[Test] Remove VLLM_USE_V1 in example and tests (#1733)

V1 is enabled by default, no need to set it by hand now. This PR remove
the useless setting in example and tests

- vLLM version: v0.9.2
- vLLM main:
9ad0a4588b

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_correctness.py

@@ -53,7 +53,6 @@ def model_name():
 @pytest.mark.skipif(
     True, reason="TODO: Enable me after test_mtp_correctness is fixed")
 def test_mtp_correctness(
-    monkeypatch: pytest.MonkeyPatch,
     test_prompts: list[list[dict[str, Any]]],
     sampling_config: SamplingParams,
     model_name: str,
@@ -62,33 +61,30 @@ def test_mtp_correctness(
     Compare the outputs of a original LLM and a speculative LLM
     should be the same when using mtp speculative decoding.
     '''
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
+    ref_llm = LLM(model=model_name, max_model_len=256, enforce_eager=True)
+    ref_outputs = ref_llm.chat(test_prompts, sampling_config)
+    del ref_llm
 
-        ref_llm = LLM(model=model_name, max_model_len=256, enforce_eager=True)
-        ref_outputs = ref_llm.chat(test_prompts, sampling_config)
-        del ref_llm
+    spec_llm = LLM(model=model_name,
+                   trust_remote_code=True,
+                   speculative_config={
+                       "method": "deepseek_mtp",
+                       "num_speculative_tokens": 1,
+                   },
+                   max_model_len=256,
+                   enforce_eager=True)
+    spec_outputs = spec_llm.chat(test_prompts, sampling_config)
+    matches = 0
+    misses = 0
+    for ref_output, spec_output in zip(ref_outputs, spec_outputs):
+        if ref_output.outputs[0].text == spec_output.outputs[0].text:
+            matches += 1
+        else:
+            misses += 1
+            print(f"ref_output: {ref_output.outputs[0].text}")
+            print(f"spec_output: {spec_output.outputs[0].text}")
 
-        spec_llm = LLM(model=model_name,
-                       trust_remote_code=True,
-                       speculative_config={
-                           "method": "deepseek_mtp",
-                           "num_speculative_tokens": 1,
-                       },
-                       max_model_len=256,
-                       enforce_eager=True)
-        spec_outputs = spec_llm.chat(test_prompts, sampling_config)
-        matches = 0
-        misses = 0
-        for ref_output, spec_output in zip(ref_outputs, spec_outputs):
-            if ref_output.outputs[0].text == spec_output.outputs[0].text:
-                matches += 1
-            else:
-                misses += 1
-                print(f"ref_output: {ref_output.outputs[0].text}")
-                print(f"spec_output: {spec_output.outputs[0].text}")
-
-        # Heuristic: expect at least 66% of the prompts to match exactly
-        # Upon failure, inspect the outputs to check for inaccuracy.
-        assert matches > int(0.66 * len(ref_outputs))
-        del spec_llm
+    # Heuristic: expect at least 66% of the prompts to match exactly
+    # Upon failure, inspect the outputs to check for inaccuracy.
+    assert matches > int(0.66 * len(ref_outputs))
+    del spec_llm

tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py

@@ -60,7 +60,6 @@ def eagle3_model_name():
 
 
 def test_ngram_correctness(
-    monkeypatch: pytest.MonkeyPatch,
     test_prompts: list[list[dict[str, Any]]],
     sampling_config: SamplingParams,
     model_name: str,
@@ -70,44 +69,40 @@ def test_ngram_correctness(
     should be the same when using ngram speculative decoding.
     '''
     pytest.skip("Not current support for the test.")
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
+    ref_llm = LLM(model=model_name, max_model_len=1024, enforce_eager=True)
+    ref_outputs = ref_llm.chat(test_prompts, sampling_config)
+    del ref_llm
 
-        ref_llm = LLM(model=model_name, max_model_len=1024, enforce_eager=True)
-        ref_outputs = ref_llm.chat(test_prompts, sampling_config)
-        del ref_llm
+    spec_llm = LLM(
+        model=model_name,
+        speculative_config={
+            "method": "ngram",
+            "prompt_lookup_max": 5,
+            "prompt_lookup_min": 3,
+            "num_speculative_tokens": 3,
+        },
+        max_model_len=1024,
+        enforce_eager=True,
+    )
+    spec_outputs = spec_llm.chat(test_prompts, sampling_config)
+    matches = 0
+    misses = 0
+    for ref_output, spec_output in zip(ref_outputs, spec_outputs):
+        if ref_output.outputs[0].text == spec_output.outputs[0].text:
+            matches += 1
+        else:
+            misses += 1
+            print(f"ref_output: {ref_output.outputs[0].text}")
+            print(f"spec_output: {spec_output.outputs[0].text}")
 
