[CI] Prune tests/models/decoder_only/language/* tests

tests/models/decoder_only/language/test_big_models.py

@@ -56,6 +56,10 @@ def test_models(
         with vllm_runner(model, dtype=dtype, enforce_eager=True) as vllm_model:
             vllm_outputs = vllm_model.generate_greedy_logprobs(
                 example_prompts, max_tokens, NUM_LOG_PROBS)
+            # This test is for verifying whether the model's extra_repr
+            # can be printed correctly.
+            print(vllm_model.model.llm_engine.model_executor.driver_worker.
+                model_runner.model)
 
         check_logprobs_close(
             outputs_0_lst=hf_outputs,
@@ -70,24 +74,14 @@ def test_models(
         with vllm_runner(model, dtype=dtype, enforce_eager=True) as vllm_model:
             vllm_outputs = vllm_model.generate_greedy(example_prompts,
                                                       max_tokens)
+            # This test is for verifying whether the model's extra_repr
+            # can be printed correctly.
+            print(vllm_model.model.llm_engine.model_executor.driver_worker.
+                model_runner.model)
 
         check_outputs_equal(
             outputs_0_lst=hf_outputs,
             outputs_1_lst=vllm_outputs,
             name_0="hf",
             name_1="vllm",
         )
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", [target_dtype])
-def test_model_print(
-    vllm_runner,
-    model: str,
-    dtype: str,
-) -> None:
-    with vllm_runner(model, dtype=dtype, enforce_eager=True) as vllm_model:
-        # This test is for verifying whether the model's extra_repr
-        # can be printed correctly.
-        print(vllm_model.model.llm_engine.model_executor.driver_worker.
-              model_runner.model)

tests/models/decoder_only/language/test_fp8.py

@@ -21,19 +21,19 @@
     "kv_cache_dtype,base_model,test_model,scale_path",
     [
         # Test FP8 checkpoint w. fp8_e4m3 kv-cache scaling factors.
-        ("fp8_e4m3", "meta-llama/Meta-Llama-3-8B-Instruct",
-         "nm-testing/Meta-Llama-3-8B-Instruct-FP8-KV", None),
+        ("fp8_e4m3", "meta-llama/Llama-3.2-1B-Instruct",
+         "nm-testing/Llama-3.2-1B-Instruct-FP8-KV", None),
         # Test FP16 checkpoint w. fp8_e5m2 kv-cache.
-        ("fp8_e5m2", "meta-llama/Meta-Llama-3-8B-Instruct",
-         "meta-llama/Meta-Llama-3-8B-Instruct", None),
+        ("fp8_e5m2", "meta-llama/Llama-3.2-1B-Instruct",
+         "meta-llama/Llama-3.2-1B-Instruct", None),
         # Test FP16 checkpoint w. fp8_e4m3 kv-cache scaling factors in json.
         ("fp8_e4m3", "meta-llama/Llama-2-7b-chat-hf",
          "meta-llama/Llama-2-7b-chat-hf",
          "./tests/fp8_kv/llama2-7b-fp8-kv/kv_cache_scales.json")
     ])
 # Due to low-precision numerical divergence, we only test logprob of 4 tokens
 @pytest.mark.parametrize("max_tokens", [4])
-@pytest.mark.parametrize("enforce_eager", [False, True])
+@pytest.mark.parametrize("enforce_eager", [True])
 @pytest.mark.parametrize("backend", ["FLASH_ATTN", "XFORMERS", "FLASHINFER"])
 # NOTE: Increasing this in this suite will fail CI because we currently cannot
 # reset distributed env properly. Use a value > 1 just when you test.

