yet more pydantic v2 stuff

configs/train_decoder_config.test.json

@@ -9,7 +9,7 @@
                 "dim_mults": [1, 2, 4, 8],
                 "attn_dim_head": 16,
                 "attn_heads": 4,
-		"self_attn": [false, true, true, true]
+                "self_attn": [false, true, true, true]
             }
         ],
         "clip": {

dalle2_pytorch/__init__.py

@@ -1,10 +1,3 @@
-import torch
-from packaging import version
-
-if version.parse(torch.__version__) >= version.parse('2.0.0'):
-    from einops._torch_specific import allow_ops_in_compiled_graph
-    allow_ops_in_compiled_graph()
-
 from dalle2_pytorch.version import __version__
 from dalle2_pytorch.dalle2_pytorch import DALLE2, DiffusionPriorNetwork, DiffusionPrior, Unet, Decoder
 from dalle2_pytorch.dalle2_pytorch import OpenAIClipAdapter, OpenClipAdapter

dalle2_pytorch/train_configs.py

@@ -115,7 +115,7 @@ class TrackerConfig(BaseModel):
 class AdapterConfig(BaseModel):
     make: str = "openai"
     model: str = "ViT-L/14"
-    base_model_kwargs: Dict[str, Any] = None
+    base_model_kwargs: Optional[Dict[str, Any]] = None
 
     def create(self):
         if self.make == "openai":
@@ -134,8 +134,8 @@ class AdapterConfig(BaseModel):
 class DiffusionPriorNetworkConfig(BaseModel):
     dim: int
     depth: int
-    max_text_len: int = None
+    max_text_len: Optional[int] = None
-    num_timesteps: int = None
+    num_timesteps: Optional[int] = None
     num_time_embeds: int = 1
     num_image_embeds: int = 1
     num_text_embeds: int = 1
@@ -158,7 +158,7 @@ class DiffusionPriorNetworkConfig(BaseModel):
         return DiffusionPriorNetwork(**kwargs)
 
 class DiffusionPriorConfig(BaseModel):
-    clip: AdapterConfig = None
+    clip: Optional[AdapterConfig] = None
     net: DiffusionPriorNetworkConfig
     image_embed_dim: int
     image_size: int
@@ -195,7 +195,7 @@ class DiffusionPriorTrainConfig(BaseModel):
     use_ema: bool = True
     ema_beta: float = 0.99
     amp: bool = False
-    warmup_steps: int = None             # number of warmup steps
+    warmup_steps: Optional[int] = None   # number of warmup steps
     save_every_seconds: int = 3600       # how often to save
     eval_timesteps: List[int] = [64]     # which sampling timesteps to evaluate with
     best_validation_loss: float = 1e9    # the current best valudation loss observed
@@ -228,12 +228,12 @@ class TrainDiffusionPriorConfig(BaseModel):
 class UnetConfig(BaseModel):
     dim: int
     dim_mults: ListOrTuple[int]
-    image_embed_dim: int = None
+    image_embed_dim: Optional[int] = None
-    text_embed_dim: int = None
+    text_embed_dim: Optional[int] = None
-    cond_on_text_encodings: bool = None
+    cond_on_text_encodings: Optional[bool] = None
-    cond_dim: int = None
+    cond_dim: Optional[int] = None
     channels: int = 3
-    self_attn: ListOrTuple[int]
+    self_attn: SingularOrIterable[bool] = False
     attn_dim_head: int = 32
     attn_heads: int = 16
     init_cross_embed: bool = True
@@ -243,14 +243,14 @@ class UnetConfig(BaseModel):
 
