221 lines
9.5 KiB
Python
221 lines
9.5 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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r"""
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Quantization
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^^^^^^^^^^^^
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"""
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import functools
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from typing import Any, Callable, Optional, Sequence, Union
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import torch
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from torch.quantization import QConfig
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import pytorch_lightning as pl
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from pytorch_lightning.callbacks.base import Callback
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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def wrap_qat_forward_context(
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quant_cb, model: "pl.LightningModule", func: Callable, trigger_condition: Optional[Union[Callable, int]] = None
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) -> Callable:
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"""
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Decorator to wrap forward path as it is needed to quantize inputs and dequantize outputs for in/out compatibility
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Moreover this version has the (de)quantization conditional as it may not be needed for the training all the time
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"""
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# todo: consider using registering hook before/after forward
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@functools.wraps(func)
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def wrapper(data) -> Any:
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_is_func_true = isinstance(trigger_condition, Callable) and trigger_condition(model.trainer)
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_is_count_true = isinstance(trigger_condition, int) and quant_cb._forward_calls < trigger_condition
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_quant_run = trigger_condition is None or _is_func_true or _is_count_true
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# apply custom trigger
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if _quant_run:
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quant_cb._forward_calls += 1
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data = model.quant(data)
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data = func(data)
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# apply custom trigger
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if _quant_run:
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data = model.dequant(data)
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return data
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return wrapper
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def wrap_quantize_forward_context(model: "pl.LightningModule", func: Callable) -> Callable:
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"""
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Decorator to wrap forward path as it is needed to quantize inputs and dequantize outputs for in/out compatibility
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"""
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# todo: consider using registering hook before/after forward
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@functools.wraps(func)
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def wrapper(data) -> Any:
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data = model.quant(data)
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data = func(data)
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data = model.dequant(data)
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return data
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return wrapper
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def _recursive_hasattr(obj: Any, attribs: str, state: bool = True) -> bool:
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"""recursive check if model has some layers denoted with '.'"""
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if "." in attribs:
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attrib, attribs = attribs.split(".", 1)
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if hasattr(obj, attrib):
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return _recursive_hasattr(getattr(obj, attrib), attribs, state)
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return False
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return state and hasattr(obj, attribs)
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class QuantizationAwareTraining(Callback):
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"""
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Quantization allows speeding up inference and decreasing memory requirements
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by performing computations and storing tensors at lower bitwidths
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(such as INT8 or FLOAT16) than floating point precision.
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We use native PyTorch API so for more information
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see `Quantization <https://pytorch.org/docs/stable/quantization.html#quantization-aware-training>`_.
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.. warning:: ``QuantizationAwareTraining`` is in beta and subject to change.
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Args:
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qconfig: quantization configuration:
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- 'fbgemm' for server inference.
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- 'qnnpack' for mobile inference.
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- a custom `torch.quantization.QConfig
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<https://pytorch.org/docs/stable/torch.quantization.html#torch.quantization.QConfig>`_.
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observer_type: allows switching between ``MovingAverageMinMaxObserver`` as "average" (default)
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and ``HistogramObserver`` as "histogram" which is more computationally expensive.
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collect_quantization: count or custom function to collect quantization statistics:
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- ``None`` (deafult). The quantization observer is called in each module forward
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(useful for collecting extended statistic when useing image/data augmentation).
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- ``int``. Use to set a fixed number of calls, starting from the beginning.
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- ``Callable``. Custom function with single trainer argument.
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See this example to trigger only the last epoch:
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.. code-block:: python
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def custom_trigger_last(trainer):
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return trainer.current_epoch == (trainer.max_epochs - 1)
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QuantizationAwareTraining(collect_quantization=custom_trigger_last)
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modules_to_fuse: allows you fuse a few layers together as shown in
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`diagram <https://pytorch.org/docs/stable/quantization.html#quantization-aware-training>`_
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to find which layer types can be fused, check https://github.com/pytorch/pytorch/pull/43286.
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input_compatible: preserve quant/dequant layers. This allows to feat any input as to the original model,
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but break compatibility to torchscript and export with ``torch.save``.
