[BugFix] Fix DeepGEMM warmup, no m.weight_scale_inv (#25206)

Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>

.buildkite/test-pipeline.yaml

@@ -280,7 +280,7 @@ steps:
     # split the test to avoid interference
     - pytest -v -s v1/core
     - pytest -v -s v1/executor
-    - pytest -v -s v1/offloading
+    - pytest -v -s v1/kv_offload
     - pytest -v -s v1/sample
     - pytest -v -s v1/logits_processors
     - pytest -v -s v1/worker

examples/online_serving/multi_instance_data_parallel.py

@@ -1,15 +1,12 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import asyncio
-import threading
 from typing import Optional
 
-from vllm.config import VllmConfig
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import SamplingParams
-from vllm.v1.metrics.loggers import AggregatedStatLogger, LoggingStatLogger
 
 """
 To run this example, run the following commands simultaneously with
@@ -25,67 +22,37 @@ send a request to the instance with DP rank 1.
 """
 
 
-def _do_background_logging(engine, interval, stop_event):
-    try:
-        while not stop_event.is_set():
-            asyncio.run(engine.do_log_stats())
-            stop_event.wait(interval)
-    except Exception as e:
-        print(f"vLLM background logging shutdown: {e}")
-        pass
-
-
 async def main():
     engine_args = AsyncEngineArgs(
         model="ibm-research/PowerMoE-3b",
         data_parallel_size=2,
-        tensor_parallel_size=1,
         dtype="auto",
         max_model_len=2048,
         data_parallel_address="127.0.0.1",
         data_parallel_rpc_port=62300,
         data_parallel_size_local=1,
         enforce_eager=True,
-        enable_log_requests=True,
-        disable_custom_all_reduce=True,
     )
 
-    def per_engine_logger_factory(config: VllmConfig, rank: int) -> LoggingStatLogger:
-        return LoggingStatLogger(config, rank)
+    engine_client = AsyncLLMEngine.from_engine_args(engine_args)
 
-    engine_client = AsyncLLMEngine.from_engine_args(
-        engine_args,
-        # Example: Using both regular loggers and aggregated logger
-        stat_loggers=[per_engine_logger_factory, AggregatedStatLogger],
-    )
-    stop_logging_event = threading.Event()
-    logging_thread = threading.Thread(
-        target=_do_background_logging,
-        args=(engine_client, 5, stop_logging_event),
-        daemon=True,
-    )
-    logging_thread.start()
     sampling_params = SamplingParams(
         temperature=0.7,
         top_p=0.9,
         max_tokens=100,
     )
-    num_prompts = 10
-    for i in range(num_prompts):
-        prompt = "Who won the 2004 World Series?"
-        final_output: Optional[RequestOutput] = None
-        async for output in engine_client.generate(
-            prompt=prompt,
-            sampling_params=sampling_params,
-            request_id=f"abcdef-{i}",
-            data_parallel_rank=1,
-        ):
-            final_output = output
-        if final_output:
-            print(final_output.outputs[0].text)
 
-    stop_logging_event.set()
-    logging_thread.join()
+    prompt = "Who won the 2004 World Series?"
+    final_output: Optional[RequestOutput] = None
+    async for output in engine_client.generate(
+        prompt=prompt,
+        sampling_params=sampling_params,
+        request_id="abcdef",
+        data_parallel_rank=1,
+    ):
+        final_output = output
+    if final_output:
+        print(final_output.outputs[0].text)
 
 
 if __name__ == "__main__":

tests/v1/engine/test_async_llm.py

@@ -18,7 +18,7 @@ from vllm.platforms import current_platform
 from vllm.sampling_params import RequestOutputKind
 from vllm.utils import set_default_torch_num_threads
 from vllm.v1.engine.async_llm import AsyncLLM
-from vllm.v1.metrics.loggers import AggregatedStatLogger, LoggingStatLogger
+from vllm.v1.metrics.loggers import LoggingStatLogger
 
 if not current_platform.is_cuda():
     pytest.skip(reason="V1 currently only supported on CUDA.",
@@ -389,15 +389,6 @@ class MockLoggingStatLogger(LoggingStatLogger):
         self.log = MagicMock()
 
