Mark some functions for deprecation.

bitsandbytes/autograd/_functions.py

@@ -5,6 +5,7 @@
 from warnings import warn
 
 import torch
+from typing_extensions import deprecated
 
 import bitsandbytes.functional as F
 
@@ -97,6 +98,10 @@ def undo_layout(permuted_tensor: torch.Tensor, tile_indices: torch.LongTensor) -
     return outputs.reshape(rows, cols).contiguous()
 
 
+@deprecated(
+    "MatMul8bit is deprecated and will be removed in a future release. Please use MatMul8bitLt instead.",
+    category=FutureWarning,
+)
 class MatMul8bit(torch.autograd.Function):
     @staticmethod
     def forward(ctx, A, B, out=None, quant_type="vector", precision=None):
@@ -208,6 +213,7 @@ def backward(ctx, grad_output):
 matmul_cublas = MatMul8bit.apply
 
 
+@deprecated("This function is deprecated and will be removed in a future release.", category=FutureWarning)
 def supports_igemmlt(device: torch.device) -> bool:
     """check if this device supports the optimized int8 kernel"""
     if torch.cuda.get_device_capability(device=device) < (7, 5):
@@ -219,6 +225,7 @@ def supports_igemmlt(device: torch.device) -> bool:
     return True
 
 
+@deprecated("This function is deprecated and will be removed in a future release.", category=FutureWarning)
 def _get_tile_size(format):
     assert format in (
         "col_turing",
@@ -227,6 +234,7 @@ def _get_tile_size(format):
     return (8, 32) if format == "col_turing" else (32, 32)
 
 
+@deprecated("This function is deprecated and will be removed in a future release.", category=FutureWarning)
 def get_tile_inds(format, device):
     transform = lambda x: F.transform(x.to(device), from_order="row", to_order=format)[0].to(x.device)
     with torch.no_grad():
@@ -331,14 +339,6 @@ def forward(
                 # 2. Quantize B
                 state.CB, state.SCB, _ = F.int8_vectorwise_quant(B.to(torch.float16))
 
-                # (
-                #     state.CB,
-                #     state.CBt,
-                #     state.SCB,
-                #     state.SCBt,
-                #     _,
-                # ) = F.double_quant(B.to(torch.float16))
-
         if state.threshold > 0.0 and coo_tensorA is not None:
             state.idx = torch.unique(coo_tensorA._indices()[1]).long()
 

bitsandbytes/cextension.py

@@ -113,6 +113,6 @@ def get_native_library() -> BNBNativeLibrary:
 
 Inspect the output of the command and see if you can locate CUDA libraries. You might need to add them
 to your LD_LIBRARY_PATH. If you suspect a bug, please take the information from python -m bitsandbytes
-and open an issue at: https://github.com/TimDettmers/bitsandbytes/issues
+and open an issue at: https://github.com/bitsandbytes-foundation/bitsandbytes/issues
 """,
         )

bitsandbytes/functional.py

@@ -10,6 +10,7 @@
 import numpy as np
 import torch
 from torch import Tensor
+from typing_extensions import deprecated
 
 from bitsandbytes.utils import pack_dict_to_tensor, unpack_tensor_to_dict
 
@@ -244,10 +245,12 @@ def fill(A, value, device=None, prefetch=True):
     elementwise_func("fill", A, None, value)
 
 
+@deprecated("Function will be removed in a future release.", category=FutureWarning)
 def arange(A, device=None):
     elementwise_func("arange", A, None, 0)
 
 
+@deprecated("Function will be removed in a future release.", category=FutureWarning)
 def _mul(A, B, device=None):
     elementwise_func("_mul", A, B, 0)
 
@@ -414,7 +417,7 @@ def create_quantile_map(A, total_bits=8):
     return q
 
 
-# TODO: Deprecate
+@deprecated("This function is deprecated and will be removed in a future version.", category=FutureWarning)
 def get_special_format_str():
     return "row"
 
