[Inference]Adapt temperature processing logic (#5689)

* Adapt temperature processing logic

* add ValueError for top_p and top_k

* add GQA Test

* fix except_msg

colossalai/inference/logit_processors.py

@@ -17,11 +17,30 @@ def register_logit_processor(process_type):
     return register
 
 
+@register_logit_processor("temperature")
+def temperature_logit_process(logits, temperature: float):
+    """
+    apply temperature scaling.
+    """
+
+    if not isinstance(temperature, float) or not (0.0 < temperature <= 1.0):
+        except_msg = f"'temperature={temperature}' should be a strictly positive float, less than or equal to 1.0 and greater than 0."
+        if temperature == 0.0:
+            except_msg += "if you want to use greedy decoding strategies, set `do_sample=False`."
+        raise ValueError(except_msg)
+
+    return logits if temperature == 1.0 else logits / temperature
+
+
 @register_logit_processor("top_k")
 def top_k_logit_processor(logits, top_k: int):
     """
     top_k logit processor
     """
+
+    if not isinstance(top_k, int) or top_k <= 0:
+        raise ValueError(f"`top_k` should be a strictly positive integer, but got {top_k}.")
+
     indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
     logits[indices_to_remove] = -float("inf")
     return logits
@@ -32,6 +51,10 @@ def top_p_logit_processor(logits, top_p: float):
     """
     top_p logit processor
     """
+
+    if top_p < 0 or top_p > 1.0:
+        raise ValueError(f"`top_p` should be a float > 0 and < 1, but got {top_p}.")
+
     sorted_logits, sorted_indices = torch.sort(logits, descending=True)
     cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)