 
-class MockAggregatedStatLogger(AggregatedStatLogger):
-
-    def __init__(self,
-                 vllm_config: VllmConfig,
-                 engine_indexes: Optional[list[int]] = None):
-        super().__init__(vllm_config, engine_indexes)
-        self.log = MagicMock()
-
-
 @pytest.mark.asyncio
 async def test_customize_loggers(monkeypatch):
     """Test that we can customize the loggers.
@@ -424,35 +415,6 @@ async def test_customize_loggers(monkeypatch):
         stat_loggers[0][0].log.assert_called_once()
 
 
-@pytest.mark.asyncio
-async def test_customize_aggregated_loggers(monkeypatch):
-    """Test that we can customize the aggregated loggers.
-    If a customized logger is provided at the init, it should
-    be added to the default loggers.
-    """
-
-    with monkeypatch.context() as m, ExitStack() as after:
-        m.setenv("VLLM_USE_V1", "1")
-
-        with set_default_torch_num_threads(1):
-            engine = AsyncLLM.from_engine_args(
-                TEXT_ENGINE_ARGS,
-                stat_loggers=[MockLoggingStatLogger, MockAggregatedStatLogger],
-            )
-        after.callback(engine.shutdown)
-
-        await engine.do_log_stats()
-
-        stat_loggers = engine.logger_manager.per_engine_logger_dict
-        assert len(stat_loggers) == 1
-        assert len(
-            stat_loggers[0]) == 2  # LoggingStatLogger + MockLoggingStatLogger
-        aggregated_loggers = engine.logger_manager.aggregated_loggers
-        assert len(aggregated_loggers) == 1
-        aggregated_loggers[0].log.assert_called_once()
-        stat_loggers[0][0].log.assert_called_once()
-
-
 @pytest.mark.asyncio(scope="module")
 async def test_dp_rank_argument(monkeypatch: pytest.MonkeyPatch):
     with monkeypatch.context() as m, ExitStack() as after:

tests/v1/kv_offload/test_worker.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from vllm.v1.offloading.abstract import LoadStoreSpec
-from vllm.v1.offloading.worker.worker import (OffloadingHandler,
+from vllm.v1.kv_offload.abstract import LoadStoreSpec
+from vllm.v1.kv_offload.worker.worker import (OffloadingHandler,
                                               OffloadingWorker, TransferResult,
                                               TransferSpec)
 

vllm/config/compilation.py

@@ -563,18 +563,6 @@ class CompilationConfig:
                 self.cudagraph_mode = CUDAGraphMode.FULL
             self.splitting_ops = []
 
-        if envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput":
-            # exclude MoE dispatch/combine from capture by ensuring
-            # piecewise splitting includes them, so communication remains
-            # outside CUDA graphs while compute can still be graphed.
-            moe_ops = [
-                "vllm.moe_forward",
-                "vllm.moe_forward_shared",
-            ]
-            for op in moe_ops:
-                if op not in self.splitting_ops:
-                    self.splitting_ops.append(op)
-
     def splitting_ops_contain_attention(self) -> bool:
         return self.splitting_ops is not None and all(
             op in self.splitting_ops for op in self._attention_ops)

