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xet

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danieldk HF Staff commited on 13 days ago

Commit

a8d29bb

1 Parent(s): 3631a93

Remove builds incompatible with kernels >= 0.14

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Files changed (5) hide show

build/torch29-metal-aarch64-darwin/__init__.py +0 -165
build/torch29-metal-aarch64-darwin/_bitsandbytes_mps_metal_42e0dd1.abi3.so +0 -3
build/torch29-metal-aarch64-darwin/_ops.py +0 -9
build/torch29-metal-aarch64-darwin/bitsandbytes_mps/__init__.py +0 -26
build/torch29-metal-aarch64-darwin/metadata.json +0 -4

build/torch29-metal-aarch64-darwin/__init__.py DELETED Viewed

@@ -1,165 +0,0 @@
-from typing import Optional, Tuple
-import torch
-from ._ops import ops
-# Quant type constants (match bitsandbytes DataType_t)
-FP4 = 1
-NF4 = 2
-def quantize_4bit(
-    input: torch.Tensor,
-    blocksize: int = 64,
-    quant_type: int = NF4,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """Blockwise 4-bit quantization using NF4 or FP4 codebook.
-    Args:
-        input: Input tensor on MPS device (float16, bfloat16, or float32).
-        blocksize: Number of elements per quantization block (64 or 128).
-        quant_type: FP4 (1) or NF4 (2).
-    Returns:
-        Tuple of (packed, absmax):
-            packed: uint8 tensor of packed 4-bit values [numel/2].
-            absmax: float32 tensor of per-block max absolute values.
-    """
-    return ops.bnb_quantize_4bit(input, blocksize, quant_type)
-def dequantize_4bit(
-    packed: torch.Tensor,
-    absmax: torch.Tensor,
-    blocksize: int = 64,
-    quant_type: int = NF4,
-    numel: int = -1,
-    output_dtype: torch.dtype = torch.float16,
-) -> torch.Tensor:
-    """Blockwise 4-bit dequantization using NF4 or FP4 codebook.
-    Args:
-        packed: uint8 tensor of packed 4-bit values.
-        absmax: float32 tensor of per-block max absolute values.
-        blocksize: Number of elements per quantization block (64 or 128).
-        quant_type: FP4 (1) or NF4 (2).
-        numel: Number of elements in the original tensor.
-               If -1, inferred as packed.numel() * 2.
-        output_dtype: Output scalar type.
-    Returns:
-        Dequantized tensor.
-    """
-    if numel < 0:
-        numel = packed.numel() * 2
-    return ops.bnb_dequantize_4bit(
-        packed, absmax, blocksize, quant_type, numel, output_dtype
-    )
-def gemv_4bit(
-    x: torch.Tensor,
-    w: torch.Tensor,
-    absmax: torch.Tensor,
-    output_features: int,
-    blocksize: int = 64,
-    quant_type: int = NF4,
-) -> torch.Tensor:
-    """Fused matrix-vector multiply with 4-bit quantized weights.
-    Computes y = dequant(W) @ x, where W is blockwise NF4/FP4 quantized.
-    Args:
-        x: Input vector [..., K] on MPS device.
-        w: Packed weight matrix [N, K/2] (uint8) on MPS device.
-        absmax: Per-block scales [N, ceil(K/blocksize)] (float32).
-        output_features: Number of output features (N).
-        blocksize: Quantization block size (64 or 128).
-        quant_type: FP4 (1) or NF4 (2).
-    Returns:
-        Output tensor [..., N].
-    """
-    return ops.bnb_gemv_4bit(x, w, absmax, blocksize, quant_type, output_features)
-def gemm_4bit(
-    x: torch.Tensor,
-    w: torch.Tensor,
-    absmax: torch.Tensor,
-    output_features: int,
-    blocksize: int = 64,
-    quant_type: int = NF4,
-) -> torch.Tensor:
-    """Fused matrix-matrix multiply with 4-bit quantized transposed weights.
-    Computes Y = X @ dequant(W).T, where W is blockwise NF4/FP4 quantized.
-    Args:
-        x: Input matrix [..., M, K] on MPS device.
-        w: Packed weight matrix [N, K/2] (uint8) on MPS device.
-        absmax: Per-block scales [N, ceil(K/blocksize)] (float32).
-        output_features: Number of output features (N).
-        blocksize: Quantization block size (64 or 128).
-        quant_type: FP4 (1) or NF4 (2).
-    Returns:
-        Output tensor [..., M, N].
-    """
-    return ops.bnb_gemm_4bit(x, w, absmax, blocksize, quant_type, output_features)
-def linear_4bit(
-    x: torch.Tensor,
-    w: torch.Tensor,
-    absmax: torch.Tensor,
-    output_features: int,
-    blocksize: int = 64,
-    quant_type: int = NF4,
-    bias: Optional[torch.Tensor] = None,
-) -> torch.Tensor:
-    """4-bit quantized linear layer (auto-selects GEMV or GEMM).
-    Args:
-        x: Input tensor on MPS device.
-        w: Packed weight [N, K/2] (uint8).
-        absmax: Scales [N, ceil(K/blocksize)] (float32).
-        output_features: N.
-        blocksize: 64 or 128.
-        quant_type: FP4 (1) or NF4 (2).
-        bias: Optional bias [N].
-    Returns:
-        Output tensor.
-    """
-    input_1d = x.dim() == 1
-    if input_1d or (x.dim() >= 2 and x.size(-2) == 1):
-        x_flat = x.view(x.size(-1)) if input_1d else x.squeeze(-2)
-        y = gemv_4bit(
-            x_flat,
-            w,
-            absmax,
-            output_features,
-            blocksize,
-            quant_type,
-        )
-        if input_1d:
-            y = y.squeeze(0)
-        elif x.dim() >= 2:
-            y = y.unsqueeze(-2)
-    else:
-        y = gemm_4bit(x, w, absmax, output_features, blocksize, quant_type)
-    if bias is not None:
-        y = y + bias
-    return y
-__all__ = [
-    "quantize_4bit",
-    "dequantize_4bit",
-    "gemv_4bit",
-    "gemm_4bit",
-    "linear_4bit",
-]

build/torch29-metal-aarch64-darwin/_bitsandbytes_mps_metal_42e0dd1.abi3.so DELETED Viewed

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e1376c17866d5c32339a331a725fbff002041d94d825ccf97795420788cc74f4
-size 844504

build/torch29-metal-aarch64-darwin/_ops.py DELETED Viewed

@@ -1,9 +0,0 @@
-import torch
-from . import _bitsandbytes_mps_metal_42e0dd1
-ops = torch.ops._bitsandbytes_mps_metal_42e0dd1
-def add_op_namespace_prefix(op_name: str):
-    """
-    Prefix op by namespace.
-    """
-    return f"_bitsandbytes_mps_metal_42e0dd1::{op_name}"

build/torch29-metal-aarch64-darwin/bitsandbytes_mps/__init__.py DELETED Viewed

@@ -1,26 +0,0 @@
-import ctypes
-import sys
-import importlib
-from pathlib import Path
-from types import ModuleType
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch29-metal-aarch64-darwin/metadata.json DELETED Viewed

@@ -1,4 +0,0 @@
-{
-  "version": 1,
-  "python-depends": []
-}