turn CLIPTokenizer into a fl.Module

scripts/convert-clip-weights.py

@@ -6,13 +6,16 @@ from diffusers import DiffusionPipeline  # type: ignore
 from transformers.models.clip.modeling_clip import CLIPTextModel  # type: ignore
 
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
+from refiners.foundationals.clip.tokenizer import CLIPTokenizer
 
 
 @torch.no_grad()
 def convert(src_model: CLIPTextModel) -> dict[str, torch.Tensor]:
     dst_model = CLIPTextEncoderL()
-    x = dst_model.tokenizer("Nice cat", sequence_length=77)
+    tokenizer = dst_model.find(layer_type=CLIPTokenizer)
-    mapping = create_state_dict_mapping(source_model=src_model, target_model=dst_model, source_args=[x])  # type: ignore
+    assert tokenizer is not None, "Could not find tokenizer"
+    tokens = tokenizer("Nice cat")
+    mapping = create_state_dict_mapping(source_model=src_model, target_model=dst_model, source_args=[tokens], target_args=["Nice cat"])  # type: ignore
     assert mapping is not None, "Model conversion failed"
     state_dict = convert_state_dict(
         source_state_dict=src_model.state_dict(), target_state_dict=dst_model.state_dict(), state_dict_mapping=mapping

scripts/convert-loras-to-sdwebui.py

@@ -4,6 +4,7 @@ from refiners.fluxion.utils import (
     save_to_safetensors,
 )
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
+from refiners.foundationals.clip.tokenizer import CLIPTokenizer
 from refiners.foundationals.latent_diffusion.unet import UNet
 from refiners.foundationals.latent_diffusion.lora import LoraTarget
 from refiners.fluxion.layers.module import Module
@@ -33,9 +34,10 @@ def create_unet_mapping(src_model: UNet2DConditionModel, dst_model: UNet) -> dic
 
 @torch.no_grad()
 def create_text_encoder_mapping(src_model: CLIPTextModel, dst_model: CLIPTextEncoderL) -> dict[str, str] | None:
-    x = dst_model.tokenizer("Nice cat", sequence_length=77)
+    tokenizer = dst_model.find(layer_type=CLIPTokenizer)
-
+    assert tokenizer is not None, "Could not find tokenizer"
-    return create_state_dict_mapping(source_model=src_model, target_model=dst_model, source_args=[x])  # type: ignore
+    tokens = tokenizer("Nice cat")
+    return create_state_dict_mapping(source_model=src_model, target_model=dst_model, source_args=[tokens], target_args=["Nice cat"])  # type: ignore
 
 
 def main() -> None:

scripts/convert-sdxl-text-encoder-2.py

@@ -6,6 +6,7 @@ from refiners.fluxion.utils import create_state_dict_mapping, convert_state_dict
 from diffusers import DiffusionPipeline  # type: ignore
 from transformers.models.clip.modeling_clip import CLIPTextModel  # type: ignore
 
+from refiners.foundationals.clip.tokenizer import CLIPTokenizer
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderG
 import refiners.fluxion.layers as fl
 
@@ -15,8 +16,10 @@ def convert(src_model: CLIPTextModel) -> dict[str, torch.Tensor]:
     dst_model = CLIPTextEncoderG()
     # Extra projection layer (see CLIPTextModelWithProjection in transformers)
     dst_model.append(module=fl.Linear(in_features=1280, out_features=1280, bias=False))
-    x = dst_model.tokenizer("Nice cat", sequence_length=77)
+    tokenizer = dst_model.find(layer_type=CLIPTokenizer)
-    mapping = create_state_dict_mapping(source_model=src_model, target_model=dst_model, source_args=[x])  # type: ignore
+    assert tokenizer is not None, "Could not find tokenizer"
+    tokens = tokenizer("Nice cat")
+    mapping = create_state_dict_mapping(source_model=src_model, target_model=dst_model, source_args=[tokens], target_args=["Nice cat"])  # type: ignore
     if mapping is None:
         raise RuntimeError("Could not create state dict mapping")
     state_dict = convert_state_dict(

src/refiners/foundationals/clip/text_encoder.py

@@ -131,7 +131,6 @@ class CLIPTextEncoder(fl.Chain):
         "feedforward_dim",
         "layer_norm_eps",
         "use_quick_gelu",
-        "tokenizer",
     ]
 
     def __init__(
@@ -156,8 +155,9 @@ class CLIPTextEncoder(fl.Chain):
         self.feedforward_dim = feedforward_dim
         self.layer_norm_eps = layer_norm_eps
         self.use_quick_gelu = use_quick_gelu
-        self.tokenizer = tokenizer or CLIPTokenizer()
         super().__init__(
+            tokenizer or CLIPTokenizer(sequence_length=max_sequence_length),
+            fl.Converter(set_dtype=False),
             fl.Sum(
                 TokenEncoder(
                     vocabulary_size=vocabulary_size,
@@ -189,13 +189,9 @@ class CLIPTextEncoder(fl.Chain):
             for gelu, parent in self.walk(predicate=lambda m, _: isinstance(m, fl.GeLU)):
                 parent.replace(old_module=gelu, new_module=fl.ApproximateGeLU())
 