vllm/engine/async_timeout.py

@@ -1,173 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-# Workaround for https://github.com/python/cpython/issues/86296
-#
-# From https://github.com/aio-libs/async-timeout/blob/master/async_timeout/__init__.py
-# Licensed under the Apache License (Apache-2.0)
-
-import asyncio
-import enum
-import sys
-from types import TracebackType
-from typing import Any, Optional, Type
-
-if sys.version_info[:2] >= (3, 11):
-    from asyncio import timeout as asyncio_timeout
-else:
-
-    class _State(enum.Enum):
-        INIT = "INIT"
-        ENTER = "ENTER"
-        TIMEOUT = "TIMEOUT"
-        EXIT = "EXIT"
-
-    class Timeout:
-        # Internal class, please don't instantiate it directly
-        # Use timeout() and timeout_at() public factories instead.
-        #
-        # Implementation note: `async with timeout()` is preferred
-        # over `with timeout()`.
-        # While technically the Timeout class implementation
-        # doesn't need to be async at all,
-        # the `async with` statement explicitly points that
-        # the context manager should be used from async function context.
-        #
-        # This design allows to avoid many silly misusages.
-        #
-        # TimeoutError is raised immediately when scheduled
-        # if the deadline is passed.
-        # The purpose is to time out as soon as possible
-        # without waiting for the next await expression.
-
-        __slots__ = ("_deadline", "_loop", "_state", "_timeout_handler")
-
-        def __init__(self, deadline: Optional[float],
-                     loop: asyncio.AbstractEventLoop) -> None:
-            self._loop = loop
-            self._state = _State.INIT
-
-            self._timeout_handler = None  # type: Optional[asyncio.Handle]
-            if deadline is None:
-                self._deadline = None  # type: Optional[float]
-            else:
-                self.update(deadline)
-
-        async def __aenter__(self) -> "Timeout":
-            self._do_enter()
-            return self
-
-        async def __aexit__(
-            self,
-            exc_type: Optional[Type[BaseException]],
-            exc_val: Optional[BaseException],
-            exc_tb: Optional[TracebackType],
-        ) -> Optional[bool]:
-            self._do_exit(exc_type)
-            return None
-
-        @property
-        def expired(self) -> bool:
-            """Is timeout expired during execution?"""
-            return self._state == _State.TIMEOUT
-
-        @property
-        def deadline(self) -> Optional[float]:
-            return self._deadline
-
-        def reject(self) -> None:
-            """Reject scheduled timeout if any."""
-            # cancel is maybe better name but
-            # task.cancel() raises CancelledError in asyncio world.
-            if self._state not in (_State.INIT, _State.ENTER):
-                raise RuntimeError(f"invalid state {self._state.value}")
-            self._reject()
-
-        def _reject(self) -> None:
-            if self._timeout_handler is not None:
-                self._timeout_handler.cancel()
-                self._timeout_handler = None
-
-        def shift(self, delay: float) -> None:
-            """Advance timeout on delay seconds.
-            The delay can be negative.
-            Raise RuntimeError if shift is called when deadline is not scheduled
-            """
-            deadline = self._deadline
-            if deadline is None:
-                raise RuntimeError(
-                    "cannot shift timeout if deadline is not scheduled")
-            self.update(deadline + delay)
-
-        def update(self, deadline: float) -> None:
-            """Set deadline to absolute value.
-            deadline argument points on the time in the same clock system
-            as loop.time().
-            If new deadline is in the past the timeout is raised immediately.
-            Please note: it is not POSIX time but a time with
-            undefined starting base, e.g. the time of the system power on.
-            """
-            if self._state == _State.EXIT:
-                raise RuntimeError(
-                    "cannot reschedule after exit from context manager")
-            if self._state == _State.TIMEOUT:
-                raise RuntimeError("cannot reschedule expired timeout")
-            if self._timeout_handler is not None:
-                self._timeout_handler.cancel()
-            self._deadline = deadline
-            if self._state != _State.INIT:
-                self._reschedule()
-
-        def _reschedule(self) -> None:
-            assert self._state == _State.ENTER
-            deadline = self._deadline
-            if deadline is None:
-                return
-
-            now = self._loop.time()
-            if self._timeout_handler is not None:
-                self._timeout_handler.cancel()
-
-            task = asyncio.current_task()
-            if deadline <= now:
-                self._timeout_handler = self._loop.call_soon(
-                    self._on_timeout, task)
-            else:
-                self._timeout_handler = self._loop.call_at(
-                    deadline, self._on_timeout, task)
-
-        def _do_enter(self) -> None:
-            if self._state != _State.INIT:
-                raise RuntimeError(f"invalid state {self._state.value}")
-            self._state = _State.ENTER
-            self._reschedule()
-
-        def _do_exit(self, exc_type: Optional[Type[BaseException]]) -> None:
-            if exc_type is asyncio.CancelledError and \
-                    self._state == _State.TIMEOUT:
-                self._timeout_handler = None
-                raise asyncio.TimeoutError
-            # timeout has not expired
-            self._state = _State.EXIT
-            self._reject()
-            return None
-
-        def _on_timeout(self, task: "Optional[asyncio.Task[Any]]") -> None:
-            if task:
-                task.cancel()
-            self._state = _State.TIMEOUT
-            # drop the reference early
-            self._timeout_handler = None
-
-    def asyncio_timeout(delay: Optional[float]) -> Timeout:
-        """timeout context manager.
-        Useful in cases when you want to apply timeout logic around block
-        of code or in cases when asyncio.wait_for is not suitable. For example:
-        >>> async with timeout(0.001):
-        ...     async with aiohttp.get('https://github.com') as r:
-        ...         await r.text()
-        delay - value in seconds or None to disable timeout logic
-        """
-        loop = asyncio.get_running_loop()
-        deadline = loop.time() + delay if delay is not None else None
-        return Timeout(deadline, loop)

