Merge branch 'main' of https://github.com/Jorgedavyd/LighTorch

lightorch/nn/sequential/residual.py

@@ -4,9 +4,7 @@
 
 
 class _Residual(nn.Module):
-    def __init__(
-        self, module: nn.Module, n_layers: int
-    ):
+    def __init__(self, module: nn.Module, n_layers: int):
         super().__init__()
         self.model = nn.ModuleList([module for _ in range(n_layers)])
 

lightorch/nn/transformer/transformer.py

@@ -78,7 +78,9 @@ def __init__(
         fc: Optional[nn.Module] = None,
         n_layers: int = 1,
     ) -> None:
-        assert (encoder is not None or decoder is not None), "Not valid parameters, must be at least one encoder or decoder."
+        assert (
+            encoder is not None or decoder is not None
+        ), "Not valid parameters, must be at least one encoder or decoder."
         super().__init__()
         self.embedding = embedding_layer
         self.pe = positional_encoding
@@ -133,8 +135,8 @@ def __init__(
 
     def _single_forward(
         self,
-        cell_1:TransformerCell,
-        cell_2:TransformerCell,
+        cell_1: TransformerCell,
+        cell_2: TransformerCell,
         head_1: Tensor,
         head_2: Tensor,
     ) -> Tuple[Tensor, Tensor]:
@@ -149,9 +151,7 @@ def _single_forward(
 
         return out0, out1
 
-    def forward(
-        self, head_1: Tensor, head_2: Tensor
-        ) -> Tuple[Tensor, Tensor]:
+    def forward(self, head_1: Tensor, head_2: Tensor) -> Tuple[Tensor, Tensor]:
         for cell_1, cell_2 in zip(self.cell_1, self.cell_2):
             head_1, head_2 = self._single_forward(cell_1, cell_2, head_1, head_2)
 

lightorch/training/supervised.py

@@ -38,15 +38,17 @@ def interval(algo: LRScheduler) -> str:
     else:
         return "epoch"
 
-class Module(LightningModule): 
-    """ 
-    init: triggers: Dict[str, Dict[str, float]] -> This is an interpretative implementation for grouped optimization where the parameters are stored in groups given a "trigger", namely, as trigger parameters you can put a string describing the beginning of the parameters to optimize in a group. 
-    optimizer: str | Optimizer -> Name of the optimizer or an Optimizer instance. 
-    scheduler: str | LRScheduler -> Name of the scheduler or a Scheduler instance. 
-    scheduler_kwargs: Dict[str, Any] -> Arguments of the scheduler. 
-    gradient_clip_algorithm: str -> Gradient clip algorithm [value, norm]. 
-    gradient_clip_val: float -> Clipping value. 
+
+class Module(LightningModule):
+    """
+    init: triggers: Dict[str, Dict[str, float]] -> This is an interpretative implementation for grouped optimization where the parameters are stored in groups given a "trigger", namely, as trigger parameters you can put a string describing the beginning of the parameters to optimize in a group.
+    optimizer: str | Optimizer -> Name of the optimizer or an Optimizer instance.
+    scheduler: str | LRScheduler -> Name of the scheduler or a Scheduler instance.
+    scheduler_kwargs: Dict[str, Any] -> Arguments of the scheduler.
+    gradient_clip_algorithm: str -> Gradient clip algorithm [value, norm].
+    gradient_clip_val: float -> Clipping value.
     """
+
     def __init__(
         self,
         *,
@@ -189,7 +191,9 @@ def _configure_scheduler(self, optimizer: Optimizer) -> LRScheduler:
         else:
             return self.scheduler(optimizer)
 
