 @@ def mm_dequant(A, quant_state, row_stats, col_stats, out=None, new_row_stats=Non numRows = ct.c_int32(out_shape[0]) numCols = ct.c_int32(out_shape[1]) + is_on_gpu([A, row_stats, col_stats, out, new_row_stats, new_col_stats]) lib.cdequant_mm_int32_fp16(ptrA, ptrRowStats, ptrColStats, ptrOut, ptrNewRowStats, ptrNewColStats, numRows, numCols) return out @@ -1024,6 +1043,7 @@ def get_colrow_absmax(A, row_stats=None, col_stats=None, nnz_block_ptr=None, thr cols = ct.c_int32(cols) prev_device = pre_call(A.device) + is_on_gpu([A, row_stats, col_stats, nnz_block_ptr]) lib.cget_col_row_stats(ptrA, ptrRowStats, ptrColStats, ptrNnzrows, ct.c_float(threshold), rows, cols) post_call(prev_device) @@ -1133,6 +1153,7 @@ def double_quant(A, col_stats=None, row_stats=None, out_col=None, out_row=None, ptrOutCol = get_ptr(out_col) ptrOutRow = get_ptr(out_row) + is_on_gpu([A, col_stats, row_stats, out_col, out_row]) if threshold > 0.0: nnz = nnz_row_ptr[-1].item() if nnz > 0: @@ -1185,6 +1206,7 @@ def transform(A, to_order, from_order='row', out=None, transpose=False, state=No ptrA = get_ptr(A) ptrOut = get_ptr(out) + is_on_gpu([A, out]) if to_order == 'col32': if transpose: lib.ctransform_row2col32T(get_ptr(A), get_ptr(out), dim1, dim2) @@ -1240,6 +1262,7 @@ def spmm_coo(cooA, B, out=None): cldb = ct.c_int32(ldb) cldc = ct.c_int32(ldc) + is_on_gpu([cooA.rowidx, cooA.colidx, cooA.values, B, out]) lib.cspmm_coo(ptr, ptrRowidx, ptrColidx, ptrValues, cnnz, crowsA, ccolsA, ccolsB, cldb, ptrB, cldc, ptrC, ct.c_bool(transposed_B)) return out @@ -1285,6 +1308,7 @@ def spmm_coo_very_sparse(cooA, B, dequant_stats=None, out=None): #print(cooA.rowidx[:64]) #print(cooA.colidx[:64].sort()[0]) + is_on_gpu([cooA.rowidx, cooA.colidx, cooA.values, B, out, dequant_stats]) if B.dtype == torch.float16: lib.cspmm_coo_very_sparse_naive_fp16(ptrMaxCount, ptrMaxIdx, ptrOffset, ptrRowidx, ptrColidx, ptrValues, ptrB, ptrC, ptrDequantStats, cnnz_rows, cnnz, crowsA, crowsB, ccolsB) elif B.dtype == torch.int8: |