# Copyright 2020-2025 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import subprocess import pytest from transformers import AutoModelForCausalLM from transformers.testing_utils import require_torch_multi_accelerator, torch_device from trl.extras.vllm_client import VLLMClient from trl.scripts.vllm_serve import chunk_list from .testing_utils import TrlTestCase, kill_process, require_3_accelerators, require_vllm class TestChunkList(TrlTestCase): def test_even_split(self): assert chunk_list([1, 2, 3, 4, 5, 6], 2) == [[1, 2, 3], [4, 5, 6]] def test_uneven_split(self): assert chunk_list([1, 2, 3, 4, 5, 6], 4) == [[1, 2], [3, 4], [5], [6]] def test_more_chunks_than_elements(self): assert chunk_list([1, 2, 3, 4, 5, 6], 8) == [[1], [2], [3], [4], [5], [6], [], []] def test_n_equals_len(self): assert chunk_list([1, 2, 3], 3) == [[1], [2], [3]] def test_n_is_1(self): assert chunk_list([1, 2, 3], 1) == [[1, 2, 3]] def test_single_element_list(self): assert chunk_list([42], 2) == [[42], []] def test_any_dtype(self): assert chunk_list([1, "two", 3.0, {"four": 4}, ["f", "i", "v", "e"]], 2) == [ [1, "two", 3.0], [{"four": 4}, ["f", "i", "v", "e"]], ] @pytest.mark.slow @require_torch_multi_accelerator @require_vllm class TestVLLMClientServer(TrlTestCase): model_id = "Qwen/Qwen2.5-1.5B" @classmethod def setup_class(cls): # We want the server to run on accelerator 1, so we set VISIBLE_DEVICES to "1" env = os.environ.copy() VISIBLE_DEVICES = "ZE_AFFINITY_MASK" if torch_device == "xpu" else "CUDA_VISIBLE_DEVICES" env[VISIBLE_DEVICES] = "1" # Restrict to accelerator 1 # Start the server process cls.server_process = subprocess.Popen( ["trl", "vllm-serve", "--model", cls.model_id], stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env ) # Initialize the client cls.client = VLLMClient(connection_timeout=240, host="localhost") cls.client.init_communicator() def test_generate(self): prompts = ["Hello, AI!", "Tell me a joke"] outputs = self.client.generate(prompts) prompt_ids = outputs["prompt_ids"] completion_ids = outputs["completion_ids"] # Check that the outputs are lists assert isinstance(prompt_ids, list) assert isinstance(completion_ids, list) # Check that the number of sequences are equal to the number of prompts assert len(prompt_ids) == len(prompts) assert len(completion_ids) == len(prompts) # Check that the sequences are lists of integers for seq in prompt_ids: assert all(isinstance(tok, int) for tok in seq) for seq in completion_ids: assert all(isinstance(tok, int) for tok in seq) def test_generate_with_params(self): prompts = ["Hello, AI!", "Tell me a joke"] completion_ids = self.client.generate(prompts, n=2, repetition_penalty=0.9, temperature=0.8, max_tokens=32)[ "completion_ids" ] # Check that the output is a list assert isinstance(completion_ids, list) # Check that the number of generated sequences is 2 times the number of prompts assert len(completion_ids) == 2 * len(prompts) # Check that the generated sequences are lists of integers for seq in completion_ids: assert all(isinstance(tok, int) for tok in seq) # Check that the length of the generated sequences is less than or equal to 32 for seq in completion_ids: assert len(seq) <= 32 def test_update_model_params(self): model = AutoModelForCausalLM.from_pretrained(self.model_id, device_map=torch_device) self.client.update_model_params(model) def test_reset_prefix_cache(self): # Test resetting the prefix cache self.client.reset_prefix_cache() @classmethod def teardown_class(cls): # Close the client cls.client.close_communicator() # vLLM x pytest (or Popen) seems not to handle process termination well. To avoid zombie processes, we need to # kill the server process and its children explicitly. kill_process(cls.server_process) # Same as above but using base_url to instantiate the client. @pytest.mark.slow @require_torch_multi_accelerator @require_vllm class TestVLLMClientServerBaseURL(TrlTestCase): model_id = "Qwen/Qwen2.5-1.5B" @classmethod def setup_class(cls): # We want the server to run on accelerator 1, so we set VISIBLE_DEVICES to "1" env = os.environ.copy() VISIBLE_DEVICES = "ZE_AFFINITY_MASK" if torch_device == "xpu" else "CUDA_VISIBLE_DEVICES" env[VISIBLE_DEVICES] = "1" # Restrict to accelerator 1 # Start the server