Merge tag 'v0.10.0' into v0.10.0-dev

tests/multimodal/test_utils.py

@@ -41,7 +41,7 @@ TEST_IMAGE_URLS = [
 
 TEST_VIDEO_URLS = [
     "https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4",
-    "https://filesamples.com/samples/video/avi/sample_640x360.avi",
+    "https://github.com/opencv/opencv/raw/refs/tags/4.12.0/samples/data/vtest.avi",
 ]
 
 

tests/multimodal/test_video.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import tempfile
+from pathlib import Path
+
 import numpy as np
 import numpy.typing as npt
 import pytest
+from PIL import Image
 
-from vllm import envs
+from vllm.assets.base import get_vllm_public_assets
+from vllm.assets.video import video_to_ndarrays, video_to_pil_images_list
 from vllm.multimodal.image import ImageMediaIO
 from vllm.multimodal.video import (VIDEO_LOADER_REGISTRY, VideoLoader,
                                    VideoMediaIO)
 
+from .utils import cosine_similarity, create_video_from_image, normalize_image
+
 NUM_FRAMES = 10
 FAKE_OUTPUT_1 = np.random.rand(NUM_FRAMES, 1280, 720, 3)
 FAKE_OUTPUT_2 = np.random.rand(NUM_FRAMES, 1280, 720, 3)
@@ -59,30 +67,79 @@ class Assert10Frames1FPSVideoLoader(VideoLoader):
         return FAKE_OUTPUT_2
 
 
-def test_video_media_io_kwargs():
-    envs.VLLM_VIDEO_LOADER_BACKEND = "assert_10_frames_1_fps"
-    imageio = ImageMediaIO()
+def test_video_media_io_kwargs(monkeypatch: pytest.MonkeyPatch):
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_VIDEO_LOADER_BACKEND", "assert_10_frames_1_fps")
+        imageio = ImageMediaIO()
 
-    # Verify that different args pass/fail assertions as expected.
-    videoio = VideoMediaIO(imageio, **{"num_frames": 10, "fps": 1.0})
-    _ = videoio.load_bytes(b"test")
-
-    videoio = VideoMediaIO(
-        imageio, **{
-            "num_frames": 10,
-            "fps": 1.0,
-            "not_used": "not_used"
-        })
-    _ = videoio.load_bytes(b"test")
-
-    with pytest.raises(AssertionError, match="bad num_frames"):
-        videoio = VideoMediaIO(imageio, **{})
+        # Verify that different args pass/fail assertions as expected.
+        videoio = VideoMediaIO(imageio, **{"num_frames": 10, "fps": 1.0})
         _ = videoio.load_bytes(b"test")
 
-    with pytest.raises(AssertionError, match="bad num_frames"):
-        videoio = VideoMediaIO(imageio, **{"num_frames": 9, "fps": 1.0})
+        videoio = VideoMediaIO(
+            imageio, **{
+                "num_frames": 10,
+                "fps": 1.0,
+                "not_used": "not_used"
+            })
         _ = videoio.load_bytes(b"test")
 
-    with pytest.raises(AssertionError, match="bad fps"):
-        videoio = VideoMediaIO(imageio, **{"num_frames": 10, "fps": 2.0})
-        _ = videoio.load_bytes(b"test")
+        with pytest.raises(AssertionError, match="bad num_frames"):
+            videoio = VideoMediaIO(imageio, **{})
+            _ = videoio.load_bytes(b"test")
+
+        with pytest.raises(AssertionError, match="bad num_frames"):
+            videoio = VideoMediaIO(imageio, **{"num_frames": 9, "fps": 1.0})
+            _ = videoio.load_bytes(b"test")
+
+        with pytest.raises(AssertionError, match="bad fps"):
+            videoio = VideoMediaIO(imageio, **{"num_frames": 10, "fps": 2.0})
+            _ = videoio.load_bytes(b"test")
+
+
+@pytest.mark.parametrize("is_color", [True, False])
+@pytest.mark.parametrize("fourcc, ext", [("mp4v", "mp4"), ("XVID", "avi")])
+def test_opencv_video_io_colorspace(is_color: bool, fourcc: str, ext: str):
+    """
+    Test all functions that use OpenCV for video I/O return RGB format.
+    Both RGB and grayscale videos are tested.
+    """
+    image_path = get_vllm_public_assets(filename="stop_sign.jpg",
+                                        s3_prefix="vision_model_images")
+    image = Image.open(image_path)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        if not is_color:
+            image_path = f"{tmpdir}/test_grayscale_image.png"
+            image = image.convert("L")
+            image.save(image_path)
+            # Convert to gray RGB for comparison
+            image = image.convert("RGB")
+        video_path = f"{tmpdir}/test_RGB_video.{ext}"
+        create_video_from_image(
+            image_path,
+            video_path,
+            num_frames=2,
+            is_color=is_color,
+            fourcc=fourcc,
+        )
+
+        frames = video_to_ndarrays(video_path)
+        for frame in frames:
+            sim = cosine_similarity(normalize_image(np.array(frame)),
+                                    normalize_image(np.array(image)))
+            assert np.sum(np.isnan(sim)) / sim.size < 0.001
+            assert np.nanmean(sim) > 0.99
+
+        pil_frames = video_to_pil_images_list(video_path)
+        for frame in pil_frames:
+            sim = cosine_similarity(normalize_image(np.array(frame)),
+                                    normalize_image(np.array(image)))
+            assert np.sum(np.isnan(sim)) / sim.size < 0.001
+            assert np.nanmean(sim) > 0.99
+
+        io_frames, _ = VideoMediaIO(ImageMediaIO()).load_file(Path(video_path))
+        for frame in io_frames:
+            sim = cosine_similarity(normalize_image(np.array(frame)),
+                                    normalize_image(np.array(image)))
+            assert np.sum(np.isnan(sim)) / sim.size < 0.001
+            assert np.nanmean(sim) > 0.99

tests/multimodal/utils.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import cv2
 import numpy as np
+import numpy.typing as npt
 from PIL import Image
 
 
@@ -31,3 +33,47 @@ def random_audio(
 ):
     audio_len = rng.randint(min_len, max_len)
     return rng.rand(audio_len), sr
+
+
+def create_video_from_image(
+    image_path: str,
+    video_path: str,
+    num_frames: int = 10,
+    fps: float = 1.0,
+    is_color: bool = True,
+    fourcc: str = "mp4v",
+):
+    image = cv2.imread(image_path)
+    if not is_color:
+        # Convert to grayscale if is_color is False
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        height, width = image.shape
+    else:
+        height, width, _ = image.shape
+
+    video_writer = cv2.VideoWriter(
+        video_path,
+        cv2.VideoWriter_fourcc(*fourcc),
+        fps,
+        (width, height),
+        isColor=is_color,
+    )
+
+    for _ in range(num_frames):
+        video_writer.write(image)
+
+    video_writer.release()
+    return video_path
+
+
+def cosine_similarity(A: npt.NDArray,
+                      B: npt.NDArray,
+                      axis: int = -1) -> npt.NDArray:
+    """Compute cosine similarity between two vectors."""
+    return (np.sum(A * B, axis=axis) /
+            (np.linalg.norm(A, axis=axis) * np.linalg.norm(B, axis=axis)))
+
+
+def normalize_image(image: npt.NDArray) -> npt.NDArray:
+    """Normalize image to [0, 1] range."""
+    return image.astype(np.float32) / 255.0
\ No newline at end of file

tests/neuron/2_core/test_mistral.py

@@ -9,7 +9,6 @@ def test_mistral():
               tensor_parallel_size=2,
               max_num_seqs=4,
               max_model_len=128,
-              use_v2_block_manager=True,
               override_neuron_config={
                   "sequence_parallel_enabled": False,
                   "skip_warmup": True

