PyTorch Custom Operator Integration

bitsandbytes/__init__.py

@@ -3,18 +3,35 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-from . import research, utils
+
+import torch
+
+from . import _ops, research, utils
 from .autograd._functions import (
     MatmulLtState,
-    bmm_cublas,
     matmul,
     matmul_4bit,
-    matmul_cublas,
-    mm_cublas,
 )
+from .backends.cpu import ops as cpu_ops
+from .backends.default import ops as default_ops
 from .nn import modules
 from .optim import adam
 
+# This is a signal for integrations with transformers/diffusers.
+# Eventually, we will remove this and check based on release version.
+features = {"multi-backend"}
+supported_torch_devices = {
+    "cuda",
+    "cpu",
+    # "mps",
+    # "xpu",
+    # "hpu",
+    # "npu",
+}
+
+if torch.cuda.is_available():
+    from .backends.cuda import ops as cuda_ops
+
 __pdoc__ = {
     "libbitsandbytes": False,
     "optim.optimizer.Optimizer8bit": False,

bitsandbytes/_ops.py

@@ -0,0 +1,302 @@
+from math import prod
+from typing import Optional, Sequence, Tuple
+
+import torch
+
+_IS_TORCH_GTE_24 = False
+
+if hasattr(torch.library, "register_fake"):
+    _IS_TORCH_GTE_24 = True
+    register_fake = torch.library.register_fake
+    register_kernel = torch.library.register_kernel
+else:
+    # PyTorch <= 2.3
+    register_fake = torch.library.impl_abstract
+    register_kernel = torch.library.impl
+
+
+# Higher level op: int8 matmul + dequant + bias
+torch.library.define(
+    "bitsandbytes::int8_scaled_mm",
+    "(Tensor A, Tensor B, Tensor row_stats, Tensor col_stats, Tensor? bias=None, ScalarType dtype=float16) -> Tensor",
+)
+
+
+@register_fake("bitsandbytes::int8_scaled_mm")
+def _(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    row_stats: torch.Tensor,
+    col_stats: torch.Tensor,
+    bias: Optional[torch.Tensor] = None,
+    dtype=torch.float16,
+) -> torch.Tensor:
+    shapeC = (*A.shape[:-1], B.shape[0])
+    return torch.empty(shapeC, device=A.device, dtype=dtype)
+
+
+torch.library.define(
+    "bitsandbytes::int8_linear_matmul",
+    "(Tensor A, Tensor B) -> Tensor",
+)
+
+
+@register_fake("bitsandbytes::int8_linear_matmul")
+def _(A: torch.Tensor, B: torch.Tensor):
+    torch._check(A.dtype == torch.int8, lambda: "A must be int8")
+    torch._check(B.dtype == torch.int8, lambda: "B must be int8")
+    shapeC = (*A.shape[:-1], B.shape[0])
+    return torch.empty(shapeC, device=A.device, dtype=torch.int32)
+
+
+# More info on `out` overloads:
+# https://github.com/pytorch/pytorch/issues/125044
+torch.library.define(
+    "bitsandbytes::int8_linear_matmul.out",
+    "(Tensor A, Tensor B, Tensor! out) -> ()",
+)
+
+
+@register_fake("bitsandbytes::int8_linear_matmul.out")
+def _(A: torch.Tensor, B: torch.Tensor, out: torch.Tensor):
+    shapeC = (*A.shape[:-1], B.shape[0])
+
+    torch._check(A.dtype == torch.int8, lambda: "A must be int8")
+    torch._check(B.dtype == torch.int8, lambda: "B must be int8")
+    torch._check(out.shape == shapeC, lambda: f"Expected out.shape == {shapeC}, got {out.shape}")
+    torch._check(out.device == A.device, lambda: f"Expected out.device == {A.device}, got {out.device}")
+    torch._check(out.dtype == torch.int32, lambda: f"Expected out.dtype == int32, got {out.dtype}")
+
+
+torch.library.define(
+    "bitsandbytes::int8_vectorwise_quant",
+    "(Tensor A, float threshold=0.0) -> (Tensor, Tensor, Tensor?)",
+)
+
+
+@register_fake("bitsandbytes::int8_vectorwise_quant")
+def _(A: torch.Tensor, threshold=0.0):
+    out_row = torch.empty(A.shape, device=A.device, dtype=torch.int8)
+    row_stats = torch.empty(prod(A.shape[:-1]), device=A.device, dtype=torch.float32)
+
+    if threshold == 0.0:
+        return out_row, row_stats, None
+
+    outlier_cols = torch.library.get_ctx().new_dynamic_size()
+
+    return out_row, row_stats, A.new_empty(outlier_cols, dtype=torch.int64)
+
+
+torch.library.define("bitsandbytes::int8_vectorwise_dequant", "(Tensor A, Tensor stats) -> Tensor")
