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remove Black preview mode

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also fix multiline logs in training
This commit is contained in:
Pierre Chapuis 2023-12-04 10:49:26 +01:00
parent 4176868e79
commit f22f969d65
10 changed files with 98 additions and 55 deletions
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1
pyproject.toml
View file

@ -54,7 +54,6 @@ build-backend = "poetry.core.masonry.api"
[tool.black]
line-length = 120
preview = true
[tool.ruff]
ignore = [

15
src/refiners/fluxion/layers/chain.py
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@ -292,13 +292,16 @@ class Chain(ContextModule):
return Chain(*self, *other)
@overload
def __getitem__(self, key: int) -> Module: ...
def __getitem__(self, key: int) -> Module:
...
@overload
def __getitem__(self, key: str) -> Module: ...
def __getitem__(self, key: str) -> Module:
...
@overload
def __getitem__(self, key: slice) -> "Chain": ...
def __getitem__(self, key: slice) -> "Chain":
...
def __getitem__(self, key: int | str | slice) -> Module:
if isinstance(key, slice):
@ -346,10 +349,12 @@ class Chain(ContextModule):
@overload
def walk(
self, predicate: Callable[[Module, "Chain"], bool] | None = None, recurse: bool = False
) -> Iterator[tuple[Module, "Chain"]]: ...
) -> Iterator[tuple[Module, "Chain"]]:
...
@overload
def walk(self, predicate: type[T], recurse: bool = False) -> Iterator[tuple[T, "Chain"]]: ...
def walk(self, predicate: type[T], recurse: bool = False) -> Iterator[tuple[T, "Chain"]]:
...
def walk(
self, predicate: type[T] | Callable[[Module, "Chain"], bool] | None = None, recurse: bool = False

12
src/refiners/foundationals/latent_diffusion/model.py
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@ -65,18 +65,22 @@ class LatentDiffusionModel(fl.Module, ABC):
return self.scheduler.steps
@abstractmethod
def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None: ...
def set_unet_context(self, *, timestep: Tensor, clip_text_embedding: Tensor, **_: Tensor) -> None:
...
@abstractmethod
def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None: ...
def set_self_attention_guidance(self, enable: bool, scale: float = 1.0) -> None:
...
@abstractmethod
def has_self_attention_guidance(self) -> bool: ...
def has_self_attention_guidance(self) -> bool:
...
@abstractmethod
def compute_self_attention_guidance(
self, x: Tensor, noise: Tensor, step: int, *, clip_text_embedding: Tensor, **kwargs: Tensor
) -> Tensor: ...
) -> Tensor:
...
def forward(
self, x: Tensor, step: int, *, clip_text_embedding: Tensor, condition_scale: float = 7.5, **kwargs: Tensor

3
src/refiners/foundationals/latent_diffusion/multi_diffusion.py
View file

@ -69,7 +69,8 @@ class MultiDiffusion(Generic[T, D], ABC):
return torch.where(condition=num_updates > 0, input=cumulative_values / num_updates, other=x)
@abstractmethod
def diffuse_target(self, x: Tensor, step: int, target: D) -> Tensor: ...
def diffuse_target(self, x: Tensor, step: int, target: D) -> Tensor:
...
@property
def steps(self) -> list[int]:

4
src/refiners/foundationals/latent_diffusion/schedulers/ddpm.py
View file

@ -56,7 +56,9 @@ class DDPM(Scheduler):
else tensor(1, device=self.device)
)
current_factor = current_cumulative_factor / previous_cumulative_scale_factor
estimated_denoised_data = (x - (1 - current_cumulative_factor) ** 0.5 * noise) / current_cumulative_factor**0.5
estimated_denoised_data = (
x - (1 - current_cumulative_factor) ** 0.5 * noise
) / current_cumulative_factor**0.5
estimated_denoised_data = estimated_denoised_data.clamp(-1, 1)
original_data_coeff = (previous_cumulative_scale_factor**0.5 * (1 - current_factor)) / (
1 - current_cumulative_factor

