Merge pull request #1 from LorenzoMinto/master

Return score from continuation logprobs

lm_eval/models/ggml.py

@@ -10,6 +10,27 @@ from lm_eval.base import BaseLM
 logger = logging.getLogger(__name__)
 
 
+def get_result(logprobs, context_lenght):
+    is_greedy = True
+    offsets = logprobs['text_offset']
+    tokens = logprobs['tokens']
+    tokens_logprobs = logprobs['token_logprobs']
+
+    idx = 0
+    while offsets[idx] < context_lenght:
+        idx += 1
+    continuation_logprobs = sum(tokens_logprobs[idx:-1])
+    for i in range(idx, len(tokens)):
+        token = tokens[i]
+        top_tokens = logprobs["top_logprobs"][i]
+        top_token = max(top_tokens.keys(), key=lambda x: top_tokens[x])
+        if top_token != token:
+            is_greedy = False
+            break
+
+    return continuation_logprobs, is_greedy
+
+
 class GGMLLM(BaseLM):
     def __init__(self, base_url, truncate=False):
         super().__init__()
@@ -17,6 +38,7 @@ class GGMLLM(BaseLM):
         self.truncate = truncate
         self.tokenizer = transformers.GPT2TokenizerFast.from_pretrained("gpt2")
         self.logpobs = 10
+        self.temperature = 0.0
         self.max_length = 1024
         self.vocab_size = self.tokenizer.vocab_size
 
@@ -24,9 +46,11 @@ class GGMLLM(BaseLM):
         for _ in range(retries):
             try:
                 prompt = context
+                request = {'prompt': prompt, 'logprobs': self.logpobs,
+                           'temperature': self.temperature}
                 if continuation:
                     prompt += continuation
-                request = {'prompt': prompt, 'logprobs': self.logpobs}
+                    request.update({'prompt': prompt, 'max_tokens': 1, 'echo': True})
                 if stop is not None:
                     request['stop'] = stop
                 response = requests.post(f"{self.base_url}/v1/completions", json=request)
@@ -38,7 +62,6 @@ class GGMLLM(BaseLM):
         else:
             raise Exception(f"Failed to get a valid response after {retries} retries.")
 
-
     def loglikelihood(self, requests):
         if not requests:
             return []
@@ -49,8 +72,7 @@ class GGMLLM(BaseLM):
                 choice = response["choices"][0]
                 logprobs = choice.get("logprobs")
                 if logprobs and "token_logprobs" in logprobs and logprobs["token_logprobs"]:
-                    logprob = logprobs["token_logprobs"][0]
-                    is_greedy = choice["finish_reason"] == "length"
+                    logprob, is_greedy = get_result(logprobs, len(context))
                     res.append((logprob, is_greedy))
                 else:
                     logger.warning("Invalid logprobs data. Expected 'logprobs' to contain 'token_logprobs' list.")
@@ -58,7 +80,6 @@ class GGMLLM(BaseLM):
                 logger.error(f"Invalid response for loglikelihood. Response: {response}")
                 assert False
         return res
-    
 
     def greedy_until(self, requests):
         if not requests:
@@ -89,16 +110,15 @@ class GGMLLM(BaseLM):
         for request in requests:
             logprobs = []
             for i in range(0, len(request), self.max_length):
-                chunk = request[i:i+self.max_length]
-                chunk_loglikelihood = self.loglikelihood([(chunk, request[i+1:i+self.max_length+1])])
+                chunk = request[i:i + self.max_length]
+                chunk_loglikelihood = self.loglikelihood([(chunk, request[i + 1:i + self.max_length + 1])])
                 logprobs.extend(chunk_loglikelihood)
-            
+
             avg_loglikelihood = sum([logprob for logprob, _ in logprobs]) / len(logprobs)
             results.append((avg_loglikelihood, True))
 
         return results
 
-    
     def _model_call(self, inps):
         # Placeholder implementation
         raise NotImplementedError()
@@ -112,7 +132,7 @@ class GGMLLM(BaseLM):
 
     def tok_decode(self, tokens):
         return self.tokenizer.decode(tokens)
-    
+
     @property
     def batch_size(self):
         # Placeholder implementation
@@ -128,10 +148,10 @@ class GGMLLM(BaseLM):
         # Placeholder implementation
         raise NotImplementedError()
 
-    def max_length(self):        
+    def max_length(self):
         return self.max_length
 
     @property
     def max_gen_toks(self):
         # Placeholder implementation
-        raise NotImplementedError()
+        raise NotImplementedError()
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