Relative -> Absolute imports (#343)

a-r-r-o-w · web-flow · commit ff8fddc75962 · 2025-03-21T16:34:06.000+05:30
diff --git a/finetrainers/data/dataloader.py b/finetrainers/data/dataloader.py
@@ -4,7 +4,7 @@
 import torch.distributed.checkpoint.stateful
 import torchdata.stateful_dataloader
 
-from ..logging import get_logger
+from finetrainers.logging import get_logger
 
 
 logger = get_logger()
diff --git a/finetrainers/data/dataset.py b/finetrainers/data/dataset.py
@@ -17,11 +17,12 @@
 from huggingface_hub import list_repo_files, repo_exists, snapshot_download
 from tqdm.auto import tqdm
 
-from .. import constants
-from .. import functional as FF
-from ..logging import get_logger
-from ..utils.import_utils import is_datasets_version
-from . import utils
+from finetrainers import constants
+from finetrainers import functional as FF
+from finetrainers.logging import get_logger
+from finetrainers.utils.import_utils import is_datasets_version
+
+from .utils import find_files
 
 
 import decord  # isort:skip
@@ -48,7 +49,7 @@ def __init__(self, root: str, infinite: bool = False) -> None:
         self.infinite = infinite
 
         data = []
-        caption_files = sorted(utils.find_files(self.root.as_posix(), "*.txt", depth=0))
+        caption_files = sorted(find_files(self.root.as_posix(), "*.txt", depth=0))
         for caption_file in caption_files:
             data_file = self._find_data_file(caption_file)
             if data_file:
@@ -122,7 +123,7 @@ def __init__(self, root: str, infinite: bool = False) -> None:
         self.infinite = infinite
 
         data = []
-        caption_files = sorted(utils.find_files(self.root.as_posix(), "*.txt", depth=0))
+        caption_files = sorted(find_files(self.root.as_posix(), "*.txt", depth=0))
         for caption_file in caption_files:
             data_file = self._find_data_file(caption_file)
             if data_file:
@@ -926,7 +927,7 @@ def _initialize_webdataset(
 def _has_data_caption_file_pairs(root: Union[pathlib.Path, List[str]], remote: bool = False) -> bool:
     # TODO(aryan): this logic can be improved
     if not remote:
-        caption_files = utils.find_files(root.as_posix(), "*.txt", depth=0)
+        caption_files = find_files(root.as_posix(), "*.txt", depth=0)
         for caption_file in caption_files:
             caption_file = pathlib.Path(caption_file)
             for extension in [*constants.SUPPORTED_IMAGE_FILE_EXTENSIONS, *constants.SUPPORTED_VIDEO_FILE_EXTENSIONS]:
diff --git a/finetrainers/data/precomputation.py b/finetrainers/data/precomputation.py
@@ -4,8 +4,8 @@
 import torch
 from tqdm.auto import tqdm
 
-from .. import utils
-from ..logging import get_logger
+from finetrainers.logging import get_logger
+from finetrainers.utils import delete_files
 
 
 logger = get_logger()
@@ -147,7 +147,7 @@ def __init__(
         self._save_dir.mkdir(parents=True, exist_ok=True)
 
         subdirectories = [f for f in self._save_dir.iterdir() if f.is_dir()]
-        utils.delete_files(subdirectories)
+        delete_files(subdirectories)
 
     def consume(
         self,
diff --git a/finetrainers/models/cogvideox/base_specification.py b/finetrainers/models/cogvideox/base_specification.py
@@ -13,13 +13,14 @@
 from PIL.Image import Image
 from transformers import AutoModel, AutoTokenizer, T5EncoderModel, T5Tokenizer
 
-from ... import data
-from ...logging import get_logger
-from ...processors import ProcessorMixin, T5Processor
-from ...typing import ArtifactType, SchedulerType
-from ...utils import _enable_vae_memory_optimizations, get_non_null_items
-from ..modeling_utils import ModelSpecification
-from ..utils import DiagonalGaussianDistribution
+from finetrainers.data import VideoArtifact
+from finetrainers.logging import get_logger
+from finetrainers.models.modeling_utils import ModelSpecification
+from finetrainers.models.utils import DiagonalGaussianDistribution
+from finetrainers.processors import ProcessorMixin, T5Processor
+from finetrainers.typing import ArtifactType, SchedulerType
+from finetrainers.utils import _enable_vae_memory_optimizations, get_non_null_items
+
 from .utils import prepare_rotary_positional_embeddings
 