tests/neuron/2_core/test_multi_lora.py

@@ -14,7 +14,6 @@ def test_llama_single_lora():
               tensor_parallel_size=2,
               max_num_seqs=4,
               max_model_len=512,
-              use_v2_block_manager=True,
               override_neuron_config={
                   "sequence_parallel_enabled": False,
                   "skip_warmup": True,
@@ -57,7 +56,6 @@ def test_llama_multiple_lora():
               tensor_parallel_size=2,
               max_num_seqs=4,
               max_model_len=512,
-              use_v2_block_manager=True,
               override_neuron_config={
                   "sequence_parallel_enabled":
                   False,

tests/plugins/vllm_add_dummy_model/vllm_add_dummy_model/my_gemma_embedding.py

@@ -8,14 +8,16 @@ import torch
 import torch.nn as nn
 
 from vllm.config import VllmConfig
-from vllm.model_executor.layers.pooler import Pooler, PoolingType
+from vllm.model_executor.layers.pooler import DispatchPooler, Pooler
 from vllm.model_executor.models.gemma2 import Gemma2Model
 from vllm.model_executor.models.utils import WeightsMapper, maybe_prefix
-from vllm.model_executor.pooling_metadata import PoolingMetadata
-from vllm.sequence import IntermediateTensors, PoolerOutput
+from vllm.sequence import IntermediateTensors
 
 
 class MyGemma2Embedding(nn.Module):
+
+    is_pooling_model = True
+
     hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={"model.": ""})
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
@@ -24,12 +26,13 @@ class MyGemma2Embedding(nn.Module):
         self.model = Gemma2Model(vllm_config=vllm_config,
                                  prefix=maybe_prefix(prefix, "model"))
 
-        self._pooler = Pooler.from_config_with_defaults(
-            vllm_config.model_config.pooler_config,
-            pooling_type=PoolingType.LAST,
-            normalize=True,
-            softmax=False,
-        )
+        pooler_config = vllm_config.model_config.pooler_config
+        assert pooler_config is not None
+
+        self.pooler = DispatchPooler({
+            "encode": Pooler.for_encode(pooler_config),
+            "embed": Pooler.for_embed(pooler_config),
+        })
 
         self.make_empty_intermediate_tensors = (
             self.model.make_empty_intermediate_tensors)
@@ -54,13 +57,6 @@ class MyGemma2Embedding(nn.Module):
         # Return all-zero embeddings
         return torch.zeros_like(hidden_states)
 
-    def pooler(
-        self,
-        hidden_states: torch.Tensor,
-        pooling_metadata: PoolingMetadata,
-    ) -> Optional[PoolerOutput]:
-        return self._pooler(hidden_states, pooling_metadata)
-
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
 
         weights = self.hf_to_vllm_mapper.apply(weights)

tests/prefix_caching/test_disable_sliding_window.py

@@ -27,25 +27,25 @@ MODEL_LEN_LEN = [
 @pytest.mark.parametrize("model_len_len", MODEL_LEN_LEN)
 def test_disable_sliding_window(model_len_len, ):
     model, sliding_len, full_len = model_len_len
-    vllm_disabled_model = LLM(model, disable_sliding_window=True)
-    vllm_disabled_model.generate("Hi my name is")
-    model_config = vllm_disabled_model.llm_engine.model_config
+    disabled_llm = LLM(model, disable_sliding_window=True)
+    disabled_llm.generate("Hi my name is")
+    model_config = disabled_llm.llm_engine.model_config
     assert model_config.max_model_len == sliding_len, (
         "Max len expected to equal sliding_len of %s, but got %s", sliding_len,
         model_config.max_model_len)
 
-    del vllm_disabled_model
+    del disabled_llm
     cleanup_dist_env_and_memory()
 
-    vllm_enabled_model = LLM(model,
-                             enforce_eager=True,
-                             disable_sliding_window=False,
-                             enable_prefix_caching=False)
-    vllm_enabled_model.generate("Hi my name is")
-    model_config = vllm_enabled_model.llm_engine.model_config
+    enabled_llm = LLM(model,
+                      enforce_eager=True,
+                      disable_sliding_window=False,
+                      enable_prefix_caching=False)
+    enabled_llm.generate("Hi my name is")
+    model_config = enabled_llm.llm_engine.model_config
     assert model_config.max_model_len == full_len, (
         "Max len expected to equal full_len of %s, but got %s", full_len,
         model_config.max_model_len)
 
-    del vllm_enabled_model
+    del enabled_llm
     cleanup_dist_env_and_memory()

tests/prefix_caching/test_prefix_caching.py

@@ -96,8 +96,8 @@ def test_mixed_requests(
             # Run all the promopts
             greedy_params = SamplingParams(temperature=0.0,
                                            max_tokens=max_tokens)
-            req_outputs = vllm_model.model.generate(example_prompts,
-                                                    greedy_params)
+            req_outputs = vllm_model.llm.generate(example_prompts,
+                                                  greedy_params)
 
             # Verify number of cached tokens
             for i in range(len(req_outputs)):
@@ -164,7 +164,7 @@ def test_fully_cached_prefill_needs_uncached_token(model):
         max_num_batched_tokens=max_num_batched_tokens,
         max_num_seqs=max_num_batched_tokens,
     )
-    engine: LLMEngine = runner.model.llm_engine
+    engine: LLMEngine = runner.llm.llm_engine
 
     scheduler: Scheduler = SchedulerProxy(engine.scheduler[0])  # type: ignore
     engine.scheduler[0] = scheduler

