File size: 5,555 Bytes
f12ad1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | """Runtime-only loader for Capicu's quantized Cellpose-SAM exports."""
from __future__ import annotations
import hashlib
import json
from pathlib import Path
import torch
import torch.nn.functional as F
from huggingface_hub import snapshot_download
from safetensors.torch import load_file
from torch import nn
REPO_ID = "capicu-ai/cellpose-sam-wquant-w8a16"
def _digest(path: Path) -> str:
value = hashlib.sha256()
with path.open("rb") as handle:
for block in iter(lambda: handle.read(8 * 1024 * 1024), b""):
value.update(block)
return value.hexdigest()
def _restore(packed: torch.Tensor, scales: torch.Tensor, state: dict, device):
bits = int(state["bits"])
per_byte = 8 // bits
packed = packed.to(device=device, dtype=torch.uint8).flatten()
mask = (1 << bits) - 1
codes = torch.stack(
[(packed >> (index * bits)) & mask for index in range(per_byte)],
dim=1,
).flatten()[: int(state["code_count"])]
signed = codes.to(torch.int16) - int(state["qmax"])
grouped = signed.reshape(
int(state["rows"]),
int(state["n_groups"]),
int(state["group_size"]),
)
restored = grouped.float() * scales.to(device).unsqueeze(-1)
restored = restored.reshape(int(state["rows"]), int(state["padded_inner"]))
return restored[..., : int(state["inner"])].reshape(state["original_shape"])
def _activation(tensor: torch.Tensor, bits: int | None) -> torch.Tensor:
if bits is None:
return tensor
qmax = (1 << (bits - 1)) - 1
scale = tensor.detach().abs().amax().clamp_min(1e-12) / qmax
return torch.round(tensor / scale).clamp(-qmax, qmax) * scale
class _Packed(nn.Module):
def _setup(self, packed, scales, bias, state, activation_bits):
self.register_buffer("packed_weight", packed.cpu())
self.register_buffer("weight_scales", scales.cpu())
self.register_buffer("bias", None if bias is None else bias.cpu())
self.state = state
self.activation_bits = activation_bits
def _weight(self, tensor):
return _restore(
self.packed_weight,
self.weight_scales,
self.state,
tensor.device,
).to(dtype=tensor.dtype)
class _Linear(_Packed):
def __init__(self, source, **payload):
super().__init__()
self.in_features = source.in_features
self.out_features = source.out_features
self._setup(**payload)
def forward(self, tensor):
tensor = _activation(tensor, self.activation_bits)
bias = None if self.bias is None else self.bias.to(tensor.device, tensor.dtype)
return F.linear(tensor, self._weight(tensor), bias)
class _Conv2d(_Packed):
def __init__(self, source, **payload):
super().__init__()
self.stride = source.stride
self.padding = source.padding
self.dilation = source.dilation
self.groups = source.groups
self._setup(**payload)
def forward(self, tensor):
tensor = _activation(tensor, self.activation_bits)
bias = None if self.bias is None else self.bias.to(tensor.device, tensor.dtype)
return F.conv2d(
tensor,
self._weight(tensor),
bias,
self.stride,
self.padding,
self.dilation,
self.groups,
)
def _replace(root: nn.Module, name: str, module: nn.Module) -> None:
parent_name, _, child_name = name.rpartition(".")
parent = root.get_submodule(parent_name) if parent_name else root
if child_name.isdigit() and isinstance(parent, (nn.Sequential, nn.ModuleList)):
parent[int(child_name)] = module
else:
setattr(parent, child_name, module)
def load_model(
device: str | torch.device = "cpu",
local_repo: str | Path | None = None,
):
"""Download and load the ready-to-run quantized Cellpose-SAM model."""
root = (
Path(local_repo)
if local_repo is not None
else Path(
snapshot_download(
REPO_ID,
allow_patterns=[
"config.json",
"model.safetensors",
],
)
)
)
manifest = json.loads((root / "config.json").read_text())
weights_path = root / "model.safetensors"
if _digest(weights_path) != manifest["weights"]["sha256"]:
raise ValueError("model checksum mismatch")
from cellpose import models as cellpose_models
target = torch.device(device)
model = cellpose_models.CellposeModel(
gpu=target.type == "cuda",
pretrained_model=manifest["base_model"],
device=target,
use_bfloat16=False,
)
state = load_file(str(weights_path), device="cpu")
network = model.net.cpu().eval()
for item in manifest["replacements"]:
name = item["name"]
source = network.get_submodule(name)
prefix = f"{name}."
payload = {
"packed": state[f"{prefix}packed_weight"],
"scales": state[f"{prefix}weight_scales"],
"bias": state.get(f"{prefix}bias"),
"state": item["quant_state"],
"activation_bits": item.get("activation_bits"),
}
replacement = (
_Linear(source, **payload)
if item["kind"] == "linear"
else _Conv2d(source, **payload)
)
_replace(network, name, replacement)
network.load_state_dict(state, strict=True)
model.net = network.to(target).eval()
return model
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