 
@@ -359,7 +360,7 @@ def validation(
         }
         generation_kwargs = get_non_null_items(generation_kwargs)
         video = pipeline(**generation_kwargs).frames[0]
-        return [data.VideoArtifact(value=video)]
+        return [VideoArtifact(value=video)]
 
     def _save_lora_weights(
         self,
diff --git a/finetrainers/models/cogview4/base_specification.py b/finetrainers/models/cogview4/base_specification.py
@@ -12,13 +12,13 @@
 from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
 from transformers import AutoTokenizer, GlmModel
 
-from ... import data
-from ... import functional as FF
-from ...logging import get_logger
-from ...processors import CogView4GLMProcessor, ProcessorMixin
-from ...typing import ArtifactType, SchedulerType
-from ...utils import _enable_vae_memory_optimizations, get_non_null_items
-from ..modeling_utils import ModelSpecification
+import finetrainers.functional as FF
+from finetrainers.data import ImageArtifact
+from finetrainers.logging import get_logger
+from finetrainers.models.modeling_utils import ModelSpecification
+from finetrainers.processors import CogView4GLMProcessor, ProcessorMixin
+from finetrainers.typing import ArtifactType, SchedulerType
+from finetrainers.utils import _enable_vae_memory_optimizations, get_non_null_items
 
 
 logger = get_logger()
@@ -345,7 +345,7 @@ def validation(
         }
         generation_kwargs = get_non_null_items(generation_kwargs)
         image = pipeline(**generation_kwargs).images[0]
-        return [data.ImageArtifact(value=image)]
+        return [ImageArtifact(value=image)]
 
     def _save_lora_weights(
         self,
diff --git a/finetrainers/models/hunyuan_video/base_specification.py b/finetrainers/models/hunyuan_video/base_specification.py
@@ -12,13 +12,13 @@
 from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
 from transformers import AutoTokenizer, CLIPTextModel, CLIPTokenizer, LlamaModel
 
-from ... import data
-from ... import functional as FF
-from ...logging import get_logger
-from ...processors import CLIPPooledProcessor, LlamaProcessor, ProcessorMixin
-from ...typing import ArtifactType, SchedulerType
-from ...utils import _enable_vae_memory_optimizations, get_non_null_items
-from ..modeling_utils import ModelSpecification
+import finetrainers.functional as FF
+from finetrainers.data import VideoArtifact
+from finetrainers.logging import get_logger
+from finetrainers.models.modeling_utils import ModelSpecification
+from finetrainers.processors import CLIPPooledProcessor, LlamaProcessor, ProcessorMixin
+from finetrainers.typing import ArtifactType, SchedulerType
+from finetrainers.utils import _enable_vae_memory_optimizations, get_non_null_items
 
 
 logger = get_logger()
@@ -347,7 +347,7 @@ def validation(
         }
         generation_kwargs = get_non_null_items(generation_kwargs)
         video = pipeline(**generation_kwargs).frames[0]
-        return [data.VideoArtifact(value=video)]
+        return [VideoArtifact(value=video)]
 
     def _save_lora_weights(
         self,
diff --git a/finetrainers/models/ltx_video/base_specification.py b/finetrainers/models/ltx_video/base_specification.py
@@ -15,14 +15,14 @@
 from PIL.Image import Image
 from transformers import AutoModel, AutoTokenizer, T5EncoderModel, T5Tokenizer
 