tests/quantization/reference_mxfp4.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+
+BFLOAT16_EXP_BIAS = 127
+BFLOAT16_MANTISSA_BITS = 7
+BFLOAT16_EXP_BITS = 8
+
+FLOAT16_EXP_BIAS = 15
+FLOAT16_MANTISSA_BITS = 10
+FLOAT16_EXP_BITS = 5
+
+FLOAT8_E8M0_MAX_EXP = 127
+FLOAT4_EXP_BIAS = 1
+FLOAT4_MANTISSA_BITS = 1
+
+FLOAT16_VAL_TO_ADD = (1 << (FLOAT16_MANTISSA_BITS - FLOAT4_MANTISSA_BITS - 1))
+FLOAT16_SIGN_EXPONENT_MASK = ((
+    (1 << (FLOAT16_EXP_BITS + 1)) - 1) << FLOAT16_MANTISSA_BITS)
+
+BFLOAT16_VAL_TO_ADD = (1 <<
+                       (BFLOAT16_MANTISSA_BITS - FLOAT4_MANTISSA_BITS - 1))
+BFLOAT16_SIGN_EXPONENT_MASK = ((
+    (1 << (BFLOAT16_EXP_BITS + 1)) - 1) << BFLOAT16_MANTISSA_BITS)
+
+
+def e8m0_to_half(scale, half_dtype: torch.dtype):
+    assert scale.dtype == torch.uint8
+
+    scale_exp = scale.to(torch.int16) - 127
+
+    # This can be implemented with bitwise operations in a proper kernel.
+    scale_half = 2.0**(scale_exp.to(torch.float))
+
+    return scale_half.to(half_dtype)
+
+
+def upcast_fp4_to_fp16_or_bf16(val, float_dtype: torch.dtype,
+                               half_exp_bias: int, half_mantissa_bits: int):
+    assert val.dtype == torch.uint8
+
+    unpacked = torch.zeros(*val.shape[:-1],
+                           val.shape[-1] * 2,
+                           dtype=torch.uint8,
+                           device=val.device)
+    unpacked[..., 1::2] = (val >> 4) & 0x0F  # Extract high 4 bits.
+    unpacked[..., ::2] = val & 0x0F  # Extract low 4 bits.
+
+    # Takes one float4 values represented as b0000xxxx,
+    # and converts it to the corresponding float16 value.
+
+    sign = unpacked >> 3
+
+    exp = (unpacked >> 1) & 3
+    new_mantissa = unpacked & 1
+
+    # if exp == 0 and new_mantissa == 0:
+    #     new_exp = 0
+    # else:
+    #     new_exp = exp - FLOAT4_EXP_BIAS + FLOAT16_EXP_BIAS
+
+    # int8_t works with float16, but may overflow with bfloat16.
+    new_exp = exp - FLOAT4_EXP_BIAS + half_exp_bias
+
+    # Cast b0000 to 0. in fp16/bf16.
+    new_exp = new_exp * torch.logical_or(exp > 0, new_mantissa > 0)
+
+    # Cast b0001 to 0.5 in fp16/bf16.
+    new_mantissa = torch.logical_and(new_mantissa, exp > 0)
+
+    new_mantissa = new_mantissa.to(torch.int32)
+    new_exp = new_exp.to(torch.int32)
+    sign = sign.to(torch.int32)
+
+    qdq_val = (sign << 15) + (new_exp << half_mantissa_bits) + (
+        new_mantissa << (half_mantissa_bits - 1))
+
+    assert qdq_val.max() <= 65535
+    assert qdq_val.min() >= 0
+    qdq_val = qdq_val.to(torch.uint16)
+
+    result = qdq_val.view(float_dtype)
+
+    return result
+
+
+def dq_mxfp4_torch(x: torch.Tensor, scale: torch.Tensor,
+                   float_dtype: torch.dtype) -> torch.Tensor:
+    assert x.dtype == torch.uint8
+    assert scale.dtype == torch.uint8
+
+    if float_dtype == torch.float16:
+        half_exp_bias = FLOAT16_EXP_BIAS
+        half_mantissa_bits = FLOAT16_MANTISSA_BITS
+    elif float_dtype == torch.bfloat16:
+        half_exp_bias = BFLOAT16_EXP_BIAS
+        half_mantissa_bits = BFLOAT16_MANTISSA_BITS
+
+    scale_half = e8m0_to_half(scale, half_dtype=float_dtype)
+
+    x_half = upcast_fp4_to_fp16_or_bf16(x,
+                                        float_dtype=float_dtype,
+                                        half_exp_bias=half_exp_bias,
+                                        half_mantissa_bits=half_mantissa_bits)
+
+    x_half = x_half.reshape(*x_half.shape[:-1], -1, 32)
+    x_half = x_half * scale_half[..., None]
+    x_half = x_half.reshape(*x_half.shape[:-2], -1)
+
+    return x_half
+
+
+def fp16_to_fp4_simulate(val, half_mantissa_bits: int, half_exp_bits: int,
+                         half_exp_bias: int):
+    # Casts an fp16/bf16 input to the restricted values of float4_e2m1,
+    # that is to say [0., 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0, -0.0,
+    # -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0].
+
+    float_type = val.dtype
+
+    # "rshift_cuda" not implemented for 'UInt16'
+    val_view = val.view(torch.int16)  #.to(torch.int32)
+
+    exp = val_view >> half_mantissa_bits
+    exp = exp & ((1 << half_exp_bits) - 1)
+
+    exp = exp.view(torch.uint16).to(torch.int32)
+
+    sign = (val_view >> (half_mantissa_bits + half_exp_bits)) & 1
+
+    mantissa_last = (val_view >> (half_mantissa_bits - 1)) & 1
+
+    exp_unbias = exp - half_exp_bias
+    new_exp = exp_unbias + FLOAT4_EXP_BIAS
+
+    exp_shift = (new_exp <= 0) * (1 - new_exp)
+
+    # Typically 9.
+    # Take the min to prevent overflow on `uint16_t half`. This is the case for
+    # very small values, correctly mapped to `round_close`.
+    tail_bits = half_mantissa_bits - FLOAT4_MANTISSA_BITS + exp_shift
+    tail_bits[tail_bits >= 16] = 16
+
+    mantissa_plus_one = val_view & ((1 << (half_mantissa_bits + 1)) - 1)
+
+    half = 1 << (tail_bits - 1)
+
+    tail = mantissa_plus_one & ((1 << tail_bits) - 1)
+
+    round_close = (tail < half)  # round towards 0
+    round_away = (tail > half)  # round away from 0
+    tie = tail == half
+
+    new_mantissa_close = torch.zeros(val.shape,
+                                     device=val.device,
+                                     dtype=torch.bool)
+    new_exp_close = torch.zeros(val.shape,
+                                device=val.device,
+                                dtype=torch.uint16)
+
+    new_mantissa_away = torch.zeros(val.shape,
+                                    device=val.device,
+                                    dtype=torch.bool)
+    new_exp_away = torch.zeros(val.shape,
+                               device=val.device,
+                               dtype=torch.uint16)
+
+    new_exp_tie = torch.zeros(val.shape, device=val.device, dtype=torch.uint16)
+
+    # 1. round down
+    # if new_exp == 0: # case [0.5, 0.749999]
+    #     new_mantissa = 0
+    # elif new_exp < 0:  # case [0, 0.24999]
+    #     new_mantissa = 0
+    # else:
+    #     new_mantissa = mantissa_last
+
+    new_mantissa_close = (new_exp > 0) * mantissa_last
+    new_exp_close = exp
+
+    # # 2. round up
+    # if new_exp <= 0:  # case [0.250001, 0.499999] and [0.75001, 0.99999]
+    #     new_mantissa = 0
+    #     new_exp += 1
+    # elif mantissa_last == 0:
+    #     new_mantissa = 1
+    # else:
+    #     new_mantissa = 0
+    #     new_exp += 1
+
+    new_mantissa_away = torch.logical_and(new_exp > 0, mantissa_last == 0)
+    new_exp_away = exp + torch.logical_or(new_exp <= 0, mantissa_last == 1)
+
+    # # 3. tie
+    # 0.25 -> 0. (handled by `exp > (half_exp_bias - 2)`)
+    # 0.75 -> 1.
+    # 1.25 -> 1.
+    # 1.75 -> 2.
+    # 2.5 -> 2.
+    # 3.5 -> 4.
+    # 5. -> 4.
+    new_exp_tie = (exp > (half_exp_bias - 2)) * (exp + (mantissa_last == 1))
+
+    # Gather round up, round down and tie.
+    new_exp = round_away * new_exp_away \
+        + round_close * new_exp_close \
+        + tie * new_exp_tie
+
+    new_mantissa = round_away * new_mantissa_away \
+        + round_close * new_mantissa_close
+
+    # if new_exp > 3:
+    #     new_mantissa = 1
+    new_mantissa = new_mantissa + (new_exp >
+                                   (2 + half_exp_bias)) * (new_mantissa == 0)
+
+    # Clamp the exponent to acceptable values.
+    new_exp = (new_exp >= (half_exp_bias - 2)) * torch.clamp(
+        new_exp, half_exp_bias - 2, half_exp_bias + 2)
+
+    sign = sign.to(torch.int32)
+    new_mantissa = new_mantissa.to(torch.int32)
+
+    qdq_val = (sign << 15) + (new_exp << half_mantissa_bits) + (
+        new_mantissa << (half_mantissa_bits - 1))
+
+    assert qdq_val.max() <= 65535
+    assert qdq_val.min() >= 0
+    assert qdq_val.dtype == torch.int32
+    qdq_val = qdq_val.to(torch.uint16)
+
+    result = qdq_val.view(float_type)
+    return result
+
+
+def qdq_mxfp4_torch(x: torch.Tensor,
+                    scale_calculation_mode: str = "even") -> torch.Tensor:
+    half_dtype = x.dtype
+
+    if half_dtype == torch.float16:
+        half_mantissa_bits = FLOAT16_MANTISSA_BITS
+        half_exp_bits = FLOAT16_EXP_BITS
+        half_exp_bias = FLOAT16_EXP_BIAS
+        val_to_add = FLOAT16_VAL_TO_ADD
+        sign_exponent_mask = FLOAT16_SIGN_EXPONENT_MASK
+    elif half_dtype == torch.bfloat16:
+        half_mantissa_bits = BFLOAT16_MANTISSA_BITS
+        half_exp_bits = BFLOAT16_EXP_BITS
+        half_exp_bias = BFLOAT16_EXP_BIAS
+        val_to_add = BFLOAT16_VAL_TO_ADD
+        sign_exponent_mask = BFLOAT16_SIGN_EXPONENT_MASK
+    else:
+        raise ValueError("not implemented")
+
+    x = x.reshape(*x.shape[:-1], -1, 32)
+
+    block_max = torch.max(torch.abs(x), dim=-1).values
+
+    block_max = block_max.view(torch.uint16).to(torch.int32)
+
+    block_max_uint = torch.bitwise_and(block_max + val_to_add,
+                                       sign_exponent_mask)
+
+    assert block_max_uint.max() <= 65535
+    assert block_max_uint.min() >= 0
+    assert block_max_uint.dtype == torch.int32
+    block_max_uint = block_max_uint.to(torch.uint16)
+
+    block_max = block_max_uint.view(half_dtype)
+
+    scale_exp = FLOAT8_E8M0_MAX_EXP + torch.floor(torch.log2(block_max)).to(
+        torch.int32) - 2
+
+    scale_exp = torch.clamp(scale_exp, 0, 2 * FLOAT8_E8M0_MAX_EXP)
+
+    scale = 2.0**(scale_exp - FLOAT8_E8M0_MAX_EXP)
+    scale = scale.to(half_dtype)
+
+    x = x / scale[..., None]
+
+    x_fp4 = fp16_to_fp4_simulate(x,
+                                 half_exp_bits=half_exp_bits,
+                                 half_mantissa_bits=half_mantissa_bits,
+                                 half_exp_bias=half_exp_bias)
+
+    x_fp4 = x_fp4 * scale[..., None]
+    return x_fp4.reshape(*x_fp4.shape[:-2], -1)

