Merge pull request #14478 from akx/dtype-inspect

Add utility to inspect a model's dtype/device

modules/devices.py

@@ -4,6 +4,7 @@ from functools import lru_cache
 
 import torch
 from modules import errors, shared
+from modules.torch_utils import get_param
 
 if sys.platform == "darwin":
     from modules import mac_specific
@@ -131,7 +132,7 @@ patch_module_list = [
 
 
 def manual_cast_forward(self, *args, **kwargs):
-    org_dtype = next(self.parameters()).dtype
+    org_dtype = get_param(self).dtype
     self.to(dtype)
     args = [arg.to(dtype) if isinstance(arg, torch.Tensor) else arg for arg in args]
     kwargs = {k: v.to(dtype) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()}

modules/interrogate.py

@@ -11,6 +11,7 @@ from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 
 from modules import devices, paths, shared, lowvram, modelloader, errors
+from modules.torch_utils import get_param
 
 blip_image_eval_size = 384
 clip_model_name = 'ViT-L/14'
@@ -131,7 +132,7 @@ class InterrogateModels:
 
         self.clip_model = self.clip_model.to(devices.device_interrogate)
 
-        self.dtype = next(self.clip_model.parameters()).dtype
+        self.dtype = get_param(self.clip_model).dtype
 
     def send_clip_to_ram(self):
         if not shared.opts.interrogate_keep_models_in_memory:

modules/sd_models_xl.py

@@ -6,6 +6,7 @@ import sgm.models.diffusion
 import sgm.modules.diffusionmodules.denoiser_scaling
 import sgm.modules.diffusionmodules.discretizer
 from modules import devices, shared, prompt_parser
+from modules.torch_utils import get_param
 
 
 def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: prompt_parser.SdConditioning | list[str]):
@@ -90,7 +91,7 @@ sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt
 def extend_sdxl(model):
     """this adds a bunch of parameters to make SDXL model look a bit more like SD1.5 to the rest of the codebase."""
 
-    dtype = next(model.model.diffusion_model.parameters()).dtype
+    dtype = get_param(model.model.diffusion_model).dtype
     model.model.diffusion_model.dtype = dtype
     model.model.conditioning_key = 'crossattn'
     model.cond_stage_key = 'txt'

modules/torch_utils.py

+from __future__ import annotations
+
+import torch.nn
+
+
+def get_param(model) -> torch.nn.Parameter:
+    """
+    Find the first parameter in a model or module.
+    """
+    if hasattr(model, "model") and hasattr(model.model, "parameters"):
+        # Unpeel a model descriptor to get at the actual Torch module.
+        model = model.model
+
+    for param in model.parameters():
+        return param
+
+    raise ValueError(f"No parameters found in model {model!r}")

modules/upscaler_utils.py

@@ -7,6 +7,7 @@ import tqdm
 from PIL import Image
 
 from modules import images, shared
+from modules.torch_utils import get_param
 
 logger = logging.getLogger(__name__)
 
@@ -17,8 +18,8 @@ def upscale_without_tiling(model, img: Image.Image):
     img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
     img = torch.from_numpy(img).float()
 
-    model_weight = next(iter(model.model.parameters()))
-    img = img.unsqueeze(0).to(device=model_weight.device, dtype=model_weight.dtype)
+    param = get_param(model)
+    img = img.unsqueeze(0).to(device=param.device, dtype=param.dtype)
 
     with torch.no_grad():
         output = model(img)

modules/xlmr.py

@@ -5,6 +5,9 @@ from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRoberta
 from transformers import XLMRobertaModel,XLMRobertaTokenizer
 from typing import Optional
 
+from modules.torch_utils import get_param
+
+
 class BertSeriesConfig(BertConfig):
     def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs):
 
@@ -62,7 +65,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel):
         self.post_init()
 
     def encode(self,c):
-        device = next(self.parameters()).device
+        device = get_param(self).device
         text = self.tokenizer(c,
                         truncation=True,
                         max_length=77,

modules/xlmr_m18.py

@@ -5,6 +5,9 @@ from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRoberta
 from transformers import XLMRobertaModel,XLMRobertaTokenizer
 from typing import Optional
 
+from modules.torch_utils import get_param
+
+
 class BertSeriesConfig(BertConfig):
     def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs):
 
@@ -68,7 +71,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel):
         self.post_init()
 
     def encode(self,c):
-        device = next(self.parameters()).device
+        device = get_param(self).device
         text = self.tokenizer(c,
                         truncation=True,
                         max_length=77,

test/test_torch_utils.py

+import types
+
+import pytest
+import torch
+
+from modules.torch_utils import get_param
+
+
+@pytest.mark.parametrize("wrapped", [True, False])
+def test_get_param(wrapped):
+    mod = torch.nn.Linear(1, 1)
+    cpu = torch.device("cpu")
+    mod.to(dtype=torch.float16, device=cpu)
+    if wrapped:
+        # more or less how spandrel wraps a thing
+        mod = types.SimpleNamespace(model=mod)
+    p = get_param(mod)
+    assert p.dtype == torch.float16
+    assert p.device == cpu