[Minor] Fix code style (#2311)

test/srt/test_get_weights_by_name.py

@@ -67,7 +67,7 @@ class TestGetWeightsByName(unittest.TestCase):
             terminate_process(self.process)
 
     def assert_tie_word_embeddings(self, truncate_size):
-        print(f"assert_tie_word_embeddings")
+        print("assert_tie_word_embeddings")
         if self.backend == "Engine":
             backend_ret = _process_return(
                 self.engine.get_weights_by_name("lm_head.weight", truncate_size)
@@ -79,7 +79,7 @@ class TestGetWeightsByName(unittest.TestCase):
                     json={"name": "lm_head.weight", "truncate_size": truncate_size},
                 ).json()
             )
-        print(f"assert_tie_word_embeddings of hf and backend")
+        print("assert_tie_word_embeddings of hf and backend")
         assert np.allclose(
             self.hf_model.get_parameter("model.embed_tokens.weight")
             .cpu()

test/srt/test_update_weights_from_distributed.py

@@ -127,7 +127,7 @@ def init_process_hf(
     hf_instruct_params = []
     hf_base_params = []
 
-    print(f"get parameter in hf instruct model and base model")
+    print("get parameter in hf instruct model and base model")
     for parameter_name in checking_parameters:
         hf_instruct_params.append(
             hf_instruct_model.get_parameter(parameter_name)[:truncate_size]
@@ -186,7 +186,6 @@ def init_process_hf(
     param_queue.put(("broadcast_time", broadcast_time))
 
     # Delete the huggingface models to free up memory.
-
     del hf_instruct_model
     del hf_base_model
     gc.collect()
@@ -238,7 +237,6 @@ def init_process_sgl(
         print(f"rank {rank} init server on url: {url}")
 
     # Get weights of instruct model, i.e. pre-training weights.
-
     instruct_params = []
     for parameter_name in checking_parameters:
         instruct_params.append(
@@ -253,7 +251,6 @@ def init_process_sgl(
     param_queue.put((f"sgl_dp_{rank}_instruct_params", instruct_params))
 
     # Init weight update group with the training engine.
-
     if backend == "Engine":
         engine.init_weights_update_group(
             master_address="localhost",
@@ -282,7 +279,6 @@ def init_process_sgl(
     # The last parameter is lm_head.weight, which is tied
     # with embed_tokens.weight. Actually, we only need
     # to update embed_tokens.weight once.
-
     tie_word_embeddings = (
         True if model_name == DEFAULT_SMALL_MODEL_NAME_FOR_TEST else False
     )
@@ -291,7 +287,6 @@ def init_process_sgl(
         update_parameters.remove("lm_head.weight")
 
     # Get weights from the training engine and update the inference engine.
-
     for parameter_name in update_parameters:
         if backend == "Engine":
             engine.update_weights_from_distributed(
@@ -312,7 +307,6 @@ def init_process_sgl(
     time_end_update = time.time()
 
     # Measure the latency of broadcast/weights update.
-
     update_time = time_end_update - time_begin_update
     print(
         f"fully update model_name {model_name} rank {rank} parameter from distributed time: {update_time:.3f}s"
@@ -320,7 +314,6 @@ def init_process_sgl(
     param_queue.put((f"update_sgl_dp_{rank}_time", update_time))
 
     # Get the weights of post-training model after weights update for correctness check.
-
     base_params = []
     for parameter_name in checking_parameters:
         if backend == "Engine":
@@ -340,7 +333,6 @@ def init_process_sgl(
     param_queue.put((f"sgl_dp_{rank}_base_params", base_params))
 
     # Shutdown the engine or terminate the server process.
-
     if backend == "Engine":
         engine.shutdown()
     else:
@@ -426,7 +418,6 @@ def test_update_weights_from_distributed(
 
     # Check the correctness of weights update by verifying
     # the weights of instruct model and base model.
-
     for i in range(len(params["hf_instruct"])):
         verify_params_close(
             params["hf_instruct"][i],
@@ -463,7 +454,6 @@ def test_update_weights_from_distributed(
     ), "hf_instruct_params and hf_base_params have different lengths"
 
     # Check if the weights of lm_head are tied with embed_tokens.
-
     params_to_check = [
         (
             params["hf_instruct"],
@@ -509,7 +499,6 @@ def test_update_weights_from_distributed(
 
     # Time limit for broadcast and update on CI is 3 / 6
     # On local H100, it's 1 / 2
-
     time_limit = 3 if model_name == DEFAULT_SMALL_MODEL_NAME_FOR_TEST else 6
 
     assert (
@@ -526,7 +515,6 @@ def test_update_weights_from_distributed(
         ), f"update_sgl_dp_two_time exceeds time limit {time_limit}s"
 
     # Delete the context and close the parameter queue.
-
     del context
     param_queue.close()
     param_queue.join_thread()