vllm/model_executor/models/__init__.py

@@ -1,10 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from .interfaces import (HasInnerState, SupportsLoRA, SupportsMultiModal,
-                         SupportsPP, SupportsTranscription, SupportsV0Only,
-                         has_inner_state, supports_lora, supports_multimodal,
-                         supports_pp, supports_transcription, supports_v0_only)
+from .interfaces import (HasInnerState, SupportsLoRA, SupportsMRoPE,
+                         SupportsMultiModal, SupportsPP, SupportsTranscription,
+                         SupportsV0Only, has_inner_state, supports_lora,
+                         supports_mrope, supports_multimodal, supports_pp,
+                         supports_transcription, supports_v0_only)
 from .interfaces_base import (VllmModelForPooling, VllmModelForTextGeneration,
                               is_pooling_model, is_text_generation_model)
 from .registry import ModelRegistry
@@ -21,6 +22,8 @@ __all__ = [
     "supports_lora",
     "SupportsMultiModal",
     "supports_multimodal",
+    "SupportsMRoPE",
+    "supports_mrope",
     "SupportsPP",
     "supports_pp",
     "SupportsTranscription",

vllm/model_executor/models/interfaces.py

@@ -8,6 +8,7 @@ from typing import (TYPE_CHECKING, ClassVar, Literal, Optional, Protocol,
 import numpy as np
 import torch
 from torch import Tensor
+from transformers import PretrainedConfig
 from transformers.models.whisper.tokenization_whisper import LANGUAGES
 from typing_extensions import Self, TypeIs
 
@@ -852,3 +853,70 @@ def supports_eagle3(
     model: Union[type[object], object],
 ) -> Union[TypeIs[type[SupportsEagle3]], TypeIs[SupportsEagle3]]:
     return isinstance(model, SupportsEagle3)
+
+
+@runtime_checkable
+class SupportsMRoPE(Protocol):
+    """The interface required for all models that support M-RoPE."""
+
+    supports_mrope: ClassVar[Literal[True]] = True
+    """
+    A flag that indicates this model supports M-RoPE.
+    
+    Note:
+        There is no need to redefine this flag if this class is in the
+        MRO of your model class.
+    """
+
+    def get_mrope_input_positions(
+        self,
+        input_tokens: list[int],
+        hf_config: PretrainedConfig,
+        image_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
+        video_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
+        second_per_grid_ts: Optional[list[float]] = None,
+        context_len: int = 0,
+        seq_len: Optional[int] = None,
+        audio_feature_lengths: Optional[torch.Tensor] = None,
+        use_audio_in_video: bool = False,
+    ) -> tuple[torch.Tensor, int]:
+        """
+        Get M-RoPE input positions and delta value for this specific model.
+        
+        This method should be implemented by each model that supports M-RoPE
+        to provide model-specific logic for computing input positions.
+        
+        Args:
+            input_tokens: List of input token IDs
+            hf_config: HuggingFace model configuration
+            image_grid_thw: Image grid dimensions (t, h, w)
+            video_grid_thw: Video grid dimensions (t, h, w)
+            second_per_grid_ts: Seconds per grid timestep for videos
+            context_len: Context length
+            seq_len: Sequence length
+            audio_feature_lengths: Audio feature lengths for multimodal models
+            use_audio_in_video: Whether to use audio in video for interleaving
+            
+        Returns:
+            Tuple of (llm_positions, mrope_position_delta)
+            - llm_positions: Tensor of shape [3, num_tokens]
+                with T/H/W positions
+            - mrope_position_delta: Delta for position calculations
+        """
+        ...
+
+
+@overload
+def supports_mrope(model: type[object]) -> TypeIs[type[SupportsMRoPE]]:
+    ...
+
+
+@overload
+def supports_mrope(model: object) -> TypeIs[SupportsMRoPE]:
+    ...
+
+
+def supports_mrope(
+    model: Union[type[object], object],
+) -> Union[TypeIs[type[SupportsMRoPE]], TypeIs[SupportsMRoPE]]:
+    return isinstance(model, SupportsMRoPE)

