[Feature] Add temp_scaled_logprobs and top_p_normalized_logprobs parameters for logits and logprobs post processing (#3552)

* [feature] Add temp_scaled_logprobs and top_p_normalized_logprobs parameters for logits and logprobs post processing

* infer engine support temp_scaled_logprobs and top_p_normalized_logprobs

* delete some code

* code check

* code check and add doc

* fix tokenizer.decoder(-1), return 'Invalid Token'

* add ci for temp_scaled and top_p logprobs

* check test

* check seq len time shape

* logprob clip inf

---------

Co-authored-by: sunlei1024 <sunlei5788@gmail.com>

tests/ce/server/test_logprobs.py

@@ -154,8 +154,101 @@ def test_stream_without_logprobs():
     assert result_chunk["choices"][0]["logprobs"] is None
 
 
+def test_stream_with_temp_scaled_logprobs():
+    """
+    测试流式响应开启 temp_scaled_logprobs 后，首个 token 的概率信息是否正确。
+    """
+    data = {
+        "stream": True,
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "牛顿的三大运动定律是什么？"},
+        ],
+        "max_tokens": 3,
+        "temperature": 0.8,
+        "top_p": 0,
+        "temp_scaled_logprobs": True,
+    }
+
+    payload = build_request_payload(TEMPLATE, data)
+    response = send_request(URL, payload)
+
+    # 解析首个包含 content 的流式 chunk
+    result_chunk = {}
+    for line in response.iter_lines():
+        if not line:
+            continue
+        decoded = line.decode("utf-8").removeprefix("data: ")
+        if decoded == "[DONE]":
+            break
+
+        chunk = json.loads(decoded)
+        content = chunk["choices"][0]["delta"].get("content")
+        if content:
+            result_chunk = chunk
+            print(json.dumps(result_chunk, indent=2, ensure_ascii=False))
+            break
+
+    # 校验概率字段
+    assert result_chunk["choices"][0]["delta"]["content"] == "牛顿"
+    assert result_chunk["choices"][0]["logprobs"]["content"][0]["token"] == "牛顿"
+    assert result_chunk["choices"][0]["logprobs"]["content"][0]["logprob"] == -0.006811376195400953
+    assert result_chunk["choices"][0]["logprobs"]["content"][0]["top_logprobs"][0] == {
+        "token": "牛顿",
+        "logprob": -0.006811376195400953,
+        "bytes": [231, 137, 155, 233, 161, 191],
+    }
+
+
+def test_stream_with_top_p_normalized_logprobs():
+    """
+    测试流式响应开启 top_p_normalized_logprobs 后，首个 token 的概率信息是否正确。
+    """
+    data = {
+        "stream": True,
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "牛顿的三大运动定律是什么？"},
+        ],
+        "max_tokens": 3,
+        "top_p": 0,
+        "top_p_normalized_logprobs": True,
+    }
+
+    payload = build_request_payload(TEMPLATE, data)
+    response = send_request(URL, payload)
+
+    # 解析首个包含 content 的流式 chunk
+    result_chunk = {}
+    for line in response.iter_lines():
+        if not line:
+            continue
+        decoded = line.decode("utf-8").removeprefix("data: ")
+        if decoded == "[DONE]":
+            break
+
+        chunk = json.loads(decoded)
+        content = chunk["choices"][0]["delta"].get("content")
+        if content:
+            result_chunk = chunk
+            print(json.dumps(result_chunk, indent=2, ensure_ascii=False))
+            break
+
+    # 校验概率字段
+    assert result_chunk["choices"][0]["delta"]["content"] == "牛顿"
+    assert result_chunk["choices"][0]["logprobs"]["content"][0]["token"] == "牛顿"
+    assert result_chunk["choices"][0]["logprobs"]["content"][0]["logprob"] == 0.0
+    assert result_chunk["choices"][0]["logprobs"]["content"][0]["top_logprobs"][0] == {
+        "token": "牛顿",
+        "logprob": 0.0,
+        "bytes": [231, 137, 155, 233, 161, 191],
+    }
+
+
 if __name__ == "__main__":
     test_unstream_with_logprobs()
     test_unstream_without_logprobs()
     test_stream_with_logprobs()
     test_stream_without_logprobs()
+    test_stream_with_temp_scaled_logprobs()
+    test_stream_with_top_p_normalized_logprobs()