You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
68 lines
2.4 KiB
68 lines
2.4 KiB
from io import BytesIO
|
|
from typing import Any, Optional
|
|
|
|
import srsly
|
|
|
|
from ..compat import torch
|
|
from ..util import get_torch_default_device
|
|
from .pytorch import PyTorchShim
|
|
from .pytorch_grad_scaler import PyTorchGradScaler
|
|
|
|
|
|
class TorchScriptShim(PyTorchShim):
|
|
"""A Thinc shim that wraps a TorchScript module.
|
|
|
|
model:
|
|
The TorchScript module. A value of `None` is also possible to
|
|
construct a shim to deserialize into.
|
|
mixed_precision:
|
|
Enable mixed-precision. This changes whitelisted ops to run
|
|
in half precision for better performance and lower memory use.
|
|
grad_scaler:
|
|
The gradient scaler to use for mixed-precision training. If this
|
|
argument is set to "None" and mixed precision is enabled, a gradient
|
|
scaler with the default configuration is used.
|
|
device:
|
|
The PyTorch device to run the model on. When this argument is
|
|
set to "None", the default device for the currently active Thinc
|
|
ops is used.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
model: Optional["torch.jit.ScriptModule"],
|
|
config=None,
|
|
optimizer: Any = None,
|
|
mixed_precision: bool = False,
|
|
grad_scaler: Optional[PyTorchGradScaler] = None,
|
|
device: Optional["torch.device"] = None,
|
|
):
|
|
if model is not None and not isinstance(model, torch.jit.ScriptModule):
|
|
raise ValueError(
|
|
"PyTorchScriptShim must be initialized with ScriptModule or None (for deserialization)"
|
|
)
|
|
|
|
super().__init__(model, config, optimizer, mixed_precision, grad_scaler, device)
|
|
|
|
def to_bytes(self):
|
|
filelike = BytesIO()
|
|
torch.jit.save(self._model, filelike)
|
|
filelike.seek(0)
|
|
model_bytes = filelike.getvalue()
|
|
msg = {"config": self.cfg, "model": model_bytes}
|
|
return srsly.msgpack_dumps(msg)
|
|
|
|
def from_bytes(self, bytes_data):
|
|
device = get_torch_default_device()
|
|
msg = srsly.msgpack_loads(bytes_data)
|
|
self.cfg = msg["config"]
|
|
filelike = BytesIO(msg["model"])
|
|
filelike.seek(0)
|
|
# As of Torch 2.0.0, loading TorchScript models directly to
|
|
# an MPS device is not supported.
|
|
map_location = torch.device("cpu") if device.type == "mps" else device
|
|
self._model = torch.jit.load(filelike, map_location=map_location)
|
|
self._model.to(device)
|
|
self._grad_scaler.to_(device)
|
|
return self
|