split framework TARGETS to individual files (#475)

Summary:
Pull Request resolved: #475

Another try after D47418445 reverted the previous change.
1. split the framework targets, copied from D47418445
2. change the imports to directly use name in submodule.
3. run autodeps through arc lint.

Reviewed By: gunchu

Differential Revision: D47638416

fbshipit-source-id: 82b0bd027907d2579b2b049dab84d5fee2d07b2c

docs/source/framework/unit.rst

@@ -30,7 +30,7 @@ Here is an example of a unit which extends TrainUnit, EvalUnit, and PredictUnit.
 
 .. code-block:: python
 
- from torchtnt.framework import TrainUnit, EvalUnit, PredictUnit
+ from torchtnt.framework.unit import TrainUnit, EvalUnit, PredictUnit
 
  Batch = Tuple[torch.tensor, torch.tensor]
 

examples/auto_unit_example.py

@@ -17,8 +17,9 @@
 from torch.distributed import launcher as pet
 from torch.utils.data.dataset import Dataset, TensorDataset
 from torcheval.metrics import BinaryAccuracy
-from torchtnt.framework import AutoUnit, fit, State
-from torchtnt.framework.state import EntryPoint
+from torchtnt.framework.auto_unit import AutoUnit
+from torchtnt.framework.fit import fit
+from torchtnt.framework.state import EntryPoint, State
 from torchtnt.utils import init_from_env, seed, TLRScheduler
 from torchtnt.utils.loggers import TensorBoardLogger
 

examples/mingpt/main.py

@@ -22,7 +22,9 @@
     GPTConfig,
     OptimizerConfig,
 )
-from torchtnt.framework import AutoUnit, fit, State
+from torchtnt.framework.auto_unit import AutoUnit
+from torchtnt.framework.fit import fit
+from torchtnt.framework.state import State
 from torchtnt.utils import init_from_env, seed, TLRScheduler
 from torchtnt.utils.loggers import TensorBoardLogger
 

examples/mnist/main.py

@@ -18,7 +18,9 @@
 from torch.optim.lr_scheduler import StepLR
 from torch.utils.data import DataLoader
 from torcheval.metrics import MulticlassAccuracy
-from torchtnt.framework import AutoUnit, fit, State
+from torchtnt.framework.auto_unit import AutoUnit
+from torchtnt.framework.fit import fit
+from torchtnt.framework.state import State
 from torchtnt.utils import init_from_env, seed, TLRScheduler
 from torchtnt.utils.loggers import TensorBoardLogger
 from torchvision import datasets, transforms

examples/torchdata_train_example.py

@@ -19,7 +19,9 @@
 from torchdata.dataloader2 import DataLoader2, MultiProcessingReadingService
 from torchdata.datapipes.iter import IterableWrapper
 from torcheval.metrics import BinaryAccuracy
-from torchtnt.framework import State, train, TrainUnit
+from torchtnt.framework.state import State
+from torchtnt.framework.train import train
+from torchtnt.framework.unit import TrainUnit
 from torchtnt.utils import copy_data_to_device, init_from_env, seed, TLRScheduler
 
 from torchtnt.utils.loggers import TensorBoardLogger

examples/torchrec/main.py

@@ -39,9 +39,11 @@
 from torchrec.modules.embedding_configs import EmbeddingBagConfig
 from torchrec.optim.keyed import KeyedOptimizerWrapper
 from torchrec.optim.optimizers import in_backward_optimizer_filter
-
-from torchtnt.framework import EvalUnit, fit, State, TrainUnit
 from torchtnt.framework.callbacks import TQDMProgressBar
+from torchtnt.framework.fit import fit
+from torchtnt.framework.state import State
+
+from torchtnt.framework.unit import EvalUnit, TrainUnit
 from torchtnt.utils import (
     get_process_group_backend_from_device,
     init_from_env,

examples/train_unit_example.py

@@ -16,7 +16,9 @@
 import torch.nn as nn
 from torch.utils.data.dataset import Dataset, TensorDataset
 from torcheval.metrics import BinaryAccuracy
-from torchtnt.framework import State, train, TrainUnit
+from torchtnt.framework.state import State
+from torchtnt.framework.train import train
+from torchtnt.framework.unit import TrainUnit
 from torchtnt.utils import copy_data_to_device, init_from_env, seed, TLRScheduler
 
 from torchtnt.utils.loggers import TensorBoardLogger

tests/framework/callbacks/test_module_summary.py

@@ -10,9 +10,9 @@
 from unittest.mock import MagicMock
 
 import torch
-
-from torchtnt.framework import AutoUnit
 from torchtnt.framework._test_utils import DummyTrainUnit, generate_random_dataloader
+
+from torchtnt.framework.auto_unit import AutoUnit
 from torchtnt.framework.callbacks.module_summary import ModuleSummary
 from torchtnt.framework.state import EntryPoint, PhaseState, State
 from torchtnt.utils.version import is_torch_version_geq_1_13