-        spec_llm = LLM(
-            model=model_name,
-            speculative_config={
-                "method": "ngram",
-                "prompt_lookup_max": 5,
-                "prompt_lookup_min": 3,
-                "num_speculative_tokens": 3,
-            },
-            max_model_len=1024,
-            enforce_eager=True,
-        )
-        spec_outputs = spec_llm.chat(test_prompts, sampling_config)
-        matches = 0
-        misses = 0
-        for ref_output, spec_output in zip(ref_outputs, spec_outputs):
-            if ref_output.outputs[0].text == spec_output.outputs[0].text:
-                matches += 1
-            else:
-                misses += 1
-                print(f"ref_output: {ref_output.outputs[0].text}")
-                print(f"spec_output: {spec_output.outputs[0].text}")
-
-        # Heuristic: expect at least 70% of the prompts to match exactly
-        # Upon failure, inspect the outputs to check for inaccuracy.
-        assert matches > int(0.7 * len(ref_outputs))
-        del spec_llm
+    # Heuristic: expect at least 70% of the prompts to match exactly
+    # Upon failure, inspect the outputs to check for inaccuracy.
+    assert matches > int(0.7 * len(ref_outputs))
+    del spec_llm
 
 
 @pytest.mark.parametrize("use_eagle3", [False, True], ids=["eagle", "eagle3"])
 def test_eagle_correctness(
-    monkeypatch: pytest.MonkeyPatch,
     test_prompts: list[list[dict[str, Any]]],
     sampling_config: SamplingParams,
     model_name: str,
@@ -119,43 +114,40 @@ def test_eagle_correctness(
     '''
     if not use_eagle3:
         pytest.skip("Not current support for the test.")
-    with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1")
 
-        ref_llm = LLM(model=model_name, max_model_len=2048, enforce_eager=True)
-        ref_outputs = ref_llm.chat(test_prompts, sampling_config)
-        del ref_llm
+    ref_llm = LLM(model=model_name, max_model_len=2048, enforce_eager=True)
+    ref_outputs = ref_llm.chat(test_prompts, sampling_config)
+    del ref_llm
 
-        spec_model_name = eagle3_model_name(
-        ) if use_eagle3 else eagle_model_name()
-        spec_llm = LLM(
-            model=model_name,
-            trust_remote_code=True,
-            enable_chunked_prefill=True,
-            max_num_seqs=1,
-            max_num_batched_tokens=2048,
-            gpu_memory_utilization=0.6,
-            speculative_config={
-                "method": "eagle3" if use_eagle3 else "eagle",
-                "model": spec_model_name,
-                "num_speculative_tokens": 2,
-                "max_model_len": 128,
-            },
-            max_model_len=128,
-            enforce_eager=True,
-        )
-        spec_outputs = spec_llm.chat(test_prompts, sampling_config)
-        matches = 0
-        misses = 0
-        for ref_output, spec_output in zip(ref_outputs, spec_outputs):
-            if ref_output.outputs[0].text == spec_output.outputs[0].text:
-                matches += 1
-            else:
-                misses += 1
-                print(f"ref_output: {ref_output.outputs[0].text}")
-                print(f"spec_output: {spec_output.outputs[0].text}")
+    spec_model_name = eagle3_model_name() if use_eagle3 else eagle_model_name()
+    spec_llm = LLM(
+        model=model_name,
+        trust_remote_code=True,
+        enable_chunked_prefill=True,
+        max_num_seqs=1,
+        max_num_batched_tokens=2048,
+        gpu_memory_utilization=0.6,
+        speculative_config={
+            "method": "eagle3" if use_eagle3 else "eagle",
+            "model": spec_model_name,
+            "num_speculative_tokens": 2,
+            "max_model_len": 128,
+        },
+        max_model_len=128,
+        enforce_eager=True,
+    )
+    spec_outputs = spec_llm.chat(test_prompts, sampling_config)
+    matches = 0
+    misses = 0
+    for ref_output, spec_output in zip(ref_outputs, spec_outputs):
+        if ref_output.outputs[0].text == spec_output.outputs[0].text:
+            matches += 1
+        else:
+            misses += 1
+            print(f"ref_output: {ref_output.outputs[0].text}")
+            print(f"spec_output: {spec_output.outputs[0].text}")
 
-        # Heuristic: expect at least 66% of the prompts to match exactly
-        # Upon failure, inspect the outputs to check for inaccuracy.
-        assert matches > int(0.66 * len(ref_outputs))
-        del spec_llm
+    # Heuristic: expect at least 66% of the prompts to match exactly
+    # Upon failure, inspect the outputs to check for inaccuracy.
+    assert matches > int(0.66 * len(ref_outputs))
+    del spec_llm