tests/quantization/test_compressed_tensors.py

@@ -48,7 +48,8 @@ def use_v0_only(monkeypatch):
     """
     This module relies on V0 internals, so set VLLM_USE_V1=0.
     """
-    monkeypatch.setenv('VLLM_USE_V1', '0')
+    if not current_platform.is_cpu():
+        monkeypatch.setenv('VLLM_USE_V1', '0')
 
 
 @pytest.mark.parametrize(

tests/quantization/test_gptq_dynamic.py

@@ -41,7 +41,7 @@ def test_gptq_with_dynamic(vllm_runner, model_id: str, use_marlin_kernel: bool,
     linear_method_cls = GPTQMarlinLinearMethod if use_marlin_kernel else (
         GPTQLinearMethod)
 
-    for name, submodule in (vllm_model.model.llm_engine.model_executor.
+    for name, submodule in (vllm_model.llm.llm_engine.model_executor.
                             driver_worker.model_runner.model.named_modules()):
         if name == "lm_head":
             assert isinstance(submodule.quant_method, linear_method_cls)

tests/quantization/test_modelopt.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Test ModelOpt quantization method setup and weight loading.
+
+Run `pytest tests/quantization/test_modelopt.py`.
+"""
+
+import os
+
+import pytest
+import torch
+
+from tests.quantization.utils import is_quant_method_supported
+from vllm.platforms import current_platform
+
+
+@pytest.fixture(scope="function", autouse=True)
+def use_v0_only(monkeypatch):
+    """
+    This module relies on V0 internals, so set VLLM_USE_V1=0.
+    """
+    if not current_platform.is_cpu():
+        monkeypatch.setenv('VLLM_USE_V1', '0')
+
+
+@pytest.mark.skipif(not is_quant_method_supported("modelopt"),
+                    reason="ModelOpt FP8 is not supported on this GPU type.")
+def test_modelopt_fp8_checkpoint_setup(vllm_runner):
+    """Test ModelOpt FP8 checkpoint loading and structure validation."""
+    # TODO: provide a small publically available test checkpoint
+    model_path = ("/home/scratch.omniml_data_1/zhiyu/ckpts/test_ckpts/"
+                  "TinyLlama-1.1B-Chat-v1.0-fp8-0710")
+
+    # Skip test if checkpoint doesn't exist
+    if not os.path.exists(model_path):
+        pytest.skip(f"Test checkpoint not found at {model_path}. "
+                    "This test requires a local ModelOpt FP8 checkpoint.")
+
+    with vllm_runner(model_path, quantization="modelopt",
+                     enforce_eager=True) as llm:
+
+        def check_model(model):
+            layer = model.model.layers[0]
+
+            qkv_proj = layer.self_attn.qkv_proj
+            o_proj = layer.self_attn.o_proj
+            gate_up_proj = layer.mlp.gate_up_proj
+            down_proj = layer.mlp.down_proj
+
+            # Check that ModelOpt quantization method is properly applied
+            from vllm.model_executor.layers.quantization.modelopt import (
+                ModelOptFp8LinearMethod)
+            assert isinstance(qkv_proj.quant_method, ModelOptFp8LinearMethod)
+            assert isinstance(o_proj.quant_method, ModelOptFp8LinearMethod)
+            assert isinstance(gate_up_proj.quant_method,
+                              ModelOptFp8LinearMethod)
+            assert isinstance(down_proj.quant_method, ModelOptFp8LinearMethod)
+
+            # Check weight dtype is FP8
+            assert qkv_proj.weight.dtype == torch.float8_e4m3fn
+            assert o_proj.weight.dtype == torch.float8_e4m3fn
+            assert gate_up_proj.weight.dtype == torch.float8_e4m3fn
+            assert down_proj.weight.dtype == torch.float8_e4m3fn
+
+            # Check scales are present and have correct dtype
+            assert hasattr(qkv_proj, 'weight_scale')
+            assert hasattr(qkv_proj, 'input_scale')
+            assert qkv_proj.weight_scale.dtype == torch.float32
+            assert qkv_proj.input_scale.dtype == torch.float32
+
+            assert hasattr(o_proj, 'weight_scale')
+            assert hasattr(o_proj, 'input_scale')
+            assert o_proj.weight_scale.dtype == torch.float32
+            assert o_proj.input_scale.dtype == torch.float32
+
+            assert hasattr(gate_up_proj, 'weight_scale')
+            assert hasattr(gate_up_proj, 'input_scale')
+            assert gate_up_proj.weight_scale.dtype == torch.float32
+            assert gate_up_proj.input_scale.dtype == torch.float32
+
+            assert hasattr(down_proj, 'weight_scale')
+            assert hasattr(down_proj, 'input_scale')
+            assert down_proj.weight_scale.dtype == torch.float32
+            assert down_proj.input_scale.dtype == torch.float32
+
+        llm.apply_model(check_model)
+
+        # Run a simple generation test to ensure the model works
+        output = llm.generate_greedy(["Hello my name is"], max_tokens=20)
+        assert output
+        print(f"ModelOpt FP8 output: {output}")

tests/quantization/test_register_quantization_config.py

@@ -113,7 +113,7 @@ def test_custom_quant(vllm_runner, model, monkeypatch):
                      quantization="custom_quant",
                      enforce_eager=True) as llm:
 
-        model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model  # noqa: E501
+        model = llm.llm.llm_engine.model_executor.driver_worker.model_runner.model  # noqa: E501
         layer = model.model.layers[0]
         qkv_proj = layer.self_attn.qkv_proj
 

tests/quantization/untest_quark.py

@@ -3,15 +3,45 @@
 """Test model set-up and weight loading for quark-quantized models.
 