+
+
+@register_fake("bitsandbytes::int8_vectorwise_dequant")
+def _(A: torch.Tensor, stats: torch.Tensor) -> torch.Tensor:
+    torch._check(A.dtype == torch.int8, lambda: "A must be int8")
+    return torch.empty_like(A, dtype=torch.float32)
+
+
+# Default PyTorch-native implementation
+@register_kernel("bitsandbytes::int8_vectorwise_dequant", None)
+def _(A: torch.Tensor, stats: torch.Tensor):
+    # To dequantize we divide by 127, or multiply by the reciprocal.
+    return A * stats.view(-1, 1) * 7.874015718698502e-3
+
+
+torch.library.define(
+    "bitsandbytes::int8_mm_dequant",
+    "(Tensor A, Tensor row_stats, Tensor col_stats, ScalarType dtype=float16, Tensor? bias=None) -> Tensor",
+)
+
+
+@register_fake("bitsandbytes::int8_mm_dequant")
+def _(
+    A: torch.Tensor,
+    row_stats: torch.Tensor,
+    col_stats: torch.Tensor,
+    dtype=torch.float16,
+    bias: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+    torch._check(A.dtype == torch.int32, lambda: "A must be int32")
+    return torch.empty_like(A, dtype=dtype)
+
+
+torch.library.define(
+    "bitsandbytes::int8_double_quant",
+    "(Tensor A, float threshold=0.0) -> (Tensor, Tensor, Tensor, Tensor, Tensor?)",
+)
+
+
+@register_fake("bitsandbytes::int8_double_quant")
+def _(
+    A: torch.Tensor,
+    threshold=0.0,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+    out_row = torch.empty_like(A, dtype=torch.int8)
+    out_col = torch.empty_like(A, dtype=torch.int8)
+    row_stats = torch.empty(prod(A.shape[:-1]), device=A.device, dtype=torch.float32)
+    col_stats = torch.empty(A.shape[-1], device=A.device, dtype=torch.float32)
+    outlier_n = torch.library.get_ctx().new_dynamic_size()
+    outlier_cols = A.new_empty(outlier_n, dtype=torch.int64)
+    return out_row, out_col, row_stats, col_stats, outlier_cols
+
+
+torch.library.define(
+    "bitsandbytes::dequantize_4bit",
+    "(Tensor A, Tensor absmax, int blocksize, str quant_type, int[] shape, ScalarType dtype) -> Tensor",
+)
+
+
+@register_fake("bitsandbytes::dequantize_4bit")
+def _(
+    A: torch.Tensor,
+    absmax: torch.Tensor,
+    blocksize: int,
+    quant_type: str,
+    shape: Sequence[int],
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    torch._check_is_size(blocksize)
+    return torch.empty(shape, dtype=dtype, device=A.device)
+
+
+torch.library.define(
+    "bitsandbytes::dequantize_4bit.out",
+    "(Tensor A, Tensor absmax, int blocksize, str quant_type, int[] shape, ScalarType dtype, Tensor! out) -> ()",
+)
+
+
+@register_fake("bitsandbytes::dequantize_4bit.out")
+def _(
+    A: torch.Tensor,
+    absmax: torch.Tensor,
+    blocksize: int,
+    quant_type: str,
+    shape: Sequence[int],
+    dtype: torch.dtype,
+    out: torch.Tensor,
+) -> None:
+    torch._check_is_size(blocksize)
+    torch._check(out.shape == shape, lambda: f"Expected out.shape == {shape}, got {out.shape}")
+    torch._check(out.device == A.device, lambda: f"Expected out.device == {A.device}, got {out.device}")
+    torch._check(out.dtype == dtype, lambda: f"Expected out.dtype == {dtype}, got {out.dtype}")
+
+
+torch.library.define(
+    "bitsandbytes::quantize_4bit",
+    "(Tensor A, int blocksize, str quant_type, ScalarType quant_storage) -> (Tensor, Tensor)",
+)
+
+
+@register_fake("bitsandbytes::quantize_4bit")
+def _(
+    A: torch.Tensor, blocksize: int, quant_type: str, quant_storage: torch.dtype
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    torch._check_is_size(blocksize)
+
+    n = A.numel()
+    blocks = -(n // -blocksize)
+    absmax = torch.empty((blocks,), device=A.device, dtype=torch.float32)
+    out = torch.empty(((n + 1) // (quant_storage.itemsize * 2), 1), device=A.device, dtype=quant_storage)
+    return out, absmax
+
+
+torch.library.define(
+    "bitsandbytes::dequantize_blockwise",
+    "(Tensor A, Tensor absmax, Tensor code, int blocksize, ScalarType dtype) -> Tensor",
+)
+
+