process cls.server_process = subprocess.Popen( ["trl", "vllm-serve", "--model", cls.model_id], stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env ) # Initialize the client cls.client = VLLMClient(base_url="http://localhost:8000", connection_timeout=240) cls.client.init_communicator() def test_generate(self): prompts = ["Hello, AI!", "Tell me a joke"] outputs = self.client.generate(prompts) prompt_ids = outputs["prompt_ids"] completion_ids = outputs["completion_ids"] # Check that the outputs are lists assert isinstance(prompt_ids, list) assert isinstance(completion_ids, list) # Check that the number of sequences are equal to the number of prompts assert len(prompt_ids) == len(prompts) assert len(completion_ids) == len(prompts) # Check that the sequences are lists of integers for seq in prompt_ids: assert all(isinstance(tok, int) for tok in seq) for seq in completion_ids: assert all(isinstance(tok, int) for tok in seq) def test_generate_with_params(self): prompts = ["Hello, AI!", "Tell me a joke"] completion_ids = self.client.generate(prompts, n=2, repetition_penalty=0.9, temperature=0.8, max_tokens=32)[ "completion_ids" ] # Check that the output is a list assert isinstance(completion_ids, list) # Check that the number of generated sequences is 2 times the number of prompts assert len(completion_ids) == 2 * len(prompts) # Check that the generated sequences are lists of integers for seq in completion_ids: assert all(isinstance(tok, int) for tok in seq) # Check that the length of the generated sequences is less than or equal to 32 for seq in completion_ids: assert len(seq) <= 32 def test_update_model_params(self): model = AutoModelForCausalLM.from_pretrained(self.model_id, device_map=torch_device) self.client.update_model_params(model) def test_reset_prefix_cache(self): # Test resetting the prefix cache self.client.reset_prefix_cache() @classmethod def teardown_class(cls): # Close the client cls.client.close_communicator() # vLLM x pytest (or Popen) seems not to handle process termination well. To avoid zombie processes, we need to # kill the server process and its children explicitly. kill_process(cls.server_process) @pytest.mark.slow @require_3_accelerators @require_vllm class TestVLLMClientServerTP(TrlTestCase): model_id = "Qwen/Qwen2.5-1.5B" @classmethod def setup_class(cls): # We want the server to run on accelerator 1 and 2, so we set VISIBLE_DEVICES to "1,2" env = os.environ.copy() VISIBLE_DEVICES = "ZE_AFFINITY_MASK" if torch_device == "xpu" else "CUDA_VISIBLE_DEVICES" env[VISIBLE_DEVICES] = "1,2" # Restrict to accelerator 1 and 2 # Start the server process cls.server_process = subprocess.Popen( ["trl", "vllm-serve", "--model", cls.model_id, "--tensor_parallel_size", "2"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env, ) # Initialize the client cls.client = VLLMClient(connection_timeout=240, host="localhost") cls.client.init_communicator() def test_generate(self): prompts = ["Hello, AI!", "Tell me a joke"] outputs = self.client.generate(prompts) prompt_ids = outputs["prompt_ids"] completion_ids = outputs["completion_ids"] # Check that the outputs are lists assert isinstance(prompt_ids, list) assert isinstance(completion_ids, list) # Check that the number of sequences are equal to the number of prompts assert len(prompt_ids) == len(prompts) assert len(completion_ids) == len(prompts) # Check that the sequences are lists of integers for seq in prompt_ids: assert all(isinstance(tok, int) for tok in seq) for seq in completion_ids: assert all(isinstance(tok, int) for tok in seq) def test_update_model_params(self): model = AutoModelForCausalLM.from_pretrained(self.model_id, device_map=torch_device) self.client.update_model_params(model) def test_reset_prefix_cache(self): # Test resetting the prefix cache self.client.reset_prefix_cache() @classmethod def teardown_class(cls): # Close the client cls.client.close_communicator() # vLLM x pytest (or Popen) seems not to handle process termination well. To avoid zombie processes, we need to # kill the server process and its children explicitly. kill_process(cls.server_process) @pytest.mark.slow @require_3_accelerators @require_vllm class TestVLLMClientServerDP(TrlTestCase): model_id = "Qwen/Qwen2.5-1.5B" @classmethod