pytorch TorchScript需要访问源代码,以便执行集合的编译,deque

ny6fqffe  于 2023-04-06  发布在  其他
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我尝试将PyTorchFOMM模型转换为TorchScript,刚开始用@torch.jit.script注解一些类就出现错误:
OSError: Can't get source for <class 'collections.deque'>. TorchScript requires source access in order to carry out compilation, make sure original .py files are available.
据我所知,在CPython中实现的类因此不能被TorchScript编译器读取。我没有找到任何纯Python实现。我如何克服这个问题?
下面是我尝试注解的类:

import queue
import collections
import threading
import torch

@torch.jit.script
class SyncMaster(object):
    """An abstract `SyncMaster` object.

    - During the replication, as the data parallel will trigger an callback of each module, all slave devices should
    call `register(id)` and obtain an `SlavePipe` to communicate with the master.
    - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected,
    and passed to a registered callback.
    - After receiving the messages, the master device should gather the information and determine to message passed
    back to each slave devices.
    """

    def __init__(self, master_callback):
        """

        Args:
            master_callback: a callback to be invoked after having collected messages from slave devices.
        """
        self._master_callback = master_callback
        self._queue = queue.Queue()
        self._registry = collections.OrderedDict()
        self._activated = False

    def __getstate__(self):
        return {'master_callback': self._master_callback}

    def __setstate__(self, state):
        self.__init__(state['master_callback'])

    def register_slave(self, identifier):
        """
        Register an slave device.

        Args:
            identifier: an identifier, usually is the device id.

        Returns: a `SlavePipe` object which can be used to communicate with the master device.

        """
        if self._activated:
            assert self._queue.empty(), 'Queue is not clean before next initialization.'
            self._activated = False
            self._registry.clear()
        future = FutureResult()
        self._registry[identifier] = _MasterRegistry(future)
        return SlavePipe(identifier, self._queue, future)

    def run_master(self, master_msg):
        """
        Main entry for the master device in each forward pass.
        The messages were first collected from each devices (including the master device), and then
        an callback will be invoked to compute the message to be sent back to each devices
        (including the master device).

        Args:
            master_msg: the message that the master want to send to itself. This will be placed as the first
            message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example.

        Returns: the message to be sent back to the master device.

        """
        self._activated = True

        intermediates = [(0, master_msg)]
        for i in range(self.nr_slaves):
            intermediates.append(self._queue.get())

        results = self._master_callback(intermediates)
        assert results[0][0] == 0, 'The first result should belongs to the master.'

        for i, res in results:
            if i == 0:
                continue
            self._registry[i].result.put(res)

        for i in range(self.nr_slaves):
            assert self._queue.get() is True

        return results[0][1]

    @property
    def nr_slaves(self):
        return len(self._registry)
mf98qq94

mf98qq941#

将TorchScript生成方法从torch.jit.script切换到torch.jit.trace,它可以工作,不需要注解任何东西。或者torch.onnx.export有时也可以工作。

xnifntxz

xnifntxz2#

我在尝试在使用torch的Python脚本上使用PyInstaller时遇到了这个问题。我在这个Github线程中执行了第3步,将modeling_deberta.py中的标签更改为@torch.jit._script_if_tracing。(请注意,在Github的答案中,git clone中有一个错别字,其中的“transormers”而不是“transformers”,并且文件路径略有不同:src/transformers/models/deberta/modeling_deberta.py。为了安全起见,我也在modeling_deberta_v2.py中做了。)

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