-    def encode(self, text: str) -> Tensor:
-        tokens = self.tokenizer(text, sequence_length=self.max_sequence_length).to(device=self.device)
-        return self(tokens)
-
     @property
     def unconditional_text_embedding(self) -> Tensor:
-        return self.encode(text="")
+        return self("")
 
 
 class CLIPTextEncoderL(CLIPTextEncoder):

src/refiners/foundationals/clip/tokenizer.py

@@ -5,17 +5,21 @@ from itertools import islice
 import re
 from torch import Tensor, tensor
 from refiners.fluxion import pad
+import refiners.fluxion.layers as fl
 
 
-class CLIPTokenizer:
+class CLIPTokenizer(fl.Module):
     def __init__(
         self,
         vocabulary_path: str | Path = Path(__file__).resolve().parent / "bpe_simple_vocab_16e6.txt.gz",
+        sequence_length: int = 77,
         start_of_text_token_id: int = 49406,
         end_of_text_token_id: int = 49407,
         pad_token_id: int = 49407,
     ) -> None:
+        super().__init__()
         self.vocabulary_path = vocabulary_path
+        self.sequence_length = sequence_length
         self.byte_to_unicode_mapping = self.get_bytes_to_unicode_mapping()
         self.byte_decoder = {v: k for k, v in self.byte_to_unicode_mapping.items()}
         merge_tuples = [
@@ -45,12 +49,12 @@ class CLIPTokenizer:
         self.end_of_text_token_id: int = end_of_text_token_id
         self.pad_token_id: int = pad_token_id
 
-    def __call__(self, text: str, sequence_length: int) -> Tensor:
+    def forward(self, text: str) -> Tensor:
-        tokens = self.encode(text=text, max_length=sequence_length).unsqueeze(dim=0)
+        tokens = self.encode(text=text, max_length=self.sequence_length).unsqueeze(dim=0)
         assert (
-            tokens.shape[1] <= sequence_length
+            tokens.shape[1] <= self.sequence_length
-        ), f"Text is too long: tokens.shape[1] > sequence_length: {tokens.shape[1]} > {sequence_length}"
+        ), f"Text is too long: tokens.shape[1] > sequence_length: {tokens.shape[1]} > {self.sequence_length}"
-        return pad(x=tokens, pad=(0, sequence_length - tokens.shape[1]), value=self.pad_token_id)
+        return pad(x=tokens, pad=(0, self.sequence_length - tokens.shape[1]), value=self.pad_token_id)
 
     @lru_cache()
     def get_bytes_to_unicode_mapping(self) -> dict[int, str]:

src/refiners/foundationals/latent_diffusion/__init__.py

@@ -88,7 +88,7 @@ class LatentDiffusionModel(Module):
         return self.clip_text_encoder.unconditional_text_embedding
 
     def compute_text_embedding(self, text: str) -> Tensor:
-        return self.clip_text_encoder.encode(text)
+        return self.clip_text_encoder(text)
 
     def forward(
         self,

src/refiners/training_utils/latent_diffusion.py

@@ -89,7 +89,7 @@ class TextEmbeddingLatentsDataset(Dataset[TextEmbeddingLatentsBatch]):
         processed_image: Image.Image = self.process_image(resized_image)
         latents = self.lda.encode_image(image=processed_image).to(device=self.device)
         processed_caption = self.process_caption(caption=caption)
-        clip_text_embedding = self.text_encoder.encode(text=processed_caption).to(device=self.device)
+        clip_text_embedding = self.text_encoder(processed_caption).to(device=self.device)
         return TextEmbeddingLatentsBatch(text_embeddings=clip_text_embedding, latents=latents)
 
     def collate_fn(self, batch: list[TextEmbeddingLatentsBatch]) -> TextEmbeddingLatentsBatch:

tests/foundationals/clip/test_text_encoder.py

@@ -7,7 +7,8 @@ from pathlib import Path
 from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
 from refiners.fluxion.utils import load_from_safetensors
 
-import transformers  # type: ignore
+import transformers # type: ignore
+from refiners.foundationals.clip.tokenizer import CLIPTokenizer
 
 
 long_prompt = """
@@ -86,12 +87,14 @@ def test_encoder(
         return_tensors="pt",
     ).input_ids
     assert isinstance(ref_tokens, torch.Tensor)
-    our_tokens = our_encoder.tokenizer(prompt, sequence_length=our_encoder.max_sequence_length)
+    tokenizer = our_encoder.find(layer_type=CLIPTokenizer)
+    assert tokenizer is not None
+    our_tokens = tokenizer(prompt)
     assert torch.equal(our_tokens, ref_tokens)
 
     with torch.no_grad():
         ref_embeddings = ref_encoder(ref_tokens.to(test_device))[0]
-        our_embeddings = our_encoder(our_tokens.to(test_device))
+        our_embeddings = our_encoder(prompt)
 
     assert ref_embeddings.shape == (1, 77, 768)
     assert our_embeddings.shape == (1, 77, 768)