[CI] Prune tests/models/decoder_only/language/* tests (#9940)

Signed-off-by: mgoin <michael@neuralmagic.com>

tests/models/decoder_only/language/test_models.py

@@ -7,25 +7,39 @@ Run `pytest tests/models/test_models.py`.
 """
 import pytest
 
-from ...utils import check_outputs_equal
+from vllm.platforms import current_platform
+
+from ...utils import check_logprobs_close
 
 MODELS = [
-    "facebook/opt-125m",
-    "gpt2",
-    "bigcode/tiny_starcoder_py",
-    "EleutherAI/pythia-70m",
-    "bigscience/bloom-560m",  # Testing alibi slopes.
-    "microsoft/phi-2",
-    "stabilityai/stablelm-3b-4e1t",
-    # "allenai/OLMo-1B",  # Broken
-    "bigcode/starcoder2-3b",
-    "google/gemma-1.1-2b-it",
+    "facebook/opt-125m",  # opt
+    "openai-community/gpt2",  # gpt2
+    # "Milos/slovak-gpt-j-405M",  # gptj
+    # "bigcode/tiny_starcoder_py",  # gpt_bigcode
+    # "EleutherAI/pythia-70m",  # gpt_neox
+    "bigscience/bloom-560m",  # bloom - testing alibi slopes
+    "microsoft/phi-2",  # phi
+    # "stabilityai/stablelm-3b-4e1t",  # stablelm
+    # "bigcode/starcoder2-3b",  # starcoder2
+    "google/gemma-1.1-2b-it",  # gemma
+    "Qwen/Qwen2.5-0.5B-Instruct",  # qwen2
+    "meta-llama/Llama-3.2-1B-Instruct",  # llama
 ]
 
+if not current_platform.is_cpu():
+    MODELS += [
+        # fused_moe which not supported on CPU
+        "openbmb/MiniCPM3-4B",
+    ]
+
+# TODO: remove this after CPU float16 support ready
+target_dtype = "float" if current_platform.is_cpu() else "half"
+
 
 @pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["float"])
-@pytest.mark.parametrize("max_tokens", [96])
+@pytest.mark.parametrize("dtype", [target_dtype])
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("num_logprobs", [5])
 def test_models(
     hf_runner,
     vllm_runner,
@@ -33,33 +47,24 @@ def test_models(
     model: str,
     dtype: str,
     max_tokens: int,
+    num_logprobs: int,
 ) -> None:
-    # To pass the small model tests, we need full precision.
-    assert dtype == "float"
 
     with hf_runner(model, dtype=dtype) as hf_model:
-        hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
+        hf_outputs = hf_model.generate_greedy_logprobs_limit(
+            example_prompts, max_tokens, num_logprobs)
 
     with vllm_runner(model, dtype=dtype) as vllm_model:
-        vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+        vllm_outputs = vllm_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+        # 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(
+    check_logprobs_close(
         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)