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compare-allids-embeds.py

@@ -0,0 +1,56 @@
+#!/bin/env python
+
+""" Work in progress
+    temp utility.
+    Load in two pre-calculated embeddings files.
+    (eg: *.allid.*)
+
+    Go through the full range and calculate distances between each.
+
+    Add up and display
+
+    This covers the full official range of tokenids,
+    0-49405
+
+"""
+
+
+import sys
+import torch
+from safetensors import safe_open
+
+file1=sys.argv[1]
+file2=sys.argv[2]
+
+
+
+device=torch.device("cuda")
+print(f"reading {file1} embeddings now",file=sys.stderr)
+model = safe_open(file1,framework="pt",device="cuda")
+embs1=model.get_tensor("embeddings")
+embs1.to(device)
+print("Shape of loaded embeds =",embs1.shape)
+
+print(f"reading {file2} embeddings now",file=sys.stderr)
+model = safe_open(file2,framework="pt",device="cuda")
+embs2=model.get_tensor("embeddings")
+embs2.to(device)
+print("Shape of loaded embeds =",embs2.shape)
+
+if torch.equal(embs1 , embs2):
+    print("HEY! Both files are identical!")
+    exit(0)
+
+print(f"calculating distances...")
+
+# This calculates a full cross matrix of ALL distances to ALL other points
+# in other tensor
+##targetdistances = torch.cdist( embs1,embs2, p=2)
+
+
+targetdistances = torch.norm(embs2 - embs1, dim=1)
+print(targetdistances.shape) 
+tl=targetdistances.tolist()
+
+print(tl[:10])
+

generate-allid-embeddings.py

@@ -1,11 +1,31 @@
 #!/bin/env python
 
-""" Work in progress
-Plan:
-   Similar to generate-embeddings.py
-   However, instead of reading from a dictionary, just generate by pure
-   numeric tokenID
-   Save it out
+"""
+    Take a CLIPTextModel compatible text encoder.
+    Go through the official range of tokens IDs (0-49405)
+    Generate the official "embedding" tensor for each one.
+    Save the result set to "temp.allids.safetensors"
+
+    Defaults to loading openai/clip-vit-large-patch14 from huggingface hub.
+    However, can take optional pair of arguments to a .safetensor model, and config file
+    RULES of the loader:
+    1. the model file must appear to be either in current directory or one down. So,
+      badpath1=some/directory/tree/file.here
+      badpath2=/absolutepath
+    2. yes, you MUST have a matching config.json file
+    3. if you have no alternative, you can get away with using pytorch_model.bin
+
+    Sample location for such things that you can download:
+        https://huggingface.co/stablediffusionapi/edge-of-realism/tree/main/text_encoder/
+    If there is a .safetensors AND a .bin file, ignore the .bin file
+
+    You can also convert a singlefile model, such as is downloaded from civitai,
+    by using the utility at
+        https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py
+    Args should look like
+        convert_original_stable_diffusion_to_diffusers.py --checkpoint_file somemodel.safetensors \
+            --dump_path extractdir --to_safetensors --from_safetensors
+    
 """
 
 
@@ -13,11 +33,17 @@ import sys
 import json
 import torch
 from safetensors.torch import save_file
-from transformers import CLIPProcessor,CLIPModel
+from transformers import CLIPProcessor,CLIPModel,CLIPTextModel
 
 clipsrc="openai/clip-vit-large-patch14"
 processor=None
 model=None
+encfile=None
+configfile=None
+
+if len(sys.argv) == 3:
+    encfile=sys.argv[1]
+    configfile=sys.argv[2]
 
 device=torch.device("cuda")
 
@@ -25,22 +51,42 @@ device=torch.device("cuda")
 def init():
     global processor
     global model
+    global encfile
+    global configfile
+
     # Load the processor and model
     print("loading processor from "+clipsrc,file=sys.stderr)
     processor = CLIPProcessor.from_pretrained(clipsrc)
     print("done",file=sys.stderr)
-    print("loading model from "+clipsrc,file=sys.stderr)
-    model = CLIPModel.from_pretrained(clipsrc)
+
+    # originally done this way. But its not the right one to use
+    #print("loading model from "+clipsrc,file=sys.stderr)
+    #model = CLIPModel.from_pretrained(clipsrc)
+    #print("done",file=sys.stderr)
+    if encfile != None:
+        print("loading model from "+encfile,file=sys.stderr)
+        model = CLIPTextModel.from_pretrained(
+                encfile,config=configfile,local_files_only=True,use_safetensors=True
+                )
+    else:
+        print("loading model from "+clipsrc,file=sys.stderr)
+        model = CLIPTextModel.from_pretrained(clipsrc)
+
     print("done",file=sys.stderr)
 
     model = model.to(device)
 
 
-
+# "inputs" == magic pre-embedding format
 def embed_from_inputs(inputs):
     with torch.no_grad():
-        text_features = model.get_text_features(**inputs)
-    embedding = text_features[0]
+        # This way is for CLIPModel
+        #text_features = model.get_text_features(**inputs)
+        #embedding = text_features[0]
+
+        outputs = model(**inputs)
+        embeddings = outputs.pooler_output
+        embedding = embeddings
 
     return embedding
 
@@ -55,14 +101,15 @@ for id in range(49405):
     inputs.input_ids[0][1]=id
 
     emb=embed_from_inputs(inputs)
-    emb=emb.unsqueeze(0) # stupid matrix magic to make the cat work
     all_embeddings.append(emb)
     if (id %100) ==0:
         print(id)
 
 embs = torch.cat(all_embeddings,dim=0)
 print("Shape of result = ",embs.shape)
-print("Saving all the things...")
-save_file({"embeddings": embs}, "embeddings.safetensors")
+
+outputfile="cliptextmodel.temp.allids.safetensors"
+print(f"Saving the calculatiuons to {outputfile}...")
+save_file({"embeddings": embs}, outputfile)