 Run `pytest tests/quantization/test_quark.py`.
+
+See also `tests/kernels/moe/test_mxfp4_moe.py`.
 """
 
+import importlib
+import importlib.metadata
+import os
+from dataclasses import dataclass
+
+import huggingface_hub
+import lm_eval
 import pytest
 import torch
+from packaging import version
 
 from vllm.model_executor.layers.quantization.quark.quark import (  # noqa: E501
     QuarkLinearMethod, QuarkW8A8Fp8, QuarkW8A8Int8)
 from vllm.platforms import current_platform
 
+from .reference_mxfp4 import dq_mxfp4_torch, qdq_mxfp4_torch
+from ..utils import models_path_prefix
+
+QUARK_MXFP4_AVAILABLE = importlib.util.find_spec(
+    "quark") is not None and version.parse(
+        importlib.metadata.version("amd-quark")) >= version.parse('0.8.99')
+
+if QUARK_MXFP4_AVAILABLE:
+    from quark.torch.export.nn.modules.realquantizer import (
+        StaticScaledRealQuantizer)
+    from quark.torch.kernel import mx as mx_kernel
+    from quark.torch.quantization.config.config import FP4PerGroupSpec
+
+try:
+    huggingface_hub.list_repo_refs(
+        "amd/Llama-3.3-70B-Instruct-WMXFP4-AMXFP4-KVFP8-Scale-UINT8-SQ")
+    HF_HUB_AMD_ORG_ACCESS = True
+except huggingface_hub.errors.RepositoryNotFoundError:
+    HF_HUB_AMD_ORG_ACCESS = False
+
 
 @pytest.fixture(scope="function", autouse=True)
 def use_v0_only(monkeypatch):
@@ -24,7 +54,7 @@ def use_v0_only(monkeypatch):
 @pytest.mark.parametrize('kv_cache_dtype', ['auto', 'fp8'])
 @pytest.mark.parametrize('tp', [1])
 def test_quark_fp8_w_per_tensor_a_per_tensor(vllm_runner, kv_cache_dtype, tp):
-    model_path = "amd/Llama-3.1-8B-Instruct-FP8-KV-Quark-test"
+    model_path = os.path.join(models_path_prefix, "amd/Llama-3.1-8B-Instruct-FP8-KV-Quark-test")
     with vllm_runner(model_path,
                      kv_cache_dtype=kv_cache_dtype,
                      tensor_parallel_size=tp) as llm:
@@ -68,8 +98,8 @@ def test_quark_int8_w_per_tensor_a_per_tensor(vllm_runner, tp):
 
 
 def test_quark_fp8_parity(vllm_runner):
-    quark_model_id = "amd-quark/llama-tiny-fp8-quark-quant-method"
-    fp8_model_id = "amd-quark/llama-tiny-fp8-quant-method"
+    quark_model_id = os.path.join(models_path_prefix, "amd-quark/llama-tiny-fp8-quark-quant-method")
+    fp8_model_id = os.path.join(models_path_prefix, "amd-quark/llama-tiny-fp8-quant-method")
 
     llm_kwargs = {
         "tensor_parallel_size": 1,
@@ -78,15 +108,157 @@ def test_quark_fp8_parity(vllm_runner):
     }
     with (vllm_runner(quark_model_id, **llm_kwargs) as
           quark_handle, vllm_runner(fp8_model_id, **llm_kwargs) as fp8_handle):
-        quark_model = (quark_handle.model.llm_engine.model_executor.
+        quark_model = (quark_handle.llm.llm_engine.model_executor.
                        driver_worker.model_runner.model)
         quark_state_dict = quark_model.state_dict()
 
-        fp8_model = (fp8_handle.model.llm_engine.model_executor.driver_worker.
+        fp8_model = (fp8_handle.llm.llm_engine.model_executor.driver_worker.
                      model_runner.model)
         fp8_state_dict = fp8_model.state_dict()
 
     assert fp8_state_dict.keys() == quark_state_dict.keys()
 
     for key in fp8_state_dict:
-        assert torch.equal(fp8_state_dict[key], quark_state_dict[key])
\ No newline at end of file
+        assert torch.equal(fp8_state_dict[key], quark_state_dict[key])
+
+
+@dataclass
+class ModelCase:
+    model_id: str
+    tp: int
+
+
+@dataclass
+class GSM8KAccuracyTestConfig:
+    model_name: str
+    excepted_value: float
+
+    def get_model_args(self) -> str:
+        return (
+            f"pretrained={self.model_name},"
+            "dtype=auto,add_bos_token=True,tensor_parallel_size=8,gpu_memory_utilization=0.7,max_model_len=38768"
+        )
+
+
+ACCURACY_CONFIGS = [
+    # Private model.
+    GSM8KAccuracyTestConfig(
+        model_name=os.path.join(models_path_prefix, "amd/DeepSeek-R1-WMXFP4-AMXFP4-Scale-UINT8-MoE-Quant"),
+        excepted_value=0.96),
+]
+
+
+@pytest.mark.parametrize("config", ACCURACY_CONFIGS)
+@pytest.mark.skipif(not QUARK_MXFP4_AVAILABLE,
+                    reason="amd-quark>=0.9 is not available")
+@pytest.mark.skipif(
+    not HF_HUB_AMD_ORG_ACCESS,
+    reason="Read access to huggingface.co/amd is required for this test.")
+def test_mxfp4_gsm8k_correctness(config: GSM8KAccuracyTestConfig):
+    if torch.cuda.device_count() < 8:
+        pytest.skip(
+            f"This test requires >=8 gpus, got only {torch.cuda.device_count()}"
+        )
+
+    task = "gsm8k"
+    rtol = 0.03
+
+    os.environ["VLLM_USE_TRITON_FLASH_ATTN"] = "0"
+
+    results = lm_eval.simple_evaluate(
+        model="vllm",
+        model_args=config.get_model_args(),
+        tasks=task,
+        batch_size=64,
+        num_fewshot=8,
+    )
+
+    EXPECTED_VALUE = config.excepted_value
+    measured_value = results["results"][task]["exact_match,strict-match"]
+    assert (measured_value - rtol < EXPECTED_VALUE
+            and measured_value + rtol > EXPECTED_VALUE
+            ), f"Expected: {EXPECTED_VALUE} |  Measured: {measured_value}"
+
+    del os.environ["VLLM_USE_TRITON_FLASH_ATTN"]
+
+
+@pytest.mark.skipif(not QUARK_MXFP4_AVAILABLE,
+                    reason="amd-quark>=0.9 is not available")
+@pytest.mark.parametrize("float_dtype", [torch.bfloat16, torch.float16])
+@pytest.mark.parametrize("scalings",
+                         [[2.3, 0.03, 7.3, 0.1, 0.004, 17.3, 1e4, 1e-4]])
+def test_mxfp4_fused_qdq_match_quark(float_dtype: torch.dtype,
+                                     scalings: list[int]):
+    torch.manual_seed(0)
+
+    hidden_size = 64 * 32
+    inp = (torch.rand(1, hidden_size, dtype=float_dtype, device="cuda") -
+           0.5) * 2
+    for i in range(hidden_size // 32):
+        inp[:, i * 32:(i + 1) *
+            32] = inp[:, i * 32:(i + 1) * 32] * scalings[i % len(scalings)]
+
+    inp_kernel = inp.clone()
+    inp_kernel_clone = inp_kernel.clone()
+
+    res_hip = mx_kernel.qdq_mxfp4_hip(inp_kernel_clone, "even")
+    res_torch = qdq_mxfp4_torch(inp_kernel, "even")
+
+    for i in range(hidden_size // 32):
+        assert torch.all(torch.isfinite(res_hip[:, i * 32:(i + 1) * 32]))
+        assert torch.all(torch.isfinite(res_torch[:, i * 32:(i + 1) * 32]))
+
+        torch.testing.assert_close(res_hip[:, i * 32:(i + 1) * 32],
+                                   res_torch[:, i * 32:(i + 1) * 32])
+
+
+@pytest.mark.skipif(not QUARK_MXFP4_AVAILABLE,
+                    reason="amd-quark>=0.9 is not available")
+@pytest.mark.parametrize("float_dtype", [torch.bfloat16, torch.float16])
+@pytest.mark.parametrize("scalings",
+                         [[2.3, 0.03, 7.3, 0.1, 0.004, 17.3, 1e4, 1e-4]])
+def test_mxfp4_dequant_kernel_match_quark(float_dtype: torch.dtype,
+                                          scalings: list[int]):
+    qspec = FP4PerGroupSpec(
+        ch_axis=-1,
+        group_size=32,
+        scale_format="e8m0",
+        scale_calculation_mode="even",
+        is_dynamic=False,
+    ).to_quantization_spec()
+
+    weight_quantizer = StaticScaledRealQuantizer(
+        qspec=qspec,
+        quantizer=None,
+        reorder=False,
+        real_quantized=True,
+        float_dtype=float_dtype,
+        device="cuda",
+    )
+
+    observer = qspec.observer_cls(qspec, device="cuda")
+
+    hidden_size = 512
+    shape = (11008, hidden_size)
+
+    w = (torch.rand(shape, device="cuda", dtype=float_dtype) - 0.5) * 2
+
+    # Make it so that different groups have different scales.
+    for i in range(hidden_size // 32):
+        w[:, i * 32:(i + 1) *
+          32] = w[:, i * 32:(i + 1) * 32] * scalings[i % len(scalings)]
+
+    observer(w)
+    scale, _ = observer._calculate_qparams()
+    weight_quantizer.scale = scale
+
+    w_mxfp4 = weight_quantizer.to_real_quantize_params(w).to("cuda")
+    weight_quantizer.maybe_convert_and_transpose_scale()
+
+    scale = weight_quantizer.scale
+
+    out_hip = mx_kernel.dq_mxfp4_hip(w_mxfp4, scale, float_dtype)
+
+    out_torch = dq_mxfp4_torch(w_mxfp4, scale, float_dtype)
+
+    assert torch.equal(out_hip, out_torch)

