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94 lines
3.2 KiB
94 lines
3.2 KiB
from typing import Any, Callable, Optional
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from ..compat import torch
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from ..model import Model
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from ..shims import PyTorchGradScaler, PyTorchShim, TorchScriptShim
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from .pytorchwrapper import (
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convert_pytorch_default_inputs,
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convert_pytorch_default_outputs,
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forward,
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)
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def TorchScriptWrapper_v1(
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torchscript_model: Optional["torch.jit.ScriptModule"] = None,
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convert_inputs: Optional[Callable] = None,
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convert_outputs: Optional[Callable] = 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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) -> Model[Any, Any]:
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"""Wrap a TorchScript model, so that it has the same API as Thinc models.
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torchscript_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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convert_inputs:
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Function that converts inputs and gradients that should be passed
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to the model to Torch tensors.
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convert_outputs:
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Function that converts model outputs and gradients from Torch tensors
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Thinc arrays.
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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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if convert_inputs is None:
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convert_inputs = convert_pytorch_default_inputs
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if convert_outputs is None:
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convert_outputs = convert_pytorch_default_outputs
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return Model(
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"pytorch_script",
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forward,
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attrs={"convert_inputs": convert_inputs, "convert_outputs": convert_outputs},
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shims=[
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TorchScriptShim(
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model=torchscript_model,
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mixed_precision=mixed_precision,
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grad_scaler=grad_scaler,
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device=device,
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)
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],
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dims={"nI": None, "nO": None},
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)
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def pytorch_to_torchscript_wrapper(model: Model):
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"""Convert a PyTorch wrapper to a TorchScript wrapper. The embedded PyTorch
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`Module` is converted to `ScriptModule`.
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"""
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shim = model.shims[0]
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if not isinstance(shim, PyTorchShim):
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raise ValueError("Expected PyTorchShim when converting a PyTorch wrapper")
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convert_inputs = model.attrs["convert_inputs"]
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convert_outputs = model.attrs["convert_outputs"]
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pytorch_model = shim._model
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if not isinstance(pytorch_model, torch.nn.Module):
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raise ValueError("PyTorchShim does not wrap a PyTorch module")
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torchscript_model = torch.jit.script(pytorch_model)
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grad_scaler = shim._grad_scaler
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mixed_precision = shim._mixed_precision
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device = shim.device
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return TorchScriptWrapper_v1(
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torchscript_model,
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convert_inputs=convert_inputs,
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convert_outputs=convert_outputs,
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mixed_precision=mixed_precision,
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grad_scaler=grad_scaler,
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device=device,
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)
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