-from ... import data
-from ... import functional as FF
-from ...logging import get_logger
-from ...parallel import ParallelBackendEnum
-from ...processors import ProcessorMixin, T5Processor
-from ...typing import ArtifactType, SchedulerType
-from ...utils import _enable_vae_memory_optimizations, get_non_null_items
-from ..modeling_utils import ModelSpecification
+import finetrainers.functional as FF
+from finetrainers.data import VideoArtifact
+from finetrainers.logging import get_logger
+from finetrainers.models.modeling_utils import ModelSpecification
+from finetrainers.parallel import ParallelBackendEnum
+from finetrainers.processors import ProcessorMixin, T5Processor
+from finetrainers.typing import ArtifactType, SchedulerType
+from finetrainers.utils import _enable_vae_memory_optimizations, get_non_null_items
 
 
 logger = get_logger()
@@ -373,7 +373,7 @@ def validation(
         }
         generation_kwargs = get_non_null_items(generation_kwargs)
         video = pipeline(**generation_kwargs).frames[0]
-        return [data.VideoArtifact(value=video)]
+        return [VideoArtifact(value=video)]
 
     def _save_lora_weights(
         self,
diff --git a/finetrainers/models/modeling_utils.py b/finetrainers/models/modeling_utils.py
@@ -5,11 +5,11 @@
 from diffusers.configuration_utils import FrozenDict
 from PIL.Image import Image
 
-from ..logging import get_logger
-from ..parallel import ParallelBackendEnum
-from ..processors import ProcessorMixin
-from ..typing import ArtifactType, SchedulerType, TokenizerType
-from ..utils import resolve_component_cls
+from finetrainers.logging import get_logger
+from finetrainers.parallel import ParallelBackendEnum
+from finetrainers.processors import ProcessorMixin
+from finetrainers.typing import ArtifactType, SchedulerType, TokenizerType
+from finetrainers.utils import resolve_component_cls
 
 
 logger = get_logger()
diff --git a/finetrainers/models/wan/base_specification.py b/finetrainers/models/wan/base_specification.py
@@ -14,13 +14,13 @@
 from PIL.Image import Image
 from transformers import AutoModel, AutoTokenizer, UMT5EncoderModel
 
-from ... import data
-from ... import functional as FF
-from ...logging import get_logger
-from ...processors import ProcessorMixin, T5Processor
-from ...typing import ArtifactType, SchedulerType
-from ...utils import get_non_null_items
-from ..modeling_utils import ModelSpecification
+import finetrainers.functional as FF
+from finetrainers.data import VideoArtifact
+from finetrainers.logging import get_logger
+from finetrainers.models.modeling_utils import ModelSpecification
+from finetrainers.processors import ProcessorMixin, T5Processor
+from finetrainers.typing import ArtifactType, SchedulerType
+from finetrainers.utils import get_non_null_items
 
 
 logger = get_logger()
@@ -335,7 +335,7 @@ def validation(
         }
         generation_kwargs = get_non_null_items(generation_kwargs)
         video = pipeline(**generation_kwargs).frames[0]
-        return [data.VideoArtifact(value=video)]
+        return [VideoArtifact(value=video)]
 
     def _save_lora_weights(
         self,
diff --git a/finetrainers/parallel/accelerate.py b/finetrainers/parallel/accelerate.py
@@ -8,8 +8,9 @@
 import torch
 from diffusers.utils import is_accelerate_available
 
-from ..logging import get_logger
-from ..utils import get_device_info
+from finetrainers.logging import get_logger
+from finetrainers.utils import get_device_info
+
 from .base import BaseCheckpointer, BaseParallelBackend
 
 
diff --git a/finetrainers/parallel/base.py b/finetrainers/parallel/base.py
@@ -3,7 +3,7 @@
 
 import torch
 
-from ..trackers import TrackerType, initialize_trackers
+from finetrainers.trackers import TrackerType, initialize_trackers
 
 
 class BaseParallelBackend:
diff --git a/finetrainers/parallel/ptd.py b/finetrainers/parallel/ptd.py
@@ -18,15 +18,16 @@
     set_model_state_dict,
 )
 
-from ..data import DPDataLoader
-from ..logging import get_logger
-from ..utils import enable_determinism, get_device_info
-from ..utils._common import DIFFUSERS_TRANSFORMER_BLOCK_NAMES
+from finetrainers.data import DPDataLoader
+from finetrainers.logging import get_logger
+from finetrainers.utils import enable_determinism, get_device_info
+from finetrainers.utils._common import DIFFUSERS_TRANSFORMER_BLOCK_NAMES
+
 from .base import BaseCheckpointer, BaseParallelBackend
 
 
 if TYPE_CHECKING:
-    from .. import optimizer
+    from finetrainers import optimizer
 