tests/reasoning/test_hunyuan_reasoning_parser.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+from transformers import AutoTokenizer
+
+from tests.reasoning.utils import run_reasoning_extraction
+from vllm.reasoning import ReasoningParser, ReasoningParserManager
+
+parser_name = "hunyuan_a13b"
+START_REASONING = "<think>\n"
+START_RESPONSE = "\n</think>\n<answer>\n"
+END_RESPONSE = "\n</answer>"
+
+NO_REASONING_QUICK_THROUGHT = {
+    "output":
+    f"{START_REASONING}{START_RESPONSE}This is the rest{END_RESPONSE}",  #noqa: E501
+    "reasoning_content": None,
+    "content": "This is the rest",
+}
+
+SIMPLE_REASONING = {
+    "output":
+    f"{START_REASONING}This is a reasoning section{START_RESPONSE}This is the rest{END_RESPONSE}",  #noqa: E501
+    "reasoning_content": "This is a reasoning section",
+    "content": "This is the rest",
+}
+COMPLETE_REASONING = {
+    "output": f"{START_REASONING}This is a reasoning section{START_RESPONSE}",
+    "reasoning_content": "This is a reasoning section",
+    "content": None,
+}
+
+COMPLETE_REASONING_WITH_SYMBOL = {
+    "output": f"{START_REASONING}This is a reasoning section!{START_RESPONSE}",
+    "reasoning_content": "This is a reasoning section!",
+    "content": None,
+}
+NO_REASONING = {
+    "output": "This is content",
+    "reasoning_content": None,
+    "content": "This is content",
+}
+MULTIPLE_LINES = {
+    "output":
+    f"{START_REASONING}This\nThat{START_RESPONSE}This is the rest\nThat",
+    "reasoning_content": "This\nThat",
+    "content": "This is the rest\nThat",
+}
+REASONING_WITH_THINK = {
+    "output":
+    f"{START_REASONING}This is a reasoning section{START_RESPONSE}This is the rest",  #noqa: E501
+    "reasoning_content": "This is a reasoning section",
+    "content": "This is the rest",
+}
+COMPLETE_REASONING_WITH_THINK = {
+    "output": f"{START_REASONING}This is a reasoning section{START_RESPONSE}",
+    "reasoning_content": "This is a reasoning section",
+    "content": None,
+}
+MULTIPLE_LINES_WITH_THINK = {
+    "output":
+    f"{START_REASONING}This\nThat{START_RESPONSE}This is the rest\nThat",
+    "reasoning_content": "This\nThat",
+    "content": "This is the rest\nThat",
+}
+
+TEST_CASES = [
+    pytest.param(
+        False,
+        SIMPLE_REASONING,
+        id="simple_reasoning",
+    ),
+    pytest.param(
+        False,
+        COMPLETE_REASONING,
+        id="complete_reasoning",
+    ),
+    pytest.param(
+        False,
+        COMPLETE_REASONING_WITH_SYMBOL,
+        id="complete_reasoning_with_symbol",
+    ),
+    pytest.param(
+        False,
+        NO_REASONING,
+        id="no_reasoning",
+    ),
+    pytest.param(False, NO_REASONING_QUICK_THROUGHT, id="no_reasoning_quick"),
+    pytest.param(
+        False,
+        MULTIPLE_LINES,
+        id="multiple_lines",
+    ),
+    pytest.param(
+        False,
+        REASONING_WITH_THINK,
+        id="reasoning_with_think",
+    ),
+    pytest.param(
+        False,
+        COMPLETE_REASONING_WITH_THINK,
+        id="complete_reasoning_with_think",
+    ),
+    pytest.param(
+        False,
+        MULTIPLE_LINES_WITH_THINK,
+        id="multiple_lines_with_think",
+    ),
+    pytest.param(
+        True,
+        SIMPLE_REASONING,
+        id="simple_reasoning_streaming",
+    ),
+    pytest.param(
+        True,
+        COMPLETE_REASONING,
+        id="complete_reasoning_streaming",
+    ),
+    pytest.param(
+        True,
+        NO_REASONING,
+        id="no_reasoning_streaming",
+    ),
+    pytest.param(True,
+                 NO_REASONING_QUICK_THROUGHT,
+                 id="no_reasoning_quick_stream"),
+    pytest.param(
+        True,
+        MULTIPLE_LINES,
+        id="multiple_lines_streaming",
+    ),
+    pytest.param(
+        True,
+        REASONING_WITH_THINK,
+        id="reasoning_with_think_streaming",
+    ),
+    pytest.param(
+        True,
+        COMPLETE_REASONING_WITH_THINK,
+        id="complete_reasoning_with_think_streaming",
+    ),
+    pytest.param(
+        True,
+        MULTIPLE_LINES_WITH_THINK,
+        id="multiple_lines_with_think_streaming",
+    ),
+]
+
+# Global tokenizer initialization to avoid repeated loading
+tokenizer = AutoTokenizer.from_pretrained("tencent/Hunyuan-A13B-Instruct",
+                                          trust_remote_code=True)
+
+
+@pytest.mark.parametrize("streaming, param_dict", TEST_CASES)
+def test_reasoning(
+    streaming: bool,
+    param_dict: dict,
+):
+    output = tokenizer.tokenize(param_dict["output"])
+    # decode everything to tokens
+    output_tokens: list[str] = [
+        tokenizer.convert_tokens_to_string([token]) for token in output
+    ]
+    parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser(
+        parser_name)(tokenizer)
+
+    reasoning, content = run_reasoning_extraction(parser,
+                                                  output_tokens,
+                                                  streaming=streaming)
+
+    assert reasoning == param_dict["reasoning_content"]
+    assert content == param_dict["content"]