tests/framework/callbacks/test_torchsnapshot_saver.py

@@ -16,9 +16,9 @@
 import torch
 from torch.distributed import launcher
 from torch.optim.lr_scheduler import ExponentialLR
-from torchtnt.framework import AutoUnit
 
 from torchtnt.framework._test_utils import DummyTrainUnit, generate_random_dataloader
+from torchtnt.framework.auto_unit import AutoUnit
 from torchtnt.framework.callbacks import Lambda, TorchSnapshotSaver
 from torchtnt.framework.state import State
 from torchtnt.framework.train import train

torchtnt/framework/_test_utils.py

@@ -10,8 +10,7 @@
 import torch
 from torch import nn, Tensor
 from torch.utils.data import DataLoader, Dataset, IterableDataset, TensorDataset
-from torchtnt.framework import PhaseState
-from torchtnt.framework.state import EntryPoint, State
+from torchtnt.framework.state import EntryPoint, PhaseState, State
 from torchtnt.framework.unit import EvalUnit, PredictUnit, TrainUnit
 
 Batch = Tuple[torch.Tensor, torch.Tensor]

torchtnt/framework/callback.py

@@ -21,7 +21,9 @@ class Callback:
 
     .. code-block:: python
 
-      from torchtnt.framework import Callback, State, TEvalUnit, TPredictUnit, TTrainUnit
+      from torchtnt.framework.callback import Callback
+      from torchtnt.framework.state import State
+      from torchtnt.framework.unit import TEvalUnit, TPredictUnit, TTrainUnit
 
       class PrintingCallback(Callback):
           def on_train_start(self, state: State, unit: TTrainUnit) -> None:

torchtnt/framework/callbacks/lambda_callback.py

@@ -39,8 +39,8 @@ class Lambda(Callback):
 
     Examples::
 
-        from torchtnt.framework import evaluate
         from torchtnt.framework.callbacks import Lambda
+        from torchtnt.framework.evaluate import evaluate
 
         dataloader = MyDataLoader()
         unit = MyUnit()

torchtnt/framework/evaluate.py

@@ -51,7 +51,7 @@ def evaluate(
 
     .. code-block:: python
 
-        from torchtnt.framework import evaluate
+        from torchtnt.framework.evaluate import evaluate
 
         eval_unit = MyEvalUnit(module=..., optimizer=..., lr_scheduler=...)
         eval_dataloader = torch.utils.data.DataLoader(...)

torchtnt/framework/fit.py

@@ -60,7 +60,7 @@ def fit(
 
     .. code-block:: python
 
-        from torchtnt.framework import fit
+        from torchtnt.framework.fit import fit
 
         fit_unit = MyFitUnit(module=..., optimizer=..., lr_scheduler=...)
         train_dataloader = torch.utils.data.DataLoader(...)

torchtnt/framework/predict.py

@@ -51,7 +51,7 @@ def predict(
 
     .. code-block:: python
 
-        from torchtnt.framework import predict
+        from torchtnt.framework.predict import predict
 
         predict_unit = MyPredictUnit(module=..., optimizer=..., lr_scheduler=...)
         predict_dataloader = torch.utils.data.DataLoader(...)

torchtnt/framework/train.py

@@ -58,7 +58,7 @@ def train(
 
     .. code-block:: python
 
-        from torchtnt.framework import train
+        from torchtnt.framework.train import train
 
         train_unit = MyTrainUnit(module=..., optimizer=..., lr_scheduler=...)
         train_dataloader = torch.utils.data.DataLoader(...)

torchtnt/framework/unit.py

@@ -194,7 +194,7 @@ class TrainUnit(AppStateMixin, _OnExceptionMixin, Generic[TTrainData], ABC):
 
     .. code-block:: python
 
-      from torchtnt.framework import TrainUnit
+      from torchtnt.framework.unit import TrainUnit
 
       Batch = Tuple[torch.tensor, torch.tensor]
       # specify type of the data in each batch of the dataloader to allow for typechecking
@@ -286,7 +286,7 @@ class EvalUnit(AppStateMixin, _OnExceptionMixin, Generic[TEvalData], ABC):
 
     .. code-block:: python
 
-      from torchtnt.framework import EvalUnit
+      from torchtnt.framework.unit import EvalUnit
 
       Batch = Tuple[torch.tensor, torch.tensor]
       # specify type of the data in each batch of the dataloader to allow for typechecking
@@ -373,7 +373,7 @@ class PredictUnit(
 
     .. code-block:: python
 
-      from torchtnt.framework import PredictUnit
+      from torchtnt.framework.unit import PredictUnit
 
       Batch = Tuple[torch.tensor, torch.tensor]
       # specify type of the data in each batch of the dataloader to allow for typechecking