init

tests/models/test_marlin.py

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+"""Compare the outputs of a GPTQ model to a Marlin model.
+
+Note: GPTQ and Marlin 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.
+
+Note: Marlin internally uses locks to synchronize the threads. This can
+result in very slight nondeterminism for Marlin. As a result, we re-run the test
+up to 3 times to see if we pass.
+
+Run `pytest tests/models/test_marlin.py`.
+"""
+from dataclasses import dataclass
+
+import pytest
+import torch
+
+from tests.models.utils import check_logprobs_close
+from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
+
+capability = torch.cuda.get_device_capability()
+capability = capability[0] * 10 + capability[1]
+marlin_not_supported = (capability <
+                        QUANTIZATION_METHODS["marlin"].get_min_capability())
+
+
+@dataclass
+class ModelPair:
+    model_marlin: str
+    model_gptq: str
+
+
+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")
+]
+
+
+@pytest.mark.flaky(reruns=2)
+@pytest.mark.skipif(marlin_not_supported,
+                    reason="Marlin is not supported on this GPU type.")
+@pytest.mark.parametrize("model_pair", model_pairs)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [32])
+@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:
+    marlin_model = vllm_runner(model_pair.model_marlin,
+                               dtype=dtype,
+                               quantization="marlin")
+    marlin_outputs = marlin_model.generate_greedy_logprobs(
+        example_prompts, max_tokens, num_logprobs)
+    del marlin_model
+
+    gptq_model = vllm_runner(model_pair.model_gptq,
+                             dtype=dtype,
+                             quantization="gptq")
+    gptq_outputs = gptq_model.generate_greedy_logprobs(example_prompts,
+                                                       max_tokens,
+                                                       num_logprobs)
+    del gptq_model
+
+    check_logprobs_close(
+        outputs_0_lst=gptq_outputs,
+        outputs_1_lst=marlin_outputs,
+        name_0="gptq",
+        name_1="marlin",
+    )