tests/models/decoder_only/language/test_marlin.py

@@ -26,10 +26,6 @@ class ModelPair:
 
 
 model_pairs = [
-    ModelPair(model_marlin="nm-testing/zephyr-beta-7b-marlin-g128",
-              model_gptq="nm-testing/zephyr-beta-7b-gptq-g128"),
-    ModelPair(model_marlin="robertgshaw2/zephyr-7b-beta-channelwise-marlin",
-              model_gptq="robertgshaw2/zephyr-7b-beta-channelwise-gptq"),
     ModelPair(model_marlin="robertgshaw2/TinyLlama-1.1B-Chat-v1.0-g128-marlin",
               model_gptq="robertgshaw2/TinyLlama-1.1B-Chat-v1.0-g128-gptq")
 ]

tests/models/decoder_only/language/test_mistral.py

@@ -4,7 +4,7 @@
 """
 import pytest
 
 from vllm import LLM, SamplingParams
 
 from ...utils import check_logprobs_close
 
@@ -15,6 +15,10 @@
     # "mistralai/Mistral-Nemo-Instruct-2407"
 ]
 
+MISTRAL_FORMAT_MODELS = [
+    "mistralai/Mistral-7B-Instruct-v0.3",
+]
+
 SAMPLING_PARAMS = SamplingParams(max_tokens=512, temperature=0.0, logprobs=5)
 SYMBOLIC_LANG_PROMPTS = [
     "勇敢な船乗りについての詩を書く",  # japanese
@@ -95,7 +99,7 @@
     )
 
 
-@pytest.mark.parametrize("model", MODELS[1:])
+@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
 @pytest.mark.parametrize("dtype", ["bfloat16"])
 @pytest.mark.parametrize("max_tokens", [64])
 @pytest.mark.parametrize("num_logprobs", [5])
@@ -135,28 +139,28 @@
     )
 
 
-@pytest.mark.parametrize("model", MODELS[1:])
+@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
 @pytest.mark.parametrize("dtype", ["bfloat16"])
-@pytest.mark.parametrize("prompt", SYMBOLIC_LANG_PROMPTS)
 def test_mistral_symbolic_languages(
+    vllm_runner,
     model: str,
     dtype: str,
-    prompt: str,
 ) -> None:
-    prompt = "hi"
-    msg = {"role": "user", "content": prompt}
-    llm = LLM(model=model,
-              dtype=dtype,
-              max_model_len=8192,
-              tokenizer_mode="mistral",
-              config_format="mistral",
-              load_format="mistral")
-    outputs = llm.chat([msg], sampling_params=SAMPLING_PARAMS)
-    assert "�" not in outputs[0].outputs[0].text.strip()
+    with vllm_runner(model,
+                    dtype=dtype,
+                    max_model_len=8192,
+                    tokenizer_mode="mistral",
+                    config_format="mistral",
+                    load_format="mistral") as vllm_model:
+        for prompt in SYMBOLIC_LANG_PROMPTS:
+            msg = {"role": "user", "content": prompt}
+            outputs = vllm_model.model.chat([msg],
+                                            sampling_params=SAMPLING_PARAMS)
+            assert "�" not in outputs[0].outputs[0].text.strip()
 
 
 @pytest.mark.parametrize("dtype", ["bfloat16"])
-@pytest.mark.parametrize("model", MODELS[1:])  # v1 can't do func calling
+@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)  # v1 can't do func calling
 def test_mistral_function_calling(
     vllm_runner,
     model: str,

tests/models/decoder_only/language/test_models.py

@@ -42,24 +42,14 @@ def test_models(
 
     with vllm_runner(model, dtype=dtype) as vllm_model:
         vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+        # This test is for verifying whether the model's extra_repr
+        # can be printed correctly.
+        print(vllm_model.model.llm_engine.model_executor.driver_worker.
+              model_runner.model)
 
     check_outputs_equal(
         outputs_0_lst=hf_outputs,
         outputs_1_lst=vllm_outputs,
         name_0="hf",
         name_1="vllm",
     )
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["float"])
-def test_model_print(
-    vllm_runner,
-    model: str,
-    dtype: str,
-) -> None:
-    with vllm_runner(model, dtype=dtype) as vllm_model:
-        # This test is for verifying whether the model's extra_repr
-        # can be printed correctly.
-        print(vllm_model.model.llm_engine.model_executor.driver_worker.
-              model_runner.model)