 class DecoderConfig(BaseModel):
     unets: ListOrTuple[UnetConfig]
-    image_size: int = None
+    image_size: Optional[int] = None
     image_sizes: ListOrTuple[int] = None
-    clip: Optional[AdapterConfig]   # The clip model to use if embeddings are not provided
+    clip: Optional[AdapterConfig] = None   # The clip model to use if embeddings are not provided
     channels: int = 3
     timesteps: int = 1000
     sample_timesteps: Optional[SingularOrIterable[Optional[int]]] = None
     loss_type: str = 'l2'
-    beta_schedule: ListOrTuple[str] = None  # None means all cosine
+    beta_schedule: Optional[ListOrTuple[str]] = None  # None means all cosine
     learned_variance: SingularOrIterable[bool] = True
     image_cond_drop_prob: float = 0.1
     text_cond_drop_prob: float = 0.5
@@ -320,20 +320,20 @@ class DecoderTrainConfig(BaseModel):
     n_sample_images: int = 6                       # The number of example images to produce when sampling the train and test dataset
     cond_scale: Union[float, List[float]] = 1.0
     device: str = 'cuda:0'
-    epoch_samples: int = None                      # Limits the number of samples per epoch. None means no limit. Required if resample_train is true as otherwise the number of samples per epoch is infinite.
+    epoch_samples: Optional[int] = None                      # Limits the number of samples per epoch. None means no limit. Required if resample_train is true as otherwise the number of samples per epoch is infinite.
-    validation_samples: int = None                 # Same as above but for validation.
+    validation_samples: Optional[int] = None                 # Same as above but for validation.
     save_immediately: bool = False
     use_ema: bool = True
     ema_beta: float = 0.999
     amp: bool = False
-    unet_training_mask: ListOrTuple[bool] = None   # If None, use all unets
+    unet_training_mask: Optional[ListOrTuple[bool]] = None   # If None, use all unets
 
 class DecoderEvaluateConfig(BaseModel):
     n_evaluation_samples: int = 1000
-    FID: Dict[str, Any] = None
+    FID: Optional[Dict[str, Any]] = None
-    IS: Dict[str, Any] = None
+    IS: Optional[Dict[str, Any]] = None
-    KID: Dict[str, Any] = None
+    KID: Optional[Dict[str, Any]] = None
-    LPIPS: Dict[str, Any] = None
+    LPIPS: Optional[Dict[str, Any]] = None
 
 class TrainDecoderConfig(BaseModel):
     decoder: DecoderConfig

dalle2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '1.15.2'
+__version__ = '1.15.6'

setup.py

@@ -30,7 +30,7 @@ setup(
     'clip-anytorch>=2.5.2',
     'coca-pytorch>=0.0.5',
     'ema-pytorch>=0.0.7',
-    'einops>=0.6.1',
+    'einops>=0.7.0',
     'embedding-reader',
     'kornia>=0.5.4',
     'numpy',

train_decoder.py

@@ -511,7 +511,7 @@ def train(
         if next_task == 'eval':
             if exists(evaluate_config):
                 accelerator.print(print_ribbon(f"Starting Evaluation {epoch}", repeat=40))
-                evaluation = evaluate_trainer(trainer, dataloaders["val"], inference_device, first_trainable_unet, last_trainable_unet, clip=clip, inference_device=inference_device, **evaluate_config.dict(), condition_on_text_encodings=condition_on_text_encodings, cond_scale=cond_scale)
+                evaluation = evaluate_trainer(trainer, dataloaders["val"], inference_device, first_trainable_unet, last_trainable_unet, clip=clip, inference_device=inference_device, **evaluate_config.model_dump(), condition_on_text_encodings=condition_on_text_encodings, cond_scale=cond_scale)
                 if is_master:
                     tracker.log(evaluation, step=step())
             next_task = 'sample'
@@ -548,7 +548,7 @@ def create_tracker(accelerator: Accelerator, config: TrainDecoderConfig, config_
     accelerator.wait_for_everyone()  # If nodes arrive at this point at different times they might try to autoresume the current run which makes no sense and will cause errors
     tracker: Tracker = tracker_config.create(config, accelerator_config, dummy_mode=dummy)
     tracker.save_config(config_path, config_name='decoder_config.json')
-    tracker.add_save_metadata(state_dict_key='config', metadata=config.dict())
+    tracker.add_save_metadata(state_dict_key='config', metadata=config.model_dump())
     return tracker
     
 def initialize_training(config: TrainDecoderConfig, config_path):
@@ -585,7 +585,7 @@ def initialize_training(config: TrainDecoderConfig, config_path):
         val_prop = config.data.splits.val,
         test_prop = config.data.splits.test,
         n_sample_images=config.train.n_sample_images,
-        **config.data.dict(),
+        **config.data.model_dump(),
         rank = rank,
         seed = config.seed,
     )
@@ -636,7 +636,7 @@ def initialize_training(config: TrainDecoderConfig, config_path):
         inference_device=accelerator.device,
         evaluate_config=config.evaluate,
         condition_on_text_encodings=conditioning_on_text,
-        **config.train.dict(),
+        **config.train.model_dump(),
     )
     
 # Create a simple click command line interface to load the config and start the training