Improve postprocessing in TIS serving by applying Incremental de-tokenizing (#197)

* change to incremental decoding

* update

benchmark/profile_serving.py

@@ -55,7 +55,7 @@ def infer(chatbot, session_id: int, req_que: mp.Queue, res_que: mp.Queue):
 def warmup(tritonserver_addr: str,
            concurrency: int,
            output_seqlen: int,
-           warmup_round: int = 4):
+           warmup_round: int = 1):
     print('start to warmup ...')
 
     def _infer(_chatbot, session_id):
@@ -87,7 +87,7 @@ def warmup(tritonserver_addr: str,
 
 
 def read_dataset(tokenizer_path: str, dataset_path: str, samples: int,
-                 test_round: int, session_len: int):
+                 session_len: int):
     start = time.perf_counter()
     with open(dataset_path) as f:
         dataset = json.load(f)
@@ -119,14 +119,12 @@ def read_dataset(tokenizer_path: str, dataset_path: str, samples: int,
     if samples > 0:
         filtered_dataset = random.sample(filtered_dataset, samples)
 
-    filtered_dataset *= test_round
-    random.shuffle(filtered_dataset)
     que = mp.Queue()
     for data in filtered_dataset:
         que.put(data)
     print(f'elapsed time for filtering: '
           f'{round(time.perf_counter() - start, 2)} s')
-    return que
+    return que, len(filtered_dataset)
 
 
 def main(tritonserver_addr: str,
@@ -134,11 +132,10 @@ def main(tritonserver_addr: str,
          dataset_path: str,
          concurrency: int = 1,
          session_len: int = 2048,
-         samples: int = 1000,
-         test_round: int = 1):
+         samples: int = 1000):
     warmup(tritonserver_addr, concurrency, session_len - 1)
-    req_que = read_dataset(tokenizer_path, dataset_path, samples, test_round,
-                           session_len)
+    req_que, n_req = read_dataset(tokenizer_path, dataset_path, samples,
+                                  session_len)
     res_que = mp.Queue()
     procs = []
     _start = time.perf_counter()
@@ -168,13 +165,17 @@ def main(tritonserver_addr: str,
     first_token_latency_min = np.min(stats[:, 0], axis=0)
     first_token_latency_max = np.max(stats[:, 0], axis=0)
     first_token_latency_ave = np.mean(stats[:, 0], axis=0)
-    throughput = np.sum(stats[:, 1], axis=0) / elapsed_time
+    token_throughput = np.sum(stats[:, 1], axis=0) / elapsed_time
+    req_throughput = n_req / elapsed_time
+
     print(f'\n{"-" * 50}\nconcurrency: {concurrency}\n'
           f'elapsed_time: {elapsed_time:.2f}s\n'
           f'first_token latency(min, max, ave): '
           f'{first_token_latency_min:.2f}s, {first_token_latency_max:.2f}s, '
           f'{first_token_latency_ave:.2f}s\n'
-          f'throughput: {throughput:.2f} token/s\n{"-" * 50}')
+          f'token throughput: {token_throughput:.2f} token/s\n'
+          f'req throughput: {req_throughput} req/s\n'
+          f'{"-" * 50}\n')
 
 
 if __name__ == '__main__':

lmdeploy/serve/client.py

@@ -20,7 +20,6 @@ def main(tritonserver_addr: str, session_id: int = 1):
     Args:
         tritonserver_addr (str): the address in format "ip:port" of
           triton inference server
-        model_name (str): the name of the deployed model
         session_id (int): the identical id of a session
     """
     log_level = os.environ.get('SERVICE_LOG_LEVEL', 'WARNING')

lmdeploy/serve/turbomind/chatbot.py

@@ -26,6 +26,7 @@ class Session:
     request_id: str = ''
     histories: str = ''  # history conversations of the session
     sequence_length: int = 0  # the total generated token number in the session
+    sequence_offset: int = 0  # the new generated token offset in the session
     prompt: str = ''
     response: str = ''
     status: int = None  # status of the session
@@ -539,14 +540,15 @@ class Chatbot:
         Yields:
             tuple: status, text, generated token number
         """
-        offset = n_input_token + preseq_length
+        session.sequence_offset = n_input_token + preseq_length
+        sentinel = n_input_token + preseq_length
         status, res, n_token = None, '', 0
         while True:
             result = res_queue.get()
             if result is None:
                 status = StatusCode.TRITON_STREAM_END
                 res = session.response
-                n_token = session.sequence_length - offset
+                n_token = session.sequence_length - sentinel
                 session.status = StatusCode.TRITON_STREAM_END
                 break
             if 'errcode' in result:
@@ -569,30 +571,31 @@ class Chatbot:
                 output_ids = result.as_numpy('output_ids')
 
                 session.sequence_length = sequence_length.squeeze()
-                sequence_length = sequence_length - offset
+                new_token_length = sequence_length - session.sequence_offset
                 last_token_id = output_ids[-1][-1][session.sequence_length - 1]
                 if last_token_id == eos_id:
                     session.sequence_length = session.sequence_length - 1
-                    sequence_length = sequence_length - 1
+                    new_token_length = new_token_length - 1
 
                 output_ids = output_ids.reshape((1, 1, output_ids.shape[-1]))
-                sequence_length = sequence_length.reshape(
-                    (1, sequence_length.shape[-1]))
+                new_token_length = new_token_length.reshape(
+                    (1, new_token_length.shape[-1]))
 
                 if profile_generation:
                     yield (StatusCode.TRITON_STREAM_ING,
                            'postprocessing is ignored during profiling '
-                           'token generation', sequence_length.squeeze())
+                           'token generation', new_token_length.squeeze())
                     continue
-                output_str = postprocess(output_ids[:, :, offset:],
-                                         sequence_length)
+                output_str = postprocess(
+                    output_ids[:, :, session.sequence_offset:],
+                    new_token_length)
+                session.sequence_offset = session.sequence_length
                 text = output_str[0].decode()
                 if display:
-                    new_text = text[len(session.response):]
-                    print(new_text, end='', flush=True)
-                session.response = text
+                    print(text, end='', flush=True)
+                session.response += text
                 yield (StatusCode.TRITON_STREAM_ING, session.response,
-                       sequence_length.squeeze())
+                       session.sequence_offset - sentinel)
             except Exception as e:
                 logger.error(f'catch exception: {e}')