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quantize_on_fit_end: perform the quantization in `on_fit_end`.
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Note that once converted, the model cannot be put in training mode again.
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"""
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OBSERVER_TYPES = ("histogram", "average")
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def __init__(
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self,
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qconfig: Union[str, QConfig] = "fbgemm",
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observer_type: str = "average",
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collect_quantization: Optional[Union[int, Callable]] = None,
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modules_to_fuse: Optional[Sequence] = None,
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input_compatible: bool = True,
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quantize_on_fit_end: bool = True,
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) -> None:
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_valid_qconf_str = isinstance(qconfig, str) and qconfig in torch.backends.quantized.supported_engines
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if not isinstance(qconfig, QConfig) and not _valid_qconf_str:
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raise MisconfigurationException(
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f"Unsupported qconfig: f{qconfig}.\nTry one of defaults: {torch.backends.quantized.supported_engines}"
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)
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self._qconfig = qconfig
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if observer_type not in self.OBSERVER_TYPES:
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raise MisconfigurationException(
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f'Unsupported observer type "{observer_type}", allowed are {self.OBSERVER_TYPES}.'
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)
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self._observer_type = observer_type
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if collect_quantization is not None and not isinstance(collect_quantization, (int, Callable)):
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raise MisconfigurationException(
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f'Unsupported `collect_quantization` "{collect_quantization}", allowed are `int` or `Callable`.'
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)
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self._collect_quantization = collect_quantization
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self.modules_to_fuse = modules_to_fuse
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self._input_compatible = input_compatible
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self._convert_on_fit_end = quantize_on_fit_end
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self._forward_calls = 0
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def _check_feasible_fuse(self, model):
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if not self.modules_to_fuse:
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return False
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for group in self.modules_to_fuse:
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if not all(_recursive_hasattr(model, m) for m in group):
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raise MisconfigurationException(
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f"You have requested to fuse {group} but one or more of them is not your model attributes"
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)
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return True
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def on_fit_start(self, trainer, pl_module):
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# QuantStub converts tensors from floating point to quantized
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pl_module.quant = torch.quantization.QuantStub()
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# DeQuantStub converts tensors from quantized to floating point
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pl_module.dequant = torch.quantization.DeQuantStub()
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# manually specify where tensors will be converted from quantized
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# to floating point in the quantized model
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self.__module_forward = pl_module.forward
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pl_module.forward = wrap_qat_forward_context(
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quant_cb=self, model=pl_module, func=pl_module.forward, trigger_condition=self._collect_quantization
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)
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# attach a global qconfig, which contains information about what kind
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# of observers to attach. Use 'fbgemm' for server inference
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if isinstance(self._qconfig, str):
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if self._observer_type == "histogram":
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pl_module.qconfig = torch.quantization.get_default_qconfig(self._qconfig)
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elif self._observer_type == "average":
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pl_module.qconfig = torch.quantization.get_default_qat_qconfig(self._qconfig)
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elif isinstance(self._qconfig, QConfig):
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pl_module.qconfig = self._qconfig
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if self._check_feasible_fuse(pl_module):
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torch.quantization.fuse_modules(pl_module, self.modules_to_fuse, inplace=True)
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# Prepare the model for QAT. This inserts observers and fake_quants in
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# the model that will observe weight and activation tensors during calibration.
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torch.quantization.prepare_qat(pl_module, inplace=True)
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def on_fit_end(self, trainer, pl_module):
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if not self._convert_on_fit_end:
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pl_module.forward = self.__module_forward
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return
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pl_module.eval()
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# Convert the observed model to a quantized model. This does several things:
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# quantizes the weights, computes and stores the scale and bias value to be
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# used with each activation tensor, fuses modules where appropriate,
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# and replaces key operators with quantized implementations.
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torch.quantization.convert(pl_module, inplace=True)
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# check we shall preserve wrapper
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if self._input_compatible:
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pl_module.forward = wrap_quantize_forward_context(model=pl_module, func=self.__module_forward)
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else:
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pl_module.forward = self.__module_forward
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