resolve rebase conflicts on Branch feat/online-serving

colossalai/inference/core/engine.py

@@ -527,16 +527,9 @@ class InferenceEngine:
             List[str]: Inference result returned by one generation.
         """
         with torch.inference_mode():
-<<<<<<< HEAD
-
             if isinstance(prompts, str) and isinstance(request_ids, int):
-                    prompts = [prompts]
+                prompts = [prompts]
-                    request_ids = [request_ids]
+                request_ids = [request_ids]
-=======
-            if prompts is not None or prompts_token_ids is not None:
-                self.add_request(request_ids=request_ids, prompts=prompts, prompts_token_ids=prompts_token_ids)
->>>>>>> [Inference] Fix bugs and docs for feat/online-server (#5598)
-
             if prompts is not None or prompts_token_ids is not None:
                 gen_config_dict = generation_config.to_dict() if generation_config is not None else {}
                 self.add_request(
@@ -545,7 +538,7 @@ class InferenceEngine:
                     prompts_token_ids=prompts_token_ids,
                     **gen_config_dict,
                 )
-        
+
             output_seqs_list = []
             total_tokens_list = []
 

colossalai/inference/server/README.md

@@ -0,0 +1,27 @@
+# Online Service
+Colossal-Inference supports fast-api based online service. Simple completion and chat are both supported. Follow the commands below and
+you can simply construct a server with both completion and chat functionalities. For now we only support `Llama` model, we will fullfill
+the blank quickly.
+
+# Usage
+```bash
+# First, Lauch an API locally.
+python3 -m colossalai.inference.server.api_server  --model path of your llama2 model --chat_template "{% for message in messages %}
+{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}"
+
+
+# Second, you can turn to the page `http://127.0.0.1:8000/docs` to check the api
+
+# For completion service, you can invoke it
+curl -X POST  http://127.0.0.1:8000/completion  -H 'Content-Type: application/json'  -d '{"prompt":"hello, who are you? ","stream":"False"}'
+
+# For chat service, you can invoke it
+curl -X POST  http://127.0.0.1:8000/completion  -H 'Content-Type: application/json'  -d  '{"converation":
+                [{"role": "system", "content": "you are a helpful assistant"},
+                {"role": "user", "content": "what is 1+1?"},],
+                "stream": "False",}'
+# If you just want to test a simple generation, turn to generate api
+curl -X POST  http://127.0.0.1:8000/generate  -H 'Content-Type: application/json'  -d '{"prompt":"hello, who are you? ","stream":"False"}'
+
+```
+We also support streaming output, simply change the `stream` to `True` in the request body.

colossalai/kernel/triton/no_pad_rotary_embedding.py

@@ -598,8 +598,6 @@ def decoding_fused_rotary_embedding(
     """
     q_total_tokens, q_head_num, head_dim = q.shape
     assert q.size(0) == k.size(0) == v.size(0)
-    assert k.size(1) == v.size(1)
-    assert k_cache.size(-1) == v_cache.size(-1)
 
     if head_dim >= 512:
         num_warps = 16

tests/test_infer/test_continuous_batching.py

@@ -89,7 +89,7 @@ def check_continuous_batching(prompt_template):
 
 
 def run_dist(rank, world_size, port):
-    colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host="localhost")
+    colossalai.launch(rank=rank, world_size=world_size, port=port, host="localhost")
     check_continuous_batching()