tests/reasoning/test_mistral_reasoning_parser.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+from mistral_common.tokens.tokenizers.base import SpecialTokens
+from mistral_common.tokens.tokenizers.tekken import (SpecialTokenInfo,
+                                                     Tekkenizer)
+
+from tests.reasoning.utils import run_reasoning_extraction_mistral
+from vllm.reasoning import ReasoningParser, ReasoningParserManager
+from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
+
+parser_name = "mistral"
+
+
+@pytest.fixture(scope="module")
+def mistral_tokenizer():
+    # TODO(Julien): upon model release change to a tokenizer already configured.
+    # =================================================================
+    mistral_tokenizer = MistralTokenizer.from_pretrained(
+        "mistralai/Devstral-Small-2507")
+    assert isinstance(mistral_tokenizer.tokenizer, Tekkenizer)
+    # Add think special tokens to the tokenizer
+    mistral_tokenizer.tokenizer._all_special_tokens[35] = SpecialTokenInfo(
+        rank=35, is_control=True, token_str=SpecialTokens.begin_think.value)
+    mistral_tokenizer.tokenizer._all_special_tokens[36] = SpecialTokenInfo(
+        rank=36, is_control=True, token_str=SpecialTokens.end_think.value)
+    mistral_tokenizer.tokenizer._special_tokens_reverse_vocab = {
+        k: v
+        for k, v in
+        mistral_tokenizer.tokenizer._special_tokens_reverse_vocab.items()
+        if v not in {35, 36}
+    }
+    mistral_tokenizer.tokenizer._special_tokens_reverse_vocab[
+        SpecialTokens.begin_think.value] = 35
+    mistral_tokenizer.tokenizer._special_tokens_reverse_vocab[
+        SpecialTokens.end_think.value] = 36
+    mistral_tokenizer.instruct.BEGIN_THINK = 35
+    mistral_tokenizer.instruct.END_THINK = 36
+    # =================================================================
+    return mistral_tokenizer
+
+
+SIMPLE_REASONING = {
+    "output": "This is a reasoning section[/THINK]This is the rest",
+    "reasoning_content": "This is a reasoning section",
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+COMPLETE_REASONING = {
+    "output": "This is a reasoning section[/THINK]",
+    "reasoning_content": "This is a reasoning section",
+    "content": None,
+    "is_reasoning_end": True,
+}
+NO_CONTENT = {
+    "output": "This is content",
+    "reasoning_content": "This is content",
+    "content": None,
+    "is_reasoning_end": False,
+}
+NO_REASONING_STREAMING = {
+    "output": "This is a reasoning section",
+    "reasoning_content": "This is a reasoning section",
+    "content": None,
+    "is_reasoning_end": False,
+}
+MULTIPLE_LINES = {
+    "output": "This\nThat[/THINK]This is the rest\nThat",
+    "reasoning_content": "This\nThat",
+    "content": "This is the rest\nThat",
+    "is_reasoning_end": True,
+}
+SHORTEST_REASONING_NO_STREAMING = {
+    "output": "[/THINK]This is the rest",
+    "reasoning_content": "",
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+SHORTEST_REASONING = {
+    "output": "[/THINK]This is the rest",
+    "reasoning_content": None,
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+REASONING_WITH_THINK = {
+    "output": "[THINK]This is a reasoning section[/THINK]This is the rest",
+    "reasoning_content": "This is a reasoning section",
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+COMPLETE_REASONING_WITH_THINK = {
+    "output": "[THINK]This is a reasoning section[/THINK]",
+    "reasoning_content": "This is a reasoning section",
+    "content": None,
+    "is_reasoning_end": True,
+}
+MULTIPLE_LINES_WITH_THINK = {
+    "output": "[THINK]This\nThat[/THINK]This is the rest\nThat",
+    "reasoning_content": "This\nThat",
+    "content": "This is the rest\nThat",
+    "is_reasoning_end": True,
+}
+SHORTEST_REASONING_NO_STREAMING_WITH_THINK = {
+    "output": "[/THINK]This is the rest",
+    "reasoning_content": "",
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+SHORTEST_REASONING_WITH_THINK = {
+    "output": "[/THINK]This is the rest",
+    "reasoning_content": None,
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+THINK_NO_END = {
+    "output": "[THINK]This is a reasoning section",
+    "reasoning_content": "This is a reasoning section",
+    "content": None,
+    "is_reasoning_end": False,
+}
+EMPTY = {
+    "output": "",
+    "reasoning_content": "",
+    "content": None,
+    "is_reasoning_end": False,
+}
+EMPTY_STREAMING = {
+    "output": "",
+    "reasoning_content": None,
+    "content": None,
+    "is_reasoning_end": False,
+}
+NEW_LINE = {
+    "output": "\n[THINK]This is a reasoning section[/THINK]\nThis is the rest",
+    "reasoning_content": "This is a reasoning section",
+    "content": "\nThis is the rest",
+    "is_reasoning_end": True,
+}
+# Streaming cannot handle new lines at the beginning of the output
+# because we need to support [THINK]...[/THINK] and [/THINK]...
+# We cannot know if the text before [THINK] is reasoning content
+# or not.
+NEW_LINE_STREAMING = {
+    "output": "\n[THINK]This is a reasoning section[/THINK]\nThis is the rest",
+    "reasoning_content": "\nThis is a reasoning section",
+    "content": "\nThis is the rest",
+    "is_reasoning_end": True,
+}
+
+TEST_CASES = [
+    pytest.param(
+        False,
+        SIMPLE_REASONING,
+        id="simple_reasoning",
+    ),
+    pytest.param(
+        True,
+        SIMPLE_REASONING,
+        id="simple_reasoning_streaming",
+    ),
+    pytest.param(
+        False,
+        COMPLETE_REASONING,
+        id="complete_reasoning",
+    ),
+    pytest.param(
+        True,
+        COMPLETE_REASONING,
+        id="complete_reasoning_streaming",
+    ),
+    pytest.param(
+        False,
+        NO_CONTENT,
+        id="no_content_token",
+    ),
+    pytest.param(
+        True,
+        NO_REASONING_STREAMING,
+        id="no_reasoning_token_streaming",
+    ),
+    pytest.param(
+        False,
+        MULTIPLE_LINES,
+        id="multiple_lines",
+    ),
+    pytest.param(
+        True,
+        MULTIPLE_LINES,
+        id="multiple_lines_streaming",
+    ),
+    pytest.param(
+        True,
+        SHORTEST_REASONING,
+        id="shortest",
+    ),
+    pytest.param(
+        False,
+        SHORTEST_REASONING_NO_STREAMING,
+        id="shortest_streaming",
+    ),
+    pytest.param(
+        False,
+        REASONING_WITH_THINK,
+        id="reasoning_with_think",
+    ),
+    pytest.param(
+        True,
+        REASONING_WITH_THINK,
+        id="reasoning_with_think_streaming",
+    ),
+    pytest.param(
+        False,
+        COMPLETE_REASONING_WITH_THINK,
+        id="complete_reasoning_with_think",
+    ),
+    pytest.param(
+        True,
+        COMPLETE_REASONING_WITH_THINK,
+        id="complete_reasoning_with_think_streaming",
+    ),
+    pytest.param(
+        False,
+        MULTIPLE_LINES_WITH_THINK,
+        id="multiple_lines_with_think",
+    ),
+    pytest.param(
+        True,
+        MULTIPLE_LINES_WITH_THINK,
+        id="multiple_lines_with_think_streaming",
+    ),
+    pytest.param(
+        False,
+        SHORTEST_REASONING_NO_STREAMING_WITH_THINK,
+        id="shortest_with_think",
+    ),
+    pytest.param(
+        True,
+        SHORTEST_REASONING_WITH_THINK,
+        id="shortest_with_think_streaming",
+    ),
+    pytest.param(
+        False,
+        THINK_NO_END,
+        id="think_no_end",
+    ),
+    pytest.param(
+        True,
+        THINK_NO_END,
+        id="think_no_end_streaming",
+    ),
+    pytest.param(
+        False,
+        EMPTY,
+        id="empty",
+    ),
+    pytest.param(
+        True,
+        EMPTY_STREAMING,
+        id="empty_streaming",
+    ),
+    pytest.param(
+        False,
+        NEW_LINE,
+        id="new_line",
+    ),
+    pytest.param(
+        True,
+        NEW_LINE_STREAMING,
+        id="new_line_streaming",
+    ),
+]
+
+
+@pytest.mark.parametrize("streaming, param_dict", TEST_CASES)
+def test_mistral_reasoning(
+    streaming: bool,
+    param_dict: dict,
+    mistral_tokenizer: MistralTokenizer,
+):
+    output = param_dict["output"]
+
+    index_think = output.find("[THINK]")
+    len_think = len("[THINK]")
+    index_end_think = output.find("[/THINK]")
+    len_end_think = len("[/THINK]")
+
+    # encode everything to tokens ids
+    output_tokens = []
+    if index_think != -1:
+        output_before_think = output[:index_think]
+        output_tokens += mistral_tokenizer.tokenizer.encode(
+            output_before_think, False, False)
+        output_tokens += [mistral_tokenizer.instruct.BEGIN_THINK]
+
+        if index_end_think != -1:
+            output_middle = output[index_think + len_think:index_end_think]
+            output_after_think = output[index_end_think + len_end_think:]
+            output_tokens += mistral_tokenizer.tokenizer.encode(
+                output_middle, False, False)
+            output_tokens += [mistral_tokenizer.instruct.END_THINK]
+            output_tokens += mistral_tokenizer.tokenizer.encode(
+                output_after_think, False, False)
+        else:
+            output_middle = output[index_think + len_think:]
+            output_tokens += mistral_tokenizer.tokenizer.encode(
+                output_middle, False, False)
+    elif index_end_think != -1:
+        output_before_think = output[:index_end_think]
+        output_after_think = output[index_end_think + len_end_think:]
+        output_tokens += mistral_tokenizer.tokenizer.encode(
+            output_before_think, False, False)
+        output_tokens += [mistral_tokenizer.instruct.END_THINK]
+        output_tokens += mistral_tokenizer.tokenizer.encode(
+            output_after_think, False, False)
+    else:
+        output_tokens += mistral_tokenizer.tokenizer.encode(
+            output, False, False)
+
+    parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser(
+        parser_name)(mistral_tokenizer)
+
+    reasoning, content = run_reasoning_extraction_mistral(parser,
+                                                          output_tokens,
+                                                          streaming=streaming)
+
+    assert reasoning == param_dict["reasoning_content"]
+    assert content == param_dict["content"]
+
+    # Test is_reasoning_end
+    is_reasoning_end = parser.is_reasoning_end(output_tokens)
+    assert is_reasoning_end == param_dict["is_reasoning_end"]
+
+    # Test extract_content
+    if param_dict["content"] is not None:
+        content = parser.extract_content_ids(output_tokens)
+        assert content == mistral_tokenizer.tokenizer.encode(
+            param_dict["content"], bos=False, eos=False)
+    else:
+        content = parser.extract_content_ids(output_tokens)
+        assert content == []

