Upload T5/generate-dict-embeddingsT5.py with huggingface_hub

T5/generate-dict-embeddingsT5.py

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+#!/bin/env python
+
+"""
+    (T5 counterpart of "generate-dict-embeddingsXL.py".
+"""
+
+outputfile="embeddingsT5.temp.safetensors"
+
+import sys
+import torch
+from safetensors.torch import save_file
+from transformers import T5Tokenizer,T5EncoderModel
+
+processor=None
+tmodel=None
+
+device=torch.device("cuda")
+
+def initT5model():
+    global processor,tmodel
+    T="mcmonkey/google_t5-v1_1-xxl_encoderonly"
+    processor = T5Tokenizer.from_pretrained(T)
+    tmodel = T5EncoderModel.from_pretrained(T).to(device)
+
+
+def embed_from_text(text):
+    global processor,tmodel
+    #print("Word:"+text)
+    tokens = processor(text, return_tensors="pt")
+    tokens.to(device)
+
+    if len(tokens.input_ids) >2:
+        print("ERROR: expected single token per word")
+        print(text)
+        exit(1)
+
+    with torch.no_grad():
+        outputs = tmodel(tokens.input_ids)
+
+    embedding = outputs.last_hidden_state[0][0]
+    #print(encoding.shape)
+    # Shape of this is (1,2,4096)
+    
+    return embedding
+
+initT5model()
+
+print("Reading in 'dictionary'")
+with open("dictionary","r") as f:
+    tokendict = f.readlines()
+    tokendict = [token.strip() for token in tokendict]  # Remove trailing newlines
+
+
+count=1
+all_embeddings = []
+
+for word in tokendict:
+    emb = embed_from_text(word)
+    #emb=emb.unsqueeze(0) # stupid matrix magic to make torch.cat work
+    all_embeddings.append(emb)
+    count+=1
+    if (count %100) ==0:
+        print(count)
+
+
+embs = torch.cat(all_embeddings,dim=0)
+print("Shape of result = ",embs.shape)
+
+if len(embs.shape) != 2:
+    print("Sanity check: result is wrong shape: it wont work")
+
+print(f"Saving the calculatiuons to {outputfile}...")
+save_file({"embeddings": embs}, outputfile)
+