Merge pull request bitsandbytes-foundation#898 from jianan-gu/upstrea…

…m_device_abstraction

Initial working draft to allow enable a device abstraction to enable different hardware backend.

bitsandbytes/__init__.py

@@ -12,9 +12,15 @@
     matmul_cublas,
     mm_cublas,
 )
+from .cextension import lib
 from .nn import modules
-from .optim import adam
 
+if lib and lib.compiled_with_cuda:
+    from .backends import register_backend
+    from .backends.cuda import CUDABackend
+    from .optim import adam
+
+    register_backend("cuda", CUDABackend())
 __pdoc__ = {
     "libbitsandbytes": False,
     "optim.optimizer.Optimizer8bit": False,

bitsandbytes/backends/__init__.py

@@ -0,0 +1,15 @@
+from typing import Dict
+
+from bitsandbytes.backends.base import Backend
+
+backends: Dict[str, Backend] = {}
+
+
+def register_backend(backend_name: str, backend_instance: Backend):
+    backends[backend_name.lower()] = backend_instance
+
+
+def ensure_backend_is_available(device_type: str):
+    """Check if a backend is available for the given device type."""
+    if device_type.lower() not in backends:
+        raise NotImplementedError(f"Device backend for {device_type} is currently not supported.")

bitsandbytes/backends/base.py

@@ -0,0 +1,133 @@
+from abc import ABC, abstractmethod
+from typing import Optional, Tuple
+
+import torch
+
+from bitsandbytes.utils import QuantState
+
+
+class Backend(ABC):
+    """Base class for devices backends that will implement their own 8bits and 4bits functions."""
+
+    @abstractmethod
+    def double_quant(
+        self,
+        A,
+        col_stats=None,
+        row_stats=None,
+        out_col=None,
+        out_row=None,
+        threshold=0.0,
+    ):
+        raise NotImplementedError
+
+    @abstractmethod
+    def transform(
+        self,
+        A,
+        to_order,
+        from_order="row",
+        out=None,
+        transpose=False,
+        state=None,
+        ld=None,
+    ):
+        raise NotImplementedError
+
+    @abstractmethod
+    def igemmlt(self, A, B, SA, SB, out=None, Sout=None, dtype=torch.int32):
+        raise NotImplementedError
+
+    @abstractmethod
+    def mm_dequant(
+        self,
+        A,
+        quant_state,
+        row_stats,
+        col_stats,
+        out=None,
+        new_row_stats=None,
+        new_col_stats=None,
+        bias=None,
+    ):
+        raise NotImplementedError
+
+    @abstractmethod
+    def extract_outliers(self, A, SA, idx):
+        raise NotImplementedError
+
+    @abstractmethod
+    def quantize_4bit(
+        self,
+        A: torch.Tensor,
+        absmax: Optional[torch.Tensor] = None,
+        out: Optional[torch.Tensor] = None,
+        blocksize=64,
+        compress_statistics=False,
+        quant_type="fp4",
+        quant_storage=torch.uint8,
+    ) -> Tuple[torch.Tensor, QuantState]:
+        """
+        Quantize tensor A in blocks of 4-bit values.
+
+        Quantizes tensor A by dividing it into blocks which are independently quantized to FP4.
+
+        Parameters
+        ----------
+        A : torch.Tensor
+            The input tensor.
+        absmax : torch.Tensor
+            The absmax values.
+        out : torch.Tensor
+            The output tensor.
+        blocksize : int
+            The blocksize used in quantization.
+        quant_type : str
+            The 4-bit quantization data type {fp4, nf4}
+
+        Returns
+        -------
+        torch.Tensor:
+            Tensor with packed 4-bit values.
+        tuple(torch.Tensor, torch.Size, torch.dtype, int):
+            The quantization state to undo the quantization.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def dequantize_4bit(
+        self,
+        A: torch.Tensor,
+        quant_state: Optional[QuantState] = None,
+        absmax: Optional[torch.Tensor] = None,
+        out: Optional[torch.Tensor] = None,
+        blocksize: int = 64,
+        quant_type="fp4",
+    ) -> torch.Tensor:
+        """
+        Dequantizes FP4 blockwise quantized values.
+
+        Dequantizes the tensor A with maximum absolute values absmax in blocks of size blocksize.
+
+        Parameters
+        ----------
+        A : torch.Tensor
+            The input tensor (packed 4-bit values).
+        quant_state : QuantState
+            object with quantisation stats, incl. absmax values, original tensor shape and original dtype.
+        absmax : torch.Tensor
+            The absmax values.
+        out : torch.Tensor
+            Dequantized output tensor.
+        blocksize : int
+            The blocksize used in quantization.
+        quant_type : str
+            The 4-bit quantization data type {fp4, nf4}
+
+
+        Returns
+        -------
+        torch.Tensor:
+            Dequantized tensor.
+        """
+        raise NotImplementedError