79
src/refiners/training_utils/callback.py
View file

@ -42,59 +42,82 @@ T = TypeVar("T")
class Callback(Generic[T]):
def on_train_begin(self, trainer: T) -> None: ...
def on_train_begin(self, trainer: T) -> None:
...
def on_train_end(self, trainer: T) -> None: ...
def on_train_end(self, trainer: T) -> None:
...
def on_epoch_begin(self, trainer: T) -> None: ...
def on_epoch_begin(self, trainer: T) -> None:
...
def on_epoch_end(self, trainer: T) -> None: ...
def on_epoch_end(self, trainer: T) -> None:
...
def on_batch_begin(self, trainer: T) -> None: ...
def on_batch_begin(self, trainer: T) -> None:
...
def on_batch_end(self, trainer: T) -> None: ...
def on_batch_end(self, trainer: T) -> None:
...
def on_backward_begin(self, trainer: T) -> None: ...
def on_backward_begin(self, trainer: T) -> None:
...
def on_backward_end(self, trainer: T) -> None: ...
def on_backward_end(self, trainer: T) -> None:
...
def on_optimizer_step_begin(self, trainer: T) -> None: ...
def on_optimizer_step_begin(self, trainer: T) -> None:
...
def on_optimizer_step_end(self, trainer: T) -> None: ...
def on_optimizer_step_end(self, trainer: T) -> None:
...
def on_compute_loss_begin(self, trainer: T) -> None: ...
def on_compute_loss_begin(self, trainer: T) -> None:
...
def on_compute_loss_end(self, trainer: T) -> None: ...
def on_compute_loss_end(self, trainer: T) -> None:
...
def on_evaluate_begin(self, trainer: T) -> None: ...
def on_evaluate_begin(self, trainer: T) -> None:
...
def on_evaluate_end(self, trainer: T) -> None: ...
def on_evaluate_end(self, trainer: T) -> None:
...
def on_lr_scheduler_step_begin(self, trainer: T) -> None: ...
def on_lr_scheduler_step_begin(self, trainer: T) -> None:
...
def on_lr_scheduler_step_end(self, trainer: T) -> None: ...
def on_lr_scheduler_step_end(self, trainer: T) -> None:
...
def on_checkpoint_save(self, trainer: T) -> None: ...
def on_checkpoint_save(self, trainer: T) -> None:
...
class ClockCallback(Callback["Trainer[BaseConfig, Any]"]):
def on_train_begin(self, trainer: "Trainer[BaseConfig, Any]") -> None:
trainer.clock.reset()
logger.info(f"""Starting training for a total of:
{trainer.clock.num_steps} steps.
{trainer.clock.num_epochs} epochs.
{trainer.clock.num_iterations} iterations.
""")
logger.info(
(
"Starting training for a total of: "
f"{trainer.clock.num_steps} steps, "
f"{trainer.clock.num_epochs} epochs, "
f"{trainer.clock.num_iterations} iterations."
)
)
trainer.clock.start_timer()
def on_train_end(self, trainer: "Trainer[BaseConfig, Any]") -> None:
trainer.clock.stop_timer()
logger.info(f"""Training took:
{trainer.clock.time_elapsed} seconds.
{trainer.clock.iteration} iterations.
{trainer.clock.epoch} epochs.
{trainer.clock.step} steps.
""")
logger.info(
(
"Training took: "
f"{trainer.clock.time_elapsed} seconds, "
f"{trainer.clock.iteration} iterations, "
f"{trainer.clock.epoch} epochs, "
f"{trainer.clock.step} steps."
)
)
def on_epoch_begin(self, trainer: "Trainer[BaseConfig, Any]") -> None:
logger.info(f"Epoch {trainer.clock.epoch} started.")

6
src/refiners/training_utils/huggingface_datasets.py
View file

@ -8,9 +8,11 @@ T = TypeVar("T", covariant=True)
class HuggingfaceDataset(Generic[T], Protocol):
def __getitem__(self, index: int) -> T: ...
def __getitem__(self, index: int) -> T:
...
def __len__(self) -> int: ...
def __len__(self) -> int:
...
def load_hf_dataset(

6
src/refiners/training_utils/trainer.py
View file

@ -147,13 +147,11 @@ class TrainingClock:
@cached_property
def unit_to_steps(self) -> dict[TimeUnit, int]:
iteration_factor = self.num_batches_per_epoch if self.gradient_accumulation["unit"] == TimeUnit.EPOCH else 1
return {
TimeUnit.STEP: 1,
TimeUnit.EPOCH: self.num_batches_per_epoch,
TimeUnit.ITERATION: self.gradient_accumulation["number"] * {
TimeUnit.STEP: 1,
TimeUnit.EPOCH: self.num_batches_per_epoch,
}.get(self.gradient_accumulation["unit"], 1),
TimeUnit.ITERATION: self.gradient_accumulation["number"] * iteration_factor,
}
def convert_time_unit_to_steps(self, number: int, unit: TimeUnit) -> int:

15
tests/foundationals/latent_diffusion/test_sdxl_double_encoder.py
View file

@ -18,7 +18,8 @@ class DiffusersSDXL(Protocol):
tokenizer_2: fl.Module
vae: fl.Module
def __call__(self, prompt: str, *args: Any, **kwargs: Any) -> Any: ...
def __call__(self, prompt: str, *args: Any, **kwargs: Any) -> Any:
...
def encode_prompt(
self,
@ -26,7 +27,8 @@ class DiffusersSDXL(Protocol):
prompt_2: str | None = None,
negative_prompt: str | None = None,
negative_prompt_2: str | None = None,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: ...
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
...
@pytest.fixture(scope="module")
@ -67,9 +69,12 @@ def test_double_text_encoder(diffusers_sdxl: DiffusersSDXL, double_text_encoder:
manual_seed(seed=0)
prompt = "A photo of a pizza."
prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = (
diffusers_sdxl.encode_prompt(prompt=prompt, negative_prompt="")
)
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = diffusers_sdxl.encode_prompt(prompt=prompt, negative_prompt="")
double_embedding, pooled_embedding = double_text_encoder(prompt)

12
tests/foundationals/segment_anything/utils.py
View file

@ -32,16 +32,19 @@ class FacebookSAM(nn.Module):
prompt_encoder: nn.Module
mask_decoder: nn.Module
def __call__(self, batched_input: list[SAMInput], multimask_output: bool) -> list[SAMOutput]: ...
def __call__(self, batched_input: list[SAMInput], multimask_output: bool) -> list[SAMOutput]:
...
@property
def device(self) -> Any: ...
def device(self) -> Any:
...
class FacebookSAMPredictor:
model: FacebookSAM
def set_image(self, image: NDArrayUInt8, image_format: str = "RGB") -> None: ...
def set_image(self, image: NDArrayUInt8, image_format: str = "RGB") -> None:
...
def predict(
self,
@ -51,7 +54,8 @@ class FacebookSAMPredictor:
mask_input: NDArray | None = None,
multimask_output: bool = True,
return_logits: bool = False,
) -> tuple[NDArray, NDArray, NDArray]: ...
) -> tuple[NDArray, NDArray, NDArray]:
...
@dataclass