tests/reasoning/utils.py

@@ -6,6 +6,7 @@ from typing import Optional, Union
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               DeltaMessage)
 from vllm.reasoning import ReasoningParser
+from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
 
 
 class StreamingReasoningReconstructor:
@@ -54,6 +55,32 @@ def run_reasoning_extraction(
         return reasoning, content
 
 
+def run_reasoning_extraction_mistral(
+    reasoning_parser: ReasoningParser,
+    model_output: list[int],
+    request: Union[ChatCompletionRequest, None] = None,
+    streaming: bool = False,
+) -> tuple[Optional[str], Optional[str]]:
+    assert isinstance(reasoning_parser.model_tokenizer,
+                      MistralTokenizer), type(reasoning_parser.model_tokenizer)
+    if streaming:
+        reconstructor = run_reasoning_extraction_streaming_mistral(
+            reasoning_parser,
+            model_output,
+            request,
+        )
+        return (
+            reconstructor.reasoning_content,
+            reconstructor.other_content or None,
+        )
+    else:
+        str_output = reasoning_parser.model_tokenizer.convert_ids_to_tokens(
+            model_output)
+        reasoning, content = run_reasoning_extraction_nonstreaming(
+            reasoning_parser, str_output, request)
+        return reasoning, content
+
+
 def run_reasoning_extraction_nonstreaming(
     reasoning_parser: ReasoningParser,
     model_output: list[str],
@@ -94,3 +121,35 @@ def run_reasoning_extraction_streaming(
         previous_text = current_text
         previous_tokens = current_tokens
     return reconstructor
+
+
+def run_reasoning_extraction_streaming_mistral(
+    reasoning_parser: ReasoningParser,
+    model_deltas: list[int],
+    request: Union[ChatCompletionRequest, None] = None,
+) -> StreamingReasoningReconstructor:
+    assert isinstance(reasoning_parser.model_tokenizer,
+                      MistralTokenizer), type(reasoning_parser.model_tokenizer)
+    request = request or ChatCompletionRequest(messages=[], model="test-model")
+    reconstructor = StreamingReasoningReconstructor()
+    previous_text = ""
+    previous_tokens: list[int] = []
+    for model_delta in model_deltas:
+        token_delta = [model_delta]
+        delta = reasoning_parser.model_tokenizer.convert_ids_to_tokens(
+            [model_delta])[0]
+        current_text = previous_text + delta
+        current_tokens = previous_tokens + token_delta
+        delta_message = reasoning_parser.extract_reasoning_content_streaming(
+            previous_text,
+            current_text,
+            delta,
+            previous_tokens,
+            current_tokens,
+            token_delta,
+        )
+        if delta_message is not None:
+            reconstructor.append_delta(delta_message)
+        previous_text = current_text
+        previous_tokens = current_tokens
+    return reconstructor

tests/samplers/test_ignore_eos.py

@@ -38,7 +38,7 @@ def test_ignore_eos(
                                          ignore_eos=True)
 
         for prompt in example_prompts:
-            ignore_eos_output = vllm_model.model.generate(
+            ignore_eos_output = vllm_model.llm.generate(
                 prompt, sampling_params=sampling_params)
             output_length = len(ignore_eos_output[0].outputs[0].token_ids)
             assert output_length == max_tokens

tests/samplers/test_logits_processor.py

@@ -28,7 +28,7 @@ def test_logits_processor_force_generate(
     dtype: str,
 ) -> None:
     with vllm_runner(model, dtype=dtype) as vllm_model:
-        tokenizer = vllm_model.model.get_tokenizer()
+        tokenizer = vllm_model.llm.get_tokenizer()
         repeat_times = 2
         enforced_answers = " vLLM"
         vllm_token_ids = tokenizer.encode(enforced_answers,
@@ -47,13 +47,13 @@ def test_logits_processor_force_generate(
         )
 
         # test logits_processors when prompt_logprobs is not None
-        vllm_model.model._add_request(
+        vllm_model.llm._add_request(
             example_prompts[0],
             params=params_with_logprobs,
         )
 
         # test prompt_logprobs is not None
-        vllm_model.model._add_request(
+        vllm_model.llm._add_request(
             example_prompts[1],
             params=SamplingParams(
                 prompt_logprobs=3,
@@ -62,11 +62,11 @@ def test_logits_processor_force_generate(
         )
 
         # test grouped requests
-        vllm_model.model._add_request(
+        vllm_model.llm._add_request(
             example_prompts[2],
             params=SamplingParams(max_tokens=max_tokens),
         )
 
-        outputs = vllm_model.model._run_engine(use_tqdm=False)
+        outputs = vllm_model.llm._run_engine(use_tqdm=False)
 
         assert outputs[0].outputs[0].text == enforced_answers * repeat_times

tests/samplers/test_logprobs.py

@@ -66,7 +66,7 @@ def test_get_prompt_logprobs(
                                               prompt_logprobs=num_top_logprobs,
                                               temperature=0.0,
                                               detokenize=detokenize)
-        vllm_results = vllm_model.model.generate(
+        vllm_results = vllm_model.llm.generate(
             example_prompts, sampling_params=vllm_sampling_params)
 
     # Test whether logprobs are included in the results.
@@ -176,7 +176,7 @@ def test_none_logprobs(vllm_runner, model, chunked_prefill_token_size: int,
                                                        logprobs=None,
                                                        temperature=0.0,
                                                        detokenize=detokenize)
-        results_logprobs_none = vllm_model.model.generate(
+        results_logprobs_none = vllm_model.llm.generate(
             example_prompts, sampling_params=sampling_params_logprobs_none)
 
     for i in range(len(results_logprobs_none)):