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