def setup_class(cls): # We want the server to run on accelerator 1 and 2, so we set VISIBLE_DEVICES to "1,2" env = os.environ.copy() VISIBLE_DEVICES = "ZE_AFFINITY_MASK" if torch_device == "xpu" else "CUDA_VISIBLE_DEVICES" env[VISIBLE_DEVICES] = "1,2" # Restrict to accelerator 1 and 2 # Start the server process cls.server_process = subprocess.Popen( ["trl", "vllm-serve", "--model", cls.model_id, "--data_parallel_size", "2"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env, ) # Initialize the client cls.client = VLLMClient(connection_timeout=240, host="localhost") cls.client.init_communicator() def test_generate(self): prompts = ["Hello, AI!", "Tell me a joke"] outputs = self.client.generate(prompts) prompt_ids = outputs["prompt_ids"] completion_ids = outputs["completion_ids"] # Check that the outputs are lists assert isinstance(prompt_ids, list) assert isinstance(completion_ids, list) # Check that the number of sequences are equal to the number of prompts assert len(prompt_ids) == len(prompts) assert len(completion_ids) == len(prompts) # Check that the sequences are lists of integers for seq in prompt_ids: assert all(isinstance(tok, int) for tok in seq) for seq in completion_ids: assert all(isinstance(tok, int) for tok in seq) def test_update_model_params(self): model = AutoModelForCausalLM.from_pretrained(self.model_id, device_map=torch_device) self.client.update_model_params(model) def test_reset_prefix_cache(self): # Test resetting the prefix cache self.client.reset_prefix_cache() @classmethod def teardown_class(cls): # Close the client cls.client.close_communicator() # vLLM x pytest (or Popen) seems not to handle process termination well. To avoid zombie processes, we need to # kill the server process and its children explicitly. kill_process(cls.server_process) @pytest.mark.slow @require_torch_multi_accelerator @require_vllm class TestVLLMClientServerDeviceParameter(TrlTestCase): """Test the device parameter functionality in init_communicator.""" model_id = "Qwen/Qwen2.5-1.5B" @classmethod def setup_class(cls): # We want the server to run on accelerator 1, so we set VISIBLE_DEVICES to "1" env = os.environ.copy() VISIBLE_DEVICES = "ZE_AFFINITY_MASK" if torch_device == "xpu" else "CUDA_VISIBLE_DEVICES" env[VISIBLE_DEVICES] = "1" # Restrict to accelerator 1 # Start the server process cls.server_process = subprocess.Popen( ["trl", "vllm-serve", "--model", cls.model_id], stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env ) def test_init_communicator_with_device_int(self): """Test init_communicator with integer device parameter.""" client = VLLMClient(connection_timeout=240, host="localhost") client.init_communicator(device=0) # Explicitly specify device 0 # Test basic functionality prompts = ["Hello, AI!"] outputs = client.generate(prompts) prompt_ids = outputs["prompt_ids"] completion_ids = outputs["completion_ids"] assert isinstance(prompt_ids, list) assert len(prompt_ids) == len(prompts) assert isinstance(completion_ids, list) assert len(completion_ids) == len(prompts) client.close_communicator() def test_init_communicator_with_device_string(self): """Test init_communicator with string device parameter.""" client = VLLMClient(connection_timeout=240, host="localhost") client.init_communicator(device=0) # Explicitly specify device as string # Test basic functionality prompts = ["Hello, AI!"] outputs = client.generate(prompts)["completion_ids"] assert isinstance(outputs, list) assert len(outputs) == len(prompts) client.close_communicator() def test_init_communicator_with_torch_device(self): """Test init_communicator with torch.device object.""" import torch client = VLLMClient(connection_timeout=240, host="localhost") device = torch.device(0) client.init_communicator(device=device) # Explicitly specify torch.device object # Test basic functionality prompts = ["Hello, AI!"] outputs = client.generate(prompts)["completion_ids"] assert isinstance(outputs, list) assert len(outputs) == len(prompts) client.close_communicator() @classmethod def teardown_class(cls): # vLLM x pytest (or Popen) seems not to handle process termination well. To avoid zombie processes, we need to # kill the server process and its children explicitly. kill_process(cls.server_process)