 
 _device_type, _device_module = get_device_info()
diff --git a/finetrainers/patches/__init__.py b/finetrainers/patches/__init__.py
@@ -2,13 +2,13 @@
 
 
 if TYPE_CHECKING:
-    from ..args import BaseArgs
-    from ..parallel import ParallelBackendType
+    from finetrainers.args import BaseArgs
+    from finetrainers.parallel import ParallelBackendType
 
 
 def perform_patches_for_training(args: "BaseArgs", parallel_backend: "ParallelBackendType") -> None:
     # To avoid circular imports
-    from ..config import ModelType, TrainingType
+    from finetrainers.config import ModelType, TrainingType
 
     if args.model_name == ModelType.LTX_VIDEO:
         from .models.ltx_video import patch
diff --git a/finetrainers/patches/dependencies/peft/patch.py b/finetrainers/patches/dependencies/peft/patch.py
@@ -2,7 +2,7 @@
 
 from peft.tuners.tuners_utils import BaseTunerLayer
 
-from ...utils import DisableTensorToDtype
+from finetrainers.patches.utils import DisableTensorToDtype
 
 
 def patch_peft_move_adapter_to_device_of_base_layer() -> None:
diff --git a/finetrainers/processors/text.py b/finetrainers/processors/text.py
@@ -2,7 +2,8 @@
 
 import torch
 
-from .. import functional as FF
+import finetrainers.functional as FF
+
 from .base import ProcessorMixin
 
 
diff --git a/finetrainers/trainer/config_utils.py b/finetrainers/trainer/config_utils.py
@@ -3,7 +3,7 @@
 
 
 if TYPE_CHECKING:
-    from ..args import BaseArgs
+    from finetrainers.args import BaseArgs
 
 
 class ConfigMixin:
diff --git a/finetrainers/trainer/sft_trainer/config.py b/finetrainers/trainer/sft_trainer/config.py
@@ -1,11 +1,11 @@
 import argparse
 from typing import TYPE_CHECKING, List, Union
 
-from ..config_utils import ConfigMixin
+from finetrainers.trainer.config_utils import ConfigMixin
 
 
 if TYPE_CHECKING:
-    from ...args import BaseArgs
+    from finetrainers.args import BaseArgs
 
 
 class SFTLowRankConfig(ConfigMixin):
diff --git a/finetrainers/trainer/sft_trainer/trainer.py b/finetrainers/trainer/sft_trainer/trainer.py
@@ -20,14 +20,14 @@
 from peft import LoraConfig, get_peft_model_state_dict
 from tqdm import tqdm
 
-from ... import data, logging, optimizer, parallel, patches, utils
-from ...config import TrainingType
-from ...state import State, TrainState
+from finetrainers import data, logging, optimizer, parallel, patches, utils
+from finetrainers.config import TrainingType
+from finetrainers.state import State, TrainState
 
 
 if TYPE_CHECKING:
-    from ...args import BaseArgs
-    from ...models import ModelSpecification
+    from finetrainers.args import BaseArgs
+    from finetrainers.models import ModelSpecification
 
 
 logger = logging.get_logger()
diff --git a/finetrainers/utils/data.py b/finetrainers/utils/data.py
@@ -4,7 +4,7 @@
 import torch
 from accelerate.logging import get_logger
 
-from ..constants import PRECOMPUTED_CONDITIONS_DIR_NAME, PRECOMPUTED_LATENTS_DIR_NAME
+from finetrainers.constants import PRECOMPUTED_CONDITIONS_DIR_NAME, PRECOMPUTED_LATENTS_DIR_NAME
 
 
 logger = get_logger("finetrainers")
diff --git a/finetrainers/utils/file.py b/finetrainers/utils/file.py
@@ -3,7 +3,7 @@
 from pathlib import Path
 from typing import List, Union
 
-from ..logging import get_logger
+from finetrainers.logging import get_logger
 
 
 logger = get_logger()
diff --git a/finetrainers/utils/import_utils.py b/finetrainers/utils/import_utils.py
@@ -5,7 +5,7 @@
 import importlib_metadata
 from packaging.version import Version, parse
 
-from ..logging import get_logger
+from finetrainers.logging import get_logger
 
 
 logger = get_logger()
diff --git a/finetrainers/utils/torch.py b/finetrainers/utils/torch.py