+@register_fake("bitsandbytes::dequantize_blockwise")
+def _(A: torch.Tensor, absmax: torch.Tensor, code: torch.Tensor, blocksize: int, dtype: torch.dtype) -> torch.Tensor:
+    torch._check_is_size(blocksize)
+    torch._check(A.dtype == torch.uint8, lambda: f"A must be uint8, got {A.dtype}")
+    return torch.empty_like(A, dtype=dtype)
+
+
+torch.library.define(
+    "bitsandbytes::dequantize_blockwise.out",
+    "(Tensor A, Tensor absmax, Tensor code, int blocksize, ScalarType dtype, Tensor! out) -> ()",
+)
+
+
+@register_fake("bitsandbytes::dequantize_blockwise.out")
+def _(
+    A: torch.Tensor, absmax: torch.Tensor, code: torch.Tensor, blocksize: int, dtype: torch.dtype, out: torch.Tensor
+):
+    torch._check_is_size(blocksize)
+    torch._check(A.dtype == torch.uint8, lambda: f"A must be uint8, got {A.dtype}")
+    torch._check(out.shape == A.shape, lambda: f"Expected out.shape == {A.shape}, got {out.shape}")
+    torch._check(out.device == A.device, lambda: f"Expected out.device == {A.device}, got {out.device}")
+    torch._check(out.dtype == dtype, lambda: f"Expected out.dtype == {dtype}, got {out.dtype}")
+
+
+torch.library.define("bitsandbytes::quantize_blockwise", "(Tensor A, Tensor code, int blocksize) -> (Tensor, Tensor)")
+
+
+@register_fake("bitsandbytes::quantize_blockwise")
+def _(A: torch.Tensor, code: torch.Tensor, blocksize: int) -> Tuple[torch.Tensor, torch.Tensor]:
+    torch._check_is_size(blocksize)
+    n = A.numel()
+    blocks = -(n // -blocksize)
+    absmax = torch.empty((blocks,), device=A.device, dtype=torch.float32)
+    out = torch.empty_like(A, dtype=torch.uint8)
+    return out, absmax
+
+
+torch.library.define(
+    "bitsandbytes::gemv_4bit",
+    "(Tensor A, Tensor B, int[] shapeB, Tensor absmax, Tensor code, int blocksize) -> Tensor",
+)
+
+
+@register_fake("bitsandbytes::gemv_4bit")
+def _(
+    A: torch.Tensor, B: torch.Tensor, shapeB: Sequence[int], absmax: torch.Tensor, code: torch.Tensor, blocksize: int
+) -> torch.Tensor:
+    torch._check_is_size(blocksize)
+    torch._check(A.numel() == A.size(-1), lambda: f"A must be a vector with leading dimensions of 1, got {A.shape}")
+    torch._check(
+        A.dtype in [torch.float16, torch.bfloat16, torch.float32],
+        lambda: f"A must be float16, bfloat16, or float32, got {A.dtype}",
+    )
+    torch._check(
+        B.dtype in [torch.uint8, torch.bfloat16, torch.float16, torch.float32],
+        lambda: f"B must be backed by storage of type uint8, bfloat16, float16, or float32, got {B.dtype}",
+    )
+    shape = (*A.shape[:-1], shapeB[0])
+    return torch.empty(shape, device=A.device, dtype=A.dtype)
+
+
+torch.library.define(
+    "bitsandbytes::gemv_4bit.out",
+    "(Tensor A, Tensor B, int[] shapeB, Tensor absmax, Tensor code, int blocksize, Tensor! out) -> ()",
+)
+
+
+@register_fake("bitsandbytes::gemv_4bit.out")
+def _(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    shapeB: Sequence[int],
+    absmax: torch.Tensor,
+    code: torch.Tensor,
+    blocksize: int,
+    out: torch.Tensor,
+) -> None:
+    torch._check_is_size(blocksize)
+    torch._check(A.numel() == A.size(-1), lambda: f"A must be a vector with leading dimensions of 1, got {A.shape}")
+    torch._check(
+        A.dtype in [torch.float16, torch.bfloat16, torch.float32],
+        lambda: f"A must be float16, bfloat16, or float32, got {A.dtype}",
+    )
+    torch._check(
+        B.dtype in [torch.uint8, torch.bfloat16, torch.float16, torch.float32],
+        lambda: f"B must be backed by storage of type uint8, bfloat16, float16, or float32, got {B.dtype}",
+    )
+    torch._check(
+        out.shape == (*A.shape[:-1], shapeB[0]),
+        lambda: f"Expected out.shape == {(*A.shape[:-1], shapeB[0])}, got {out.shape}",
+    )
+    torch._check(out.device == A.device, lambda: f"Expected out.device == {A.device}, got {out.device}")
+    torch._check(out.dtype == A.dtype, lambda: f"Expected out.dtype == {A.dtype}, got {out.dtype}")