Sync from v0.13

2026-01-19 10:38:50 +08:00
parent b2ef04d792
commit 5aef6c175a
3714 changed files with 854317 additions and 89342 deletions
--- a/tests/models/quantization/test_awq.py
+++ b/tests/models/quantization/test_awq.py
@@ -0,0 +1,137 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+
+import pytest
+import torch
+
+from vllm.multimodal.image import rescale_image_size
+
+from ...conftest import IMAGE_ASSETS, ImageTestAssets, VllmRunner
+from ..utils import check_logprobs_close
+
+HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts(
+    {
+        "stop_sign": "<|im_start|>User\n<image>\nWhat's the content in the center of the image?<|im_end|>\n<|im_start|>Assistant\n",  # noqa: E501
+        "cherry_blossom": "<|im_start|>User\n<image>\nWhat is the season?<|im_end|>\n<|im_start|>Assistant\n",  # noqa: E501
+    }
+)
+
+
+def run_awq_test(
+    vllm_runner: type[VllmRunner],
+    image_assets: ImageTestAssets,
+    source_model: str,
+    quant_model: str,
+    *,
+    size_factors: list[float],
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+    tensor_parallel_size: int,
+    distributed_executor_backend: str | None = None,
+):
+    images = [asset.pil_image for asset in image_assets]
+
+    inputs_per_image = [
+        (
+            [prompt for _ in size_factors],
+            [rescale_image_size(image, factor) for factor in size_factors],
+        )
+        for image, prompt in zip(images, HF_IMAGE_PROMPTS)
+    ]
+
+    # NOTE: take care of the order. run vLLM first, and then run HF.
+    # vLLM needs a fresh new process without cuda initialization.
+    # if we run HF first, the cuda initialization will be done and it
+    # will hurt multiprocessing backend with fork method (the default method).
+
+    # max_model_len should be greater than image_feature_size
+    with vllm_runner(
+        source_model,
+        max_model_len=4096,
+        dtype=dtype,
+        tensor_parallel_size=tensor_parallel_size,
+        distributed_executor_backend=distributed_executor_backend,
+        enforce_eager=True,
+        default_torch_num_threads=1,
+    ) as vllm_model:
+        source_outputs_per_image = [
+            vllm_model.generate_greedy_logprobs(
+                prompts, max_tokens, num_logprobs=num_logprobs, images=images
+            )
+            for prompts, images in inputs_per_image
+        ]
+
+    with vllm_runner(
+        quant_model,
+        quantization="awq",
+        max_model_len=4096,
+        dtype=dtype,
+        tensor_parallel_size=tensor_parallel_size,
+        distributed_executor_backend=distributed_executor_backend,
+        enforce_eager=True,
+        default_torch_num_threads=1,
+    ) as vllm_model:
+        quant_outputs_per_image = [
+            vllm_model.generate_greedy_logprobs(
+                prompts, max_tokens, num_logprobs=num_logprobs, images=images
+            )
+            for prompts, images in inputs_per_image
+        ]
+
+    for source_outputs, quant_outputs in zip(
+        source_outputs_per_image, quant_outputs_per_image
+    ):
+        # TODO: Check whether using original CLIPVisionModel can improve
+        # consistency against HF
+        check_logprobs_close(
+            outputs_0_lst=source_outputs,
+            outputs_1_lst=quant_outputs,
+            name_0="source",
+            name_1="awq",
+        )
+
+
+@pytest.mark.parametrize(
+    ("source_model", "quant_model"),
+    [("OpenGVLab/InternVL2-2B", "OpenGVLab/InternVL2-2B-AWQ")],
+)
+@pytest.mark.parametrize(
+    "size_factors",
+    [
+        # No image
+        [],
+        # Single-scale
+        [1.0],
+        # Single-scale, batched
+        [1.0, 1.0, 1.0],
+        # Multi-scale
+        [0.25, 0.5, 1.0],
+    ],
+)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("num_logprobs", [5])
+@torch.inference_mode()
+def test_awq_models(
+    vllm_runner,
+    image_assets,
+    source_model,
+    quant_model,
+    size_factors,
+    dtype,
+    max_tokens,
+    num_logprobs,
+) -> None:
+    run_awq_test(
+        vllm_runner,
+        image_assets,
+        source_model,
+        quant_model,
+        size_factors=size_factors,
+        dtype=dtype,
+        max_tokens=max_tokens,
+        num_logprobs=num_logprobs,
+        tensor_parallel_size=1,
+    )