@@ -475,6 +478,10 @@ def post_call(prev_device):
     torch.cuda.set_device(prev_device)
 
 
+@deprecated(
+    "The layout transformation operations will be removed in a future release. Please use row-major layout only.",
+    category=FutureWarning,
+)
 def get_transform_func(dtype, orderA, orderOut, transpose=False):
     name = f'ctransform_{(8 if dtype == torch.int8 else 32)}_{orderA}_to_{orderOut}_{"t" if transpose else "n"}'
     if not hasattr(lib, name):
@@ -486,6 +493,10 @@ def get_transform_func(dtype, orderA, orderOut, transpose=False):
         return getattr(lib, name)
 
 
+@deprecated(
+    "The layout transformation operations will be removed in a future release. Please use row-major layout only.",
+    category=FutureWarning,
+)
 def get_transform_buffer(shape, dtype, device, to_order, from_order="row", transpose=False):
     # init_func = torch.empty
     init_func = torch.zeros
@@ -525,6 +536,10 @@ def get_transform_buffer(shape, dtype, device, to_order, from_order="row", trans
         raise NotImplementedError(f"To_order not supported: {to_order}")
 
 
+@deprecated(
+    "The layout transformation operations will be removed in a future release. Please use row-major layout only.",
+    category=FutureWarning,
+)
 def nvidia_transform(
     A,
     to_order,
@@ -1424,6 +1439,7 @@ def dequantize_4bit(
         return out
 
 
+@deprecated("This function is deprecated and will be removed in a future release.", category=FutureWarning)
 def quantize(
     A: Tensor,
     code: Optional[torch.Tensor] = None,
@@ -1443,6 +1459,7 @@ def quantize(
     return out, (absmax, code)
 
 
+@deprecated("This function is deprecated and will be removed in a future release.", category=FutureWarning)
 def dequantize(
     A: Tensor,
     state: Optional[Tuple[Tensor, Tensor]] = None,
@@ -1463,6 +1480,7 @@ def dequantize(
     return out * state[0]
 
 
+@deprecated("This function is deprecated and will be removed in a future release.", category=FutureWarning)
 def quantize_no_absmax(A: Tensor, code: Tensor, out: Optional[torch.Tensor] = None) -> Tensor:
     """
     Quantizes input tensor to 8-bit.
@@ -1493,6 +1511,7 @@ def quantize_no_absmax(A: Tensor, code: Tensor, out: Optional[torch.Tensor] = No
     return out
 
 
+@deprecated("This function is deprecated and will be removed in a future release.", category=FutureWarning)
 def dequantize_no_absmax(A: Tensor, code: Tensor, out: Optional[torch.Tensor] = None) -> Tensor:
     """
     Dequantizes the 8-bit tensor to 32-bit.
@@ -1627,6 +1646,11 @@ def optimizer_update_32bit(
     post_call(prev_device)
 
 
+@deprecated(
+    "This function is deprecated and will be removed in a future release. "
+    "Please use optimizer_update_8bit_blockwise instead. ",
+    category=FutureWarning,
+)
 def optimizer_update_8bit(
     optimizer_name: str,
     g: Tensor,
@@ -1827,6 +1851,7 @@ def optimizer_update_8bit_blockwise(
     post_call(prev_device)
 
 
+@deprecated("This function is deprecated and will be removed in a future release.", category=FutureWarning)
 def percentile_clipping(grad: Tensor, gnorm_vec: Tensor, step: int, percentile: int = 5):
     """Applies percentile clipping
 
@@ -2516,11 +2541,6 @@ def coo_zeros(rows, cols, nnz, device, dtype=torch.half):
     return COOSparseTensor(rows, cols, nnz, rowidx, colidx, values)
 
