Add /v1/internal/logits endpoint (oobabooga#4650)

docs/12 - OpenAI API.md

@@ -97,6 +97,29 @@ curl http://127.0.0.1:5000/v1/chat/completions \
   }'
 ```
 
+#### Logits
+
+```
+curl -k http://127.0.0.1:5000/v1/internal/logits \
+  -H "Content-Type: application/json" \
+  -d '{
+    "prompt": "Who is best, Asuka or Rei? Answer:",
+    "use_samplers": false
+  }'
+```
+
+#### Logits after sampling parameters
+
+```
+curl -k http://127.0.0.1:5000/v1/internal/logits \
+  -H "Content-Type: application/json" \
+  -d '{
+    "prompt": "Who is best, Asuka or Rei? Answer:",
+    "use_samplers": true,
+    "top_k": 3
+  }'
+```
+
 #### Python chat example
 
 ```python

extensions/openai/script.py

@@ -16,6 +16,7 @@
 import extensions.openai.completions as OAIcompletions
 import extensions.openai.embeddings as OAIembeddings
 import extensions.openai.images as OAIimages
+import extensions.openai.logits as OAIlogits
 import extensions.openai.models as OAImodels
 import extensions.openai.moderations as OAImoderations
 from extensions.openai.errors import ServiceUnavailableError
@@ -38,6 +39,8 @@
     EncodeRequest,
     EncodeResponse,
     LoadModelRequest,
+    LogitsRequest,
+    LogitsResponse,
     ModelInfoResponse,
     TokenCountResponse,
     to_dict
@@ -242,6 +245,16 @@ async def handle_token_count(request_data: EncodeRequest):
     return JSONResponse(response)
 
 
+@app.post("/v1/internal/logits", response_model=LogitsResponse, dependencies=check_key)
+async def handle_logits(request_data: LogitsRequest):
+    '''
+    Given a prompt, returns the top 50 most likely logits as a dict.
+    The keys are the tokens, and the values are the probabilities.
+    '''
+    response = OAIlogits._get_next_logits(to_dict(request_data))
+    return JSONResponse(response)
+
+
 @app.post("/v1/internal/stop-generation", dependencies=check_key)
 async def handle_stop_generation(request: Request):
     stop_everything_event()

extensions/openai/typing.py

@@ -126,13 +126,13 @@ class EncodeRequest(BaseModel):
     text: str
 
 
-class DecodeRequest(BaseModel):
+class EncodeResponse(BaseModel):
     tokens: List[int]
+    length: int
 
 
-class EncodeResponse(BaseModel):
+class DecodeRequest(BaseModel):
     tokens: List[int]
-    length: int
 
 
 class DecodeResponse(BaseModel):
@@ -143,6 +143,24 @@ class TokenCountResponse(BaseModel):
     length: int
 
 
+class LogitsRequestParams(BaseModel):
+    prompt: str
+    use_samplers: bool = False
+    frequency_penalty: float | None = 0
+    max_tokens: int | None = 16
+    presence_penalty: float | None = 0
+    temperature: float | None = 1
+    top_p: float | None = 1
+
+
+class LogitsRequest(GenerationOptions, LogitsRequestParams):
+    pass
+
+
+class LogitsResponse(BaseModel):
+    logits: dict
+
+
 class ModelInfoResponse(BaseModel):
     model_name: str
     lora_names: List[str]

modules/logits.py

@@ -8,7 +8,7 @@
 global_scores = None
 
 
-def get_next_logits(prompt, state, use_samplers, previous):
+def get_next_logits(prompt, state, use_samplers, previous, return_dict=False):
     if shared.model is None:
         logger.error("No model is loaded! Select one in the Model tab.")
         return 'Error: No model is loaded1 Select one in the Model tab.', previous
@@ -56,8 +56,16 @@ def get_next_logits(prompt, state, use_samplers, previous):
         topk_indices = [i.expand((1, 1)) for i in topk_indices]
 
     tokens = [shared.tokenizer.decode(i) for i in topk_indices]
-    output = ''
-    for row in list(zip(topk_values, tokens)):
-        output += f"{row[0]}  -  {repr(row[1])}\n"
 
-    return output, previous
+    if return_dict:
+        output = {}
+        for row in list(zip(topk_values, tokens)):
+            output[row[1]] = row[0]
+
+        return output
+    else:
+        output = ''
+        for row in list(zip(topk_values, tokens)):
+            output += f"{row[0]}  -  {repr(row[1])}\n"
+
+        return output, previous