[Inference]Optimize generation process of inference engine (#5356)

* opt inference engine

* fix run_benchmark.sh

* fix generate in engine.py

* rollback tesh_inference_engine.py

colossalai/inference/core/engine.py

@@ -134,12 +134,16 @@ class InferenceEngine:
 
     def generate(
         self,
+        prompts: List[str] = None,
+        prompts_token_ids: Union[List[int], torch.Tensor, np.ndarray] = None,
         generation_config: GenerationConfig = None,
     ) -> List[str]:
         """
         Executing the inference step.
 
         Args:
+            prompts (Union[List[str], optional): Input prompts. Defaults to None.
+            prompts_token_ids (List[List[int]], optional): token ids of input prompts. Defaults to None.
             generation_config (GenerationConfig, optional): Huggingface GenerationConfig used for inference. Defaults to None.
 
         Returns:
@@ -147,13 +151,23 @@ class InferenceEngine:
         """
 
         self.generation_config = generation_config
+        if prompts is not None or prompts_token_ids is not None:
+            self.add_request(prompts=prompts, prompts_token_ids=prompts_token_ids)
 
-        output_list = []
+        output_seqs_list = []
+        output_tokens_list = []
 
         while self.request_handler.check_unfinished_seqs():
-            output_list += self.step()
+            output_seqs_list += self.step()
 
-        return output_list
+        output_seqs_list = sorted(output_seqs_list, key=lambda x: int(x.request_id))
+
+        for seq in output_seqs_list:
+            output_tokens_list.append(seq.input_token_id + seq.output_token_id)
+
+        output_str = self.tokenizer.batch_decode(output_tokens_list, skip_special_tokens=True)
+
+        return output_str
 
     def add_request(
         self,
@@ -235,7 +249,6 @@ class InferenceEngine:
             List[str]: Decoded finished sequences generated by one step.
         """
 
-        output_list = []
         batch = self.request_handler.schedule()
 
         # TODO: padding_id is used for generating attn_mask and will be removed if nopad version is supported.
@@ -251,10 +264,4 @@ class InferenceEngine:
         self.request_handler.search_tokens(self.generation_config, logits)
         finished_sequences = self.request_handler.update()
 
-        # Decode completed sentences.
-        # TODO : update decoding step
-        for seq in finished_sequences:
-            output_str = self.tokenizer.decode(seq.input_token_id + seq.output_token_id, skip_special_tokens=True)
-            output_list.append(output_str)
-
-        return output_list
+        return finished_sequences

examples/inference/benchmark_llama.py

@@ -141,8 +141,7 @@ def benchmark_inference(args):
         with ctx:
             for _ in range(N_WARMUP_STEPS):
                 if args.mode == "caiinference":
-                    engine.add_request(prompts_token_ids=data)
-                    engine.generate(generation_config)
+                    engine.generate(prompts_token_ids=data, generation_config=generation_config)
                 else:
                     engine.generate(data, generation_config=generation_config)
                 if args.profile:
@@ -156,8 +155,7 @@ def benchmark_inference(args):
             whole_end2end = time.perf_counter()
             if args.mode == "caiinference":
                 for _ in range(args.batch_size // mbsz):
-                    engine.add_request(prompts_token_ids=data)
-                engine.generate(generation_config)
+                    engine.generate(prompts_token_ids=data, generation_config=generation_config)
             else:
                 for _ in range(args.batch_size // mbsz):
                     engine.generate(data, generation_config=generation_config)

tests/test_infer/test_inference_engine.py

@@ -49,7 +49,7 @@ def check_inference_engine(test_cai=False):
         inference_engine.add_request(prompts=inputs)
         assert inference_engine.request_handler._has_waiting()
         generation_config = GenerationConfig(do_sample=do_sample, top_p=top_p, top_k=top_k)
-        outputs = inference_engine.generate(generation_config)
+        outputs = inference_engine.generate(generation_config=generation_config)
     else:
         tokenizer.pad_token = tokenizer.eos_token
         tokenizer.pad_token_id = tokenizer.eos_token_id