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""" |
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Streaming module API that should be implemented by all Streaming components, |
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""" |
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from contextlib import contextmanager |
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import typing as tp |
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from torch import nn |
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import torch |
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State = tp.Dict[str, torch.Tensor] |
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class StreamingModule(nn.Module): |
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"""Common API for streaming components. |
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Each streaming component has a streaming state, which is just a dict[str, Tensor]. |
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By convention, the first dim of each tensor must be the batch size. |
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Don't use dots in the key names, as this would clash with submodules |
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(like in state_dict). |
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If `self._is_streaming` is True, the component should use and remember |
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the proper state inside `self._streaming_state`. |
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To set a streaming component in streaming state, use |
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with module.streaming(): |
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... |
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This will automatically reset the streaming state when exiting the context manager. |
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This also automatically propagates to all streaming children module. |
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Some module might also implement the `StreamingModule.flush` method, although |
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this one is trickier, as all parents module must be StreamingModule and implement |
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it as well for it to work properly. See `StreamingSequential` after. |
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""" |
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def __init__(self) -> None: |
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super().__init__() |
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self._streaming_state: State = {} |
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self._is_streaming = False |
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def _apply_named_streaming(self, fn: tp.Any): |
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for name, module in self.named_modules(): |
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if isinstance(module, StreamingModule): |
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fn(name, module) |
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def _set_streaming(self, streaming: bool): |
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def _set_streaming(name, module): |
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module._is_streaming = streaming |
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self._apply_named_streaming(_set_streaming) |
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@contextmanager |
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def streaming(self): |
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"""Context manager to enter streaming mode. Reset streaming state on exit. |
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""" |
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self._set_streaming(True) |
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try: |
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yield |
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finally: |
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self._set_streaming(False) |
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self.reset_streaming() |
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def reset_streaming(self): |
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"""Reset the streaming state. |
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""" |
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def _reset(name: str, module: StreamingModule): |
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module._streaming_state.clear() |
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self._apply_named_streaming(_reset) |
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def get_streaming_state(self) -> State: |
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"""Return the streaming state, including that of sub-modules. |
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""" |
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state: State = {} |
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def _add(name: str, module: StreamingModule): |
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if name: |
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name += "." |
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for key, value in module._streaming_state.items(): |
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state[name + key] = value |
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self._apply_named_streaming(_add) |
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return state |
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def set_streaming_state(self, state: State): |
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"""Set the streaming state, including that of sub-modules. |
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""" |
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state = dict(state) |
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def _set(name: str, module: StreamingModule): |
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if name: |
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name += "." |
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module._streaming_state.clear() |
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for key, value in list(state.items()): |
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if key.startswith(name): |
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local_key = key[len(name):] |
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if '.' not in local_key: |
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module._streaming_state[local_key] = value |
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del state[key] |
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self._apply_named_streaming(_set) |
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assert len(state) == 0, list(state.keys()) |
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def flush(self, x: tp.Optional[torch.Tensor] = None): |
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"""Flush any remaining outputs that were waiting for completion. |
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Typically, for convolutions, this will add the final padding |
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and process the last buffer. |
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This should take an optional argument `x`, which will be provided |
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if a module before this one in the streaming pipeline has already |
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spitted out a flushed out buffer. |
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""" |
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if x is None: |
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return None |
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else: |
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return self(x) |
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class StreamingSequential(StreamingModule, nn.Sequential): |
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"""A streaming compatible alternative of `nn.Sequential`. |
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""" |
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def flush(self, x: tp.Optional[torch.Tensor] = None): |
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for module in self: |
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if isinstance(module, StreamingModule): |
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x = module.flush(x) |
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elif x is not None: |
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x = module(x) |
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return x |
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