[ci][test] add correctness test for cpu offloading

.buildkite/test-pipeline.yaml

@@ -46,6 +46,7 @@ steps:
   commands:
   - pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.0.8/flashinfer-0.0.8+cu121torch2.3-cp310-cp310-linux_x86_64.whl
   - pytest -v -s basic_correctness/test_basic_correctness.py
+  - pytest -v -s basic_correctness/test_cpu_offload.py
   - VLLM_ATTENTION_BACKEND=XFORMERS pytest -v -s basic_correctness/test_chunked_prefill.py
   - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s basic_correctness/test_chunked_prefill.py
   - VLLM_TEST_ENABLE_ARTIFICIAL_PREEMPT=1 pytest -v -s basic_correctness/test_preemption.py

tests/basic_correctness/test_cpu_offload.py

@@ -0,0 +1,8 @@
+from ..utils import compare_two_settings
+
+
+def test_cpu_offload():
+    compare_two_settings("meta-llama/Llama-2-7b-hf", [],
+                         ["--cpu-offload-gb", "4"])
+    compare_two_settings("nm-testing/llama7b-one-shot-2_4-w4a16-marlin24-t",
+                         [], ["--cpu-offload-gb", "1"])

tests/distributed/test_pipeline_parallel.py

@@ -1,7 +1,6 @@
 import pytest
-from transformers import AutoTokenizer
 
-from ..utils import RemoteOpenAIServer
+from ..utils import compare_two_settings
 
 
 @pytest.mark.parametrize(
@@ -13,7 +12,6 @@
         (1, 4, 1, 0, "meta-llama/Meta-Llama-3-8B"),
     ])
 def test_compare_tp(TP_SIZE, PP_SIZE, EAGER_MODE, CHUNKED_PREFILL, MODEL_NAME):
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 
     pp_args = [
         # use half precision for speed and memory savings in CI environment
@@ -48,85 +46,4 @@ def test_compare_tp(TP_SIZE, PP_SIZE, EAGER_MODE, CHUNKED_PREFILL, MODEL_NAME):
         pp_args.append("--enforce-eager")
         tp_args.append("--enforce-eager")
 
-    prompt = "Hello, my name is"
-    token_ids = tokenizer(prompt)["input_ids"]
-    results = []
-    for args in (pp_args, tp_args):
-        with RemoteOpenAIServer(MODEL_NAME, args) as server:
-            client = server.get_client()
-
-            # test models list
-            models = client.models.list()
-            models = models.data
-            served_model = models[0]
-            results.append({
-                "test": "models_list",
-                "id": served_model.id,
-                "root": served_model.root,
-            })
-
-            # test with text prompt
-            completion = client.completions.create(model=MODEL_NAME,
-                                                   prompt=prompt,
-                                                   max_tokens=5,
-                                                   temperature=0.0)
-
-            results.append({
-                "test": "single_completion",
-                "text": completion.choices[0].text,
-                "finish_reason": completion.choices[0].finish_reason,
-                "usage": completion.usage,
-            })
-
-            # test using token IDs
-            completion = client.completions.create(
-                model=MODEL_NAME,
-                prompt=token_ids,
-                max_tokens=5,
-                temperature=0.0,
-            )
-
-            results.append({
-                "test": "token_ids",
-                "text": completion.choices[0].text,
-                "finish_reason": completion.choices[0].finish_reason,
-                "usage": completion.usage,
-            })
-
-            # test simple list
-            batch = client.completions.create(
-                model=MODEL_NAME,
-                prompt=[prompt, prompt],
-                max_tokens=5,
-                temperature=0.0,
-            )
-
-            results.append({
-                "test": "simple_list",
-                "text0": batch.choices[0].text,
-                "text1": batch.choices[1].text,
-            })
-
-            # test streaming
-            batch = client.completions.create(
-                model=MODEL_NAME,
-                prompt=[prompt, prompt],
-                max_tokens=5,
-                temperature=0.0,
-                stream=True,
-            )
-            texts = [""] * 2
-            for chunk in batch:
-                assert len(chunk.choices) == 1
-                choice = chunk.choices[0]
-                texts[choice.index] += choice.text
-            results.append({
-                "test": "streaming",
-                "texts": texts,
-            })
-
-    n = len(results) // 2
-    pp_results = results[:n]
-    tp_results = results[n:]
-    for pp, tp in zip(pp_results, tp_results):
-        assert pp == tp
+    compare_two_settings(MODEL_NAME, pp_args, tp_args)

tests/utils.py

@@ -10,6 +10,7 @@
 import openai
 import ray
 import requests
+from transformers import AutoTokenizer
 
 from vllm.distributed import (ensure_model_parallel_initialized,
                               init_distributed_environment)
@@ -124,6 +125,99 @@ def get_async_client(self):
         )
 
 
+def compare_two_settings(model: str, arg1: List[str], arg2: List[str]):
+    """
+    Launch API server with two different sets of arguments and compare the
+    results of the API calls. The arguments are after the model name.
+    """
+
+    tokenizer = AutoTokenizer.from_pretrained(model)
+
+    prompt = "Hello, my name is"
+    token_ids = tokenizer(prompt)["input_ids"]
+    results = []
+    for args in (arg1, arg2):
+        with RemoteOpenAIServer(model, args) as server:
+            client = server.get_client()
+
+            # test models list
+            models = client.models.list()
+            models = models.data
+            served_model = models[0]
+            results.append({
+                "test": "models_list",
+                "id": served_model.id,
+                "root": served_model.root,
+            })
+
+            # test with text prompt
+            completion = client.completions.create(model=model,
+                                                   prompt=prompt,
+                                                   max_tokens=5,
+                                                   temperature=0.0)
+
+            results.append({
+                "test": "single_completion",
+                "text": completion.choices[0].text,
+                "finish_reason": completion.choices[0].finish_reason,
+                "usage": completion.usage,
+            })
+
+            # test using token IDs
+            completion = client.completions.create(
+                model=model,
+                prompt=token_ids,
+                max_tokens=5,
+                temperature=0.0,
+            )
+
+            results.append({
+                "test": "token_ids",
+                "text": completion.choices[0].text,
+                "finish_reason": completion.choices[0].finish_reason,
+                "usage": completion.usage,
+            })
+
+            # test simple list
+            batch = client.completions.create(
+                model=model,
+                prompt=[prompt, prompt],
+                max_tokens=5,
+                temperature=0.0,
+            )
+
+            results.append({
+                "test": "simple_list",
+                "text0": batch.choices[0].text,
+                "text1": batch.choices[1].text,
+            })
+
+            # test streaming
+            batch = client.completions.create(
+                model=model,
+                prompt=[prompt, prompt],
+                max_tokens=5,
+                temperature=0.0,
+                stream=True,
+            )
+            texts = [""] * 2
+            for chunk in batch:
+                assert len(chunk.choices) == 1
+                choice = chunk.choices[0]
+                texts[choice.index] += choice.text
+            results.append({
+                "test": "streaming",
+                "texts": texts,
+            })
+
+    n = len(results) // 2
+    arg1_results = results[:n]
+    arg2_results = results[n:]
+    for arg1_result, arg2_result in zip(arg1_results, arg2_results):
+        assert arg1_result == arg2_result, \
+            f"Results for {model=} are not the same with {arg1=} and {arg2=}"
+
+
 def init_test_distributed_environment(
     tp_size: int,
     pp_size: int,