Upload benchmark.py

benchmark.py

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+########################################################################################################
+# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
+########################################################################################################
+
+print('\nHere are some demos for RWKV-4-World models (https://huggingface.co/BlinkDL/rwkv-4-world)\n')
+
+import os, re
+import json
+
+os.environ['RWKV_JIT_ON'] = '0' #### set these before import RWKV
+os.environ["RWKV_CUDA_ON"] = '0' #### set to '1' to compile CUDA kernel (10x faster) - requires c++ compiler & cuda libraries
+
+from rwkv.model import RWKV #### pip install rwkv --upgrade
+from rwkv.utils import PIPELINE, PIPELINE_ARGS
+
+MODEL_FILE = '../../RWKV-5-World-3B-v2-20231113-ctx4096'
+
+model = RWKV(model=MODEL_FILE, strategy='cuda bf16')
+pipeline = PIPELINE(model, "rwkv_vocab_v20230424") #### vocab for rwkv-4-world models
+
+
+def my_qa_generator(ctx,length):
+    out_tokens = []
+    out_len = 0
+    out_str = ''
+    occurrence = {}
+    state = None
+    for i in range(length):
+
+        if i == 0:
+            out, state = pipeline.model.forward(pipeline.encode(ctx), state)
+        else:
+            out, state = pipeline.model.forward([token], state)
+
+        for n in occurrence: out[n] -= (0.4 + occurrence[n] * 0.4) #### higher repetition penalty because of lower top_p here
+        
+        token = pipeline.sample_logits(out, temperature=1.0, top_p=0.2) #### sample the next token
+
+        if token == 0: break #### exit at token [0] = <|endoftext|>
+        
+        out_tokens += [token]
+
+        for n in occurrence: occurrence[n] *= 0.996 #### decay repetition penalty
+        occurrence[token] = 1 + (occurrence[token] if token in occurrence else 0)
+        
+        tmp = pipeline.decode(out_tokens[out_len:])
+        if ('\ufffd' not in tmp) and (not tmp.endswith('\n')): #### print() only when out_str is valid utf-8 and not end with \n
+            out_str += tmp
+            #print(tmp, end = '', flush = True)
+            out_len = i + 1    
+        elif '\n\n' in tmp: #### exit at '\n\n'
+            tmp = tmp.rstrip()
+            out_str += tmp
+            #print(tmp, end = '', flush = True)
+            break
+    return out_str.strip()
+
+
+def bench():
+
+    data = json.load(open('heval_v1.json','r',encoding='utf-8'))
+    yes = 0
+    for i,q in enumerate(data):
+        question = q['question']
+        ctx = my_qa_generator(question,6)
+        #ctx = tokenizer.tokenizer.decode(ctx)
+        flag=False
+        for ans in q['answer']:
+            if ctx[:len(ans)] == ans:
+                yes+=1
+                flag=True
+                print(i,yes,len(data),yes/(i+1))
+      
+    print('Score : ',yes/len(data)*100)
+
+
+bench()
+