Sync from v0.13

tests/weight_loading/test_weight_loading.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import os
+
+import pytest
+import torch
+
+from vllm.platforms import current_platform
+
+MAX_MODEL_LEN = 1024
+MODEL_NAME = os.environ.get(
+    "MODEL_NAME", "robertgshaw2/zephyr-7b-beta-channelwise-gptq"
+)
+REVISION = os.environ.get("REVISION", "main")
+QUANTIZATION = os.environ.get("QUANTIZATION", "gptq_marlin")
+MIN_CAPABILITY = os.environ.get("MIN_CAPABILITY", "80")
+
+
+@pytest.mark.skipif(
+    MODEL_NAME == "casperhansen/deepseek-coder-v2-instruct-awq", reason="OOM in the CI"
+)
+@pytest.mark.skipif(
+    not current_platform.has_device_capability(int(MIN_CAPABILITY)),
+    reason="Current system does not have minimum capability.",
+)
+def test_weight_loading(vllm_runner):
+    """
+    Test parameter weight loading with tp>1.
+    """
+
+    # MoE models need fp16.
+    NEEDS_FP16 = (
+        QUANTIZATION == "gptq"
+        or MODEL_NAME == "nm-testing/test-w4a16-mixtral-actorder-group"
+    )
+    with vllm_runner(
+        model_name=MODEL_NAME,
+        revision=REVISION,
+        dtype=torch.half if NEEDS_FP16 else "auto",
+        quantization=None if QUANTIZATION == "None" else QUANTIZATION,
+        max_model_len=MAX_MODEL_LEN,
+        tensor_parallel_size=2,
+    ) as model:
+        output = model.generate_greedy("Hello world!", max_tokens=20)
+        print(output)
+        assert output