[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/core/request_handler.py

@@ -328,12 +328,14 @@ class RequestHandler:
         """
         Sample tokens for finished requests.
         """
+
         # do logit processor
-        # NOTE: need to decide the granularity to process logits (sequence or batch)
-        config_dict = generation_config.to_dict()
-        for type in ["top_k", "top_p", "min_p"]:
-            if type in config_dict and config_dict[type] is not None:
-                logits = logit_processor(type, logits, config_dict[type])
+        if generation_config.do_sample:
+            # NOTE: need to decide the granularity to process logits (sequence or batch)
+            config_dict = generation_config.to_dict()
+            for type in ["temperature", "top_k", "top_p"]:
+                if type in config_dict and config_dict[type] is not None:
+                    logits = logit_processor(type, logits, config_dict[type])
 
         # calculate probs
         probs = torch.softmax(logits, dim=-1, dtype=torch.float)

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)
 

tests/test_infer/test_inference_engine.py

@@ -28,7 +28,12 @@ def check_inference_engine(use_engine=False, prompt_template=None, do_sample=Tru
     tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
     model = LlamaForCausalLM(
         LlamaConfig(
-            vocab_size=50000, hidden_size=512, intermediate_size=1536, num_attention_heads=4, num_hidden_layers=16
+            vocab_size=50000,
+            hidden_size=512,
+            intermediate_size=1536,
+            num_attention_heads=4,
+            num_key_value_heads=2,
+            num_hidden_layers=16,
         )
     ).cuda()
     model = model.eval()