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from contextlib import AbstractContextManager |
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from typing import Any, Literal |
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import torch |
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from lightning_utilities.core.apply_func import apply_to_collection |
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from torch import Tensor |
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from torch.nn import Module |
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from typing_extensions import override |
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from lightning.fabric.plugins.precision.precision import Precision |
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from lightning.fabric.plugins.precision.utils import _convert_fp_tensor, _DtypeContextManager |
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class HalfPrecision(Precision): |
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"""Plugin for training with half precision. |
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Args: |
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precision: Whether to use ``torch.float16`` (``'16-true'``) or ``torch.bfloat16`` (``'bf16-true'``). |
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""" |
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precision: Literal["bf16-true", "16-true"] = "16-true" |
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def __init__(self, precision: Literal["bf16-true", "16-true"] = "16-true") -> None: |
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self.precision = precision |
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self._desired_input_dtype = torch.bfloat16 if precision == "bf16-true" else torch.float16 |
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@override |
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def convert_module(self, module: Module) -> Module: |
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return module.to(dtype=self._desired_input_dtype) |
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@override |
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def tensor_init_context(self) -> AbstractContextManager: |
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return _DtypeContextManager(self._desired_input_dtype) |
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@override |
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def module_init_context(self) -> AbstractContextManager: |
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return self.tensor_init_context() |
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@override |
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def forward_context(self) -> AbstractContextManager: |
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return self.tensor_init_context() |
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@override |
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def convert_input(self, data: Any) -> Any: |
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return apply_to_collection(data, function=_convert_fp_tensor, dtype=Tensor, dst_type=self._desired_input_dtype) |
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@override |
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def convert_output(self, data: Any) -> Any: |
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return apply_to_collection(data, function=_convert_fp_tensor, dtype=Tensor, dst_type=torch.get_default_dtype()) |
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