 
-def extract_outliers_new(A: torch.Tensor, threshold: float):
-    outlier_mask = A.abs() >= threshold
-    return A.masked_fill(outlier_mask == False, 0.0).to_sparse_coo()
-
-
 def double_quant(A: torch.Tensor, col_stats=None, row_stats=None, out_col=None, out_row=None, threshold=0.0):
     # TODO: Optimize/write CUDA kernel for this?
     # Note: for inference, use the new int8_vectorwise_quant.
@@ -2576,6 +2596,10 @@ def int8_vectorwise_quant(A: torch.Tensor, threshold=0.0):
     return out_row, row_stats, coo_tensor
 
 
+@deprecated(
+    "The layout transformation operations will be removed in a future release. Please use row-major layout only.",
+    category=FutureWarning,
+)
 def transform(A, to_order, from_order="row", out=None, transpose=False, state=None, ld=None):
     prev_device = pre_call(A.device)
     if state is None:
@@ -2772,6 +2796,11 @@ def spmm_coo_very_sparse(cooA, B, dequant_stats=None, out=None):
 C = 127.0
 
 
+@deprecated(
+    "This function is deprecated and will be removed in a future release. "
+    "Consider using `int8_vectorwise_quant` instead.",
+    category=FutureWarning,
+)
 def vectorwise_quant(x, dim=1, quant_type="vector"):
     if quant_type == "linear":
         max1 = torch.abs(x).max().float()
@@ -2816,6 +2845,7 @@ def vectorwise_quant(x, dim=1, quant_type="vector"):
         return None
 
 
+@deprecated("This function is deprecated and will be removed in a future release.", category=FutureWarning)
 def vectorwise_dequant(xq, max1, quant_type="vector"):
     if quant_type == "vector":
         x = (xq / C * max1).to(torch.float32)
@@ -2824,6 +2854,10 @@ def vectorwise_dequant(xq, max1, quant_type="vector"):
         return None
 
 
+@deprecated(
+    "This function is deprecated and will be removed in a future release. Consider using `mm_dequant` instead.",
+    category=FutureWarning,
+)
 def vectorwise_mm_dequant(xq, S1, S2, dtype=torch.half, quant_type="vector"):
     if quant_type == "linear":
         norm = S1 * S2 / (C * C)
@@ -2883,6 +2917,7 @@ def vectorwise_mm_dequant(xq, S1, S2, dtype=torch.half, quant_type="vector"):
         return None
 
 
+@deprecated("This function is deprecated and will be removed in a future release.", category=FutureWarning)
 def dequant_min_max(xq, A, B, SA, SB, dtype=torch.half):
     offset = B.float().t().sum(0) * (SA[0] + SA[1])
     x = xq.float()
@@ -2898,7 +2933,6 @@ def dequant_min_max(xq, A, B, SA, SB, dtype=torch.half):
 
 
 def extract_outliers(A, SA, idx):
-    # TODO: Implement for row-major
     shapeA = SA[0]
     formatA = SA[1]
     assert formatA in ["col_turing", "col_ampere"]
@@ -2923,6 +2957,7 @@ def extract_outliers(A, SA, idx):
     return out
 
 
+@deprecated("This function is deprecated and will be removed in a future release.", category=FutureWarning)
 def pipeline_test(A, batch_size):
     out = torch.zeros_like(A)
     lib.cpipeline_test(get_ptr(A), get_ptr(out), ct.c_size_t(A.numel()), ct.c_size_t(batch_size))

setup.py

@@ -31,10 +31,10 @@ def has_ext_modules(self):
     description="k-bit optimizers and matrix multiplication routines.",
     license="MIT",
     keywords="gpu optimizers optimization 8-bit quantization compression",
-    url="https://github.com/TimDettmers/bitsandbytes",
+    url="https://github.com/bitsandbytes-foundation/bitsandbytes",
     packages=find_packages(),
     package_data={"": libs},
-    install_requires=["torch", "numpy"],
+    install_requires=["torch", "numpy", "typing_extensions>=4.8.0"],
     extras_require={
         "benchmark": ["pandas", "matplotlib"],
         "test": ["scipy", "lion_pytorch"],