-    def configure_optimizers(self) -> Dict[str, Union[Optimizer, Dict[str, Union[float, int, LRScheduler]]]]:
+    def configure_optimizers(
+        self,
+    ) -> Dict[str, Union[Optimizer, Dict[str, Union[float, int, LRScheduler]]]]:
         optimizer = self._configure_optimizer()
         if self.scheduler is not None:
             scheduler = self._configure_scheduler(optimizer)

requirements.txt

@@ -0,0 +1,156 @@
+#
+# This file is autogenerated by pip-compile with Python 3.10
+# by the following command:
+#
+#    pip-compile ./requirements.in
+#
+aiohttp==3.9.5
+    # via fsspec
+aiosignal==1.3.1
+    # via aiohttp
+alembic==1.13.2
+    # via optuna
+async-timeout==4.0.3
+    # via aiohttp
+attrs==23.2.0
+    # via aiohttp
+colorlog==6.8.2
+    # via optuna
+einops==0.8.0
+    # via -r ./requirements.in
+filelock==3.15.4
+    # via
+    #   torch
+    #   triton
+frozenlist==1.4.1
+    # via
+    #   aiohttp
+    #   aiosignal
+fsspec[http]==2024.6.1
+    # via
+    #   lightning
+    #   pytorch-lightning
+    #   torch
+greenlet==3.0.3
+    # via sqlalchemy
+idna==3.7
+    # via yarl
+jinja2==3.1.4
+    # via torch
+lightning==2.3.3
+    # via -r ./requirements.in
+lightning-utilities==0.11.3.post0
+    # via
+    #   lightning
+    #   pytorch-lightning
+    #   torchmetrics
+mako==1.3.5
+    # via alembic
+markupsafe==2.1.5
+    # via
+    #   jinja2
+    #   mako
+mpmath==1.3.0
+    # via sympy
+multidict==6.0.5
+    # via
+    #   aiohttp
+    #   yarl
+networkx==3.3
+    # via torch
+numpy==2.0.0
+    # via
+    #   lightning
+    #   optuna
+    #   pytorch-lightning
+    #   torchmetrics
+    #   torchvision
+nvidia-cublas-cu12==12.1.3.1
+    # via
+    #   nvidia-cudnn-cu12
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cuda-cupti-cu12==12.1.105
+    # via torch
+nvidia-cuda-nvrtc-cu12==12.1.105
+    # via torch
+nvidia-cuda-runtime-cu12==12.1.105
+    # via torch
+nvidia-cudnn-cu12==8.9.2.26
+    # via torch
+nvidia-cufft-cu12==11.0.2.54
+    # via torch
+nvidia-curand-cu12==10.3.2.106
+    # via torch
+nvidia-cusolver-cu12==11.4.5.107
+    # via torch
+nvidia-cusparse-cu12==12.1.0.106
+    # via
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-nccl-cu12==2.20.5
+    # via torch
+nvidia-nvjitlink-cu12==12.5.82
+    # via
+    #   nvidia-cusolver-cu12
+    #   nvidia-cusparse-cu12
+nvidia-nvtx-cu12==12.1.105
+    # via torch
+optuna==3.6.1
+    # via -r ./requirements.in
+packaging==24.1
+    # via
+    #   lightning
+    #   lightning-utilities
+    #   optuna
+    #   pytorch-lightning
+    #   torchmetrics
+pillow==10.4.0
+    # via torchvision
+pytorch-lightning==2.3.3
+    # via lightning
+pyyaml==6.0.1
+    # via
+    #   lightning
+    #   optuna
+    #   pytorch-lightning
+sqlalchemy==2.0.31
+    # via
+    #   alembic
+    #   optuna
+sympy==1.13.0
+    # via torch
+torch==2.3.1
+    # via
+    #   -r ./requirements.in
+    #   lightning
+    #   pytorch-lightning
+    #   torchmetrics
+    #   torchvision
+torchmetrics==1.4.0.post0
+    # via
+    #   lightning
+    #   pytorch-lightning
+torchvision==0.18.1
+    # via -r ./requirements.in
+tqdm==4.66.4
+    # via
+    #   -r ./requirements.in
+    #   lightning
+    #   optuna
+    #   pytorch-lightning
+triton==2.3.1
+    # via torch
+typing-extensions==4.12.2
+    # via
+    #   alembic
+    #   lightning
+    #   lightning-utilities
+    #   pytorch-lightning
+    #   sqlalchemy
+    #   torch
+yarl==1.9.4
+    # via aiohttp
+
+# The following packages are considered to be unsafe in a requirements file:
+# setuptools