[CI/Build] Reorganize models tests (#17459)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

tests/models/quantization/test_gptq_marlin_24.py

@@ -0,0 +1,73 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Compare the outputs of a GPTQ model to a Marlin_24 model.
+
+Note: GPTQ and Marlin_24 do not have bitwise correctness.
+As a result, in this test, we just confirm that the top selected tokens of the
+Marlin/GPTQ models are in the top 3 selections of each other.
+
+Run `pytest tests/models/test_marlin_24.py`.
+"""
+from dataclasses import dataclass
+
+import pytest
+
+from tests.quantization.utils import is_quant_method_supported
+
+from ..utils import check_logprobs_close
+
+
+@dataclass
+class ModelPair:
+    model_marlin: str
+    model_gptq: str
+
+
+model_pairs = [
+    # 4-bit, group_size == 128
+    ModelPair(model_marlin="alexm-nm/tinyllama-24-marlin24-4bit-g128",
+              model_gptq="alexm-nm/tinyllama-24-gptq-4bit-g128"),
+    # # 4-bit, group_size == channelwise
+    # ModelPair(model_marlin="alexm-nm/tinyllama-24-marlin24-4bit-channelwise",
+    #           model_gptq="alexm-nm/tinyllama-24-gptq-4bit-channelwise"),
+
+    # 8-bit, group_size == 128
+    ModelPair(model_marlin="alexm-nm/tinyllama-24-marlin24-8bit-g128",
+              model_gptq="alexm-nm/tinyllama-24-gptq-8bit-g128"),
+    # # 8-bit, group_size == channelwise
+    # ModelPair(model_marlin="alexm-nm/tinyllama-24-marlin24-8bit-channelwise",
+    #           model_gptq="alexm-nm/tinyllama-24-gptq-8bit-channelwise"),
+]
+
+
+@pytest.mark.flaky(reruns=2)
+@pytest.mark.skipif(not is_quant_method_supported("gptq_marlin_24"),
+                    reason="Marlin24 is not supported on this GPU type.")
+@pytest.mark.parametrize("model_pair", model_pairs)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [8])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_models(
+    vllm_runner,
+    example_prompts,
+    model_pair: ModelPair,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+) -> None:
+    with vllm_runner(model_pair.model_marlin,
+                     dtype=dtype,
+                     quantization="gptq_marlin_24") as marlin_24_model:
+        marlin_24_outputs = marlin_24_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+
+    with vllm_runner(model_pair.model_gptq, dtype=dtype,
+                     quantization="gptq") as gptq_model:
+        gptq_outputs = gptq_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+
+    check_logprobs_close(
+        outputs_0_lst=gptq_outputs,
+        outputs_1_lst=marlin_24_outputs,
+        name_0="gptq",
+        name_1="marlin_24",
+    )