make device and dtype work in Trainer class

configs/finetune-ldm.toml

@@ -1,53 +0,0 @@
-[wandb]
-mode = "offline"
-entity = "acme"
-project = "test-ldm-training"
-
-[models]
-lda = {checkpoint="/path/to/stable-diffusion-1-5/lda.safetensors", train=false}
-text_encoder = {checkpoint="/path/to/stable-diffusion-1-5/text_encoder.safetensors", train=true}
-unet = {checkpoint="/path/to/stable-diffusion-1-5/unet.safetensors", train=true}
-
-[latent_diffusion]
-unconditional_sampling_probability = 0.2
-offset_noise = 0.1
-
-[training]
-duration = "1:epoch"
-seed = 0
-gpu_index = 0
-num_epochs = 1
-batch_size = 1
-gradient_accumulation = "1:step"
-clip_grad_norm = 2.0
-clip_grad_value = 1.0
-evaluation_interval = "1:epoch"
-evaluation_seed = 0
-
-
-[optimizer]
-optimizer = "AdamW" # "AdamW", "AdamW8bit", "Lion8bit", "Prodigy", "SGD", "Adam"
-learning_rate = 1e-5
-betas = [0.9, 0.999]
-eps = 1e-8
-weight_decay = 1e-2
-
-
-[scheduler]
-
-
-[dropout]
-dropout_probability = 0.2
-
-[dataset]
-hf_repo = "acme/images"
-revision = "main"
-
-[checkpointing]
-# save_folder = "/path/to/ckpts"
-save_interval = "1:epoch"
-
-[test_diffusion]
-prompts = [
-    "A cute cat",
-]

configs/finetune-textual-inversion.toml

@@ -1,61 +0,0 @@
-[wandb]
-mode = "offline" # "online", "offline", "disabled"
-entity = "acme"
-project = "test-textual-inversion"
-
-[models]
-unet = {checkpoint = "/path/to/stable-diffusion-1-5/unet.safetensors"}
-text_encoder = {checkpoint = "/path/to/stable-diffusion-1-5/CLIPTextEncoderL.safetensors"}
-lda = {checkpoint = "/path/to/stable-diffusion-1-5/lda.safetensors"}
-
-[latent_diffusion]
-unconditional_sampling_probability = 0.05
-offset_noise = 0.1
-
-[textual_inversion]
-placeholder_token = "<cat-toy>"
-initializer_token = "toy"
-# style_mode = true
-
-[training]
-duration = "2000:step"
-seed = 0
-gpu_index = 0
-batch_size = 4
-gradient_accumulation = "1:step"
-evaluation_interval = "250:step"
-evaluation_seed = 1
-
-[optimizer]
-optimizer = "AdamW" # "SGD", "Adam", "AdamW", "AdamW8bit", "Lion8bit"
-learning_rate = 5e-4
-betas = [0.9, 0.999]
-eps = 1e-8
-weight_decay = 1e-2
-
-[scheduler]
-scheduler_type = "ConstantLR"
-update_interval = "1:step"
-
-[dropout]
-dropout_probability = 0
-use_gyro_dropout = false
-
-[dataset]
-hf_repo = "acme/images"
-revision = "main"
-horizontal_flip = true
-random_crop = true
-resize_image_max_size = 512
-
-[checkpointing]
-# save_folder = "/path/to/ckpts"
-save_interval = "250:step"
-
-[test_diffusion]
-num_inference_steps = 30
-use_short_prompts = false
-prompts = [
-    "<cat-toy>",
-    # "green grass, <cat-toy>"
-]

src/refiners/training_utils/config.py

@@ -50,9 +50,10 @@ def parse_number_unit_field(value: str | int | dict[str, str | int]) -> TimeValu
 
 
 class TrainingConfig(BaseModel):
+    device: str = "cpu"
+    dtype: str = "float32"
     duration: TimeValue = {"number": 1, "unit": TimeUnit.ITERATION}
     seed: int = 0
-    gpu_index: int = 0
     batch_size: int = 1
     gradient_accumulation: TimeValue = {"number": 1, "unit": TimeUnit.STEP}
     clip_grad_norm: float | None = None

src/refiners/training_utils/trainer.py

@@ -6,8 +6,9 @@ from pathlib import Path
 from typing import Any, Callable, Generic, Iterable, TypeVar, cast
 
 import numpy as np
+import torch
 from loguru import logger
-from torch import Tensor, cuda, device as Device, get_rng_state, set_rng_state, stack
+from torch import Tensor, cuda, device as Device, dtype as DType, get_rng_state, set_rng_state, stack
 from torch.autograd import backward
 from torch.nn import Parameter
 from torch.optim import Optimizer
@@ -298,10 +299,17 @@ class Trainer(Generic[ConfigType, Batch], ABC):
 
     @cached_property
     def device(self) -> Device:
-        selected_device = Device(device=f"cuda:{self.config.training.gpu_index}")
+        selected_device = Device(self.config.training.device)
         logger.info(f"Using device: {selected_device}")
         return selected_device
 
+    @cached_property
+    def dtype(self) -> DType:
+        dtype = getattr(torch, self.config.training.dtype, None)
+        assert isinstance(dtype, DType), f"Unknown dtype: {self.config.training.dtype}"
+        logger.info(f"Using dtype: {dtype}")
+        return dtype
+
     @property
     def parameters(self) -> list[Parameter]:
         """Returns a list of all parameters in all models"""

tests/training_utils/mock_config.toml

@@ -4,7 +4,8 @@ train = true
 [training]
 duration = "100:epoch"
 seed = 0
-gpu_index = 0
+device = "cpu"
+dtype = "float32"
 batch_size = 4
 gradient_accumulation = "4:step"
 clip_grad_norm = 1.0

tests/training_utils/test_trainer.py

@@ -2,7 +2,6 @@ from dataclasses import dataclass
 from functools import cached_property
 from pathlib import Path
 from typing import cast
-from warnings import warn
 
 import pytest
 import torch
@@ -76,12 +75,8 @@ class MockTrainer(Trainer[MockConfig, MockBatch]):
 
 
 @pytest.fixture
-def mock_config(test_device: torch.device) -> MockConfig:
-    if not test_device.type == "cuda":
-        warn("only running on CUDA, skipping")
-        pytest.skip("Skipping test because test_device is not CUDA")
+def mock_config() -> MockConfig:
     config = MockConfig.load_from_toml(Path(__file__).parent / "mock_config.toml")
-    config.training.gpu_index = test_device.index
     return config