vllm/model_executor/models/qwen2_vl.py

@@ -32,7 +32,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange, repeat
-from transformers import AutoConfig, BatchFeature
+from transformers import AutoConfig, BatchFeature, PretrainedConfig
 from transformers.models.qwen2_vl import (Qwen2VLImageProcessor,
                                           Qwen2VLProcessor)
 from transformers.models.qwen2_vl.configuration_qwen2_vl import (
@@ -73,7 +73,7 @@ from vllm.transformers_utils.config import uses_mrope
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
-from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMRoPE,
                          SupportsMultiModal, SupportsPP)
 from .utils import (AutoWeightsLoader, WeightsMapper,
                     init_vllm_registered_model, maybe_prefix,
@@ -1096,7 +1096,7 @@ class Qwen2VLMultiModalProcessor(BaseMultiModalProcessor[Qwen2VLProcessingInfo]
                                         info=Qwen2VLProcessingInfo,
                                         dummy_inputs=Qwen2VLDummyInputsBuilder)
 class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
-                                      SupportsLoRA, SupportsPP):
+                                      SupportsLoRA, SupportsPP, SupportsMRoPE):
 
     # To ensure correct weight loading and mapping.
     hf_to_vllm_mapper = WeightsMapper(
@@ -1109,6 +1109,118 @@ class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal,
             "model.": "language_model.model.",
         })
 
+    def get_mrope_input_positions(
+        self,
+        input_tokens: list[int],
+        hf_config: PretrainedConfig,
+        image_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
+        video_grid_thw: Optional[Union[list[list[int]], torch.Tensor]],
+        second_per_grid_ts: Optional[list[float]] = None,
+        context_len: int = 0,
+        seq_len: Optional[int] = None,
+        audio_feature_lengths: Optional[torch.Tensor] = None,
+        use_audio_in_video: bool = False,
+    ) -> tuple[torch.Tensor, int]:
+        """Get M-RoPE input positions for Qwen2-VL model."""
+        if image_grid_thw is None:
+            image_grid_thw = []
+        if video_grid_thw is None:
+            video_grid_thw = []
+        if second_per_grid_ts is None:
+            second_per_grid_ts = []
+
+        image_token_id = hf_config.image_token_id
+        video_token_id = hf_config.video_token_id
+        vision_start_token_id = hf_config.vision_start_token_id
+        spatial_merge_size = hf_config.vision_config.spatial_merge_size
+        tokens_per_second = getattr(hf_config.vision_config,
+                                    "tokens_per_second", 1.0)
+
+        input_tokens_tensor = torch.tensor(input_tokens)
+        vision_start_indices = torch.argwhere(
+            input_tokens_tensor == vision_start_token_id).squeeze(1)
+        vision_tokens = input_tokens_tensor[vision_start_indices + 1]
+        image_nums = (vision_tokens == image_token_id).sum()
+        video_nums = (vision_tokens == video_token_id).sum()
+        llm_pos_ids_list: list = []
+
+        st = 0
+        remain_images, remain_videos = image_nums, video_nums
+
+        image_index, video_index = 0, 0
+        for _ in range(image_nums + video_nums):
+            video_second_per_grid_t = 0.0
+            if remain_images > 0:
+                try:
+                    ed_image = input_tokens.index(image_token_id, st)
+                except ValueError:
+                    ed_image = len(input_tokens) + 1
+            else:
+                ed_image = len(input_tokens) + 1
+            if remain_videos > 0:
+                try:
+                    ed_video = input_tokens.index(video_token_id, st)
+                except ValueError:
+                    ed_video = len(input_tokens) + 1
+            else:
+                ed_video = len(input_tokens) + 1
+            if ed_image < ed_video:
+                t, h, w = (
+                    image_grid_thw[image_index][0],
+                    image_grid_thw[image_index][1],
+                    image_grid_thw[image_index][2],
+                )
+                image_index += 1
+                remain_images -= 1
+                ed = ed_image
+            else:
+                t, h, w = (
+                    video_grid_thw[video_index][0],
+                    video_grid_thw[video_index][1],
+                    video_grid_thw[video_index][2],
+                )
+                video_second_per_grid_t = 1.0
+                if second_per_grid_ts:
+                    video_second_per_grid_t = second_per_grid_ts[video_index]
+                video_index += 1
+                remain_videos -= 1
+                ed = ed_video
+
+            llm_grid_t, llm_grid_h, llm_grid_w = \
+                t, h // spatial_merge_size, w // spatial_merge_size
+            text_len = ed - st
+
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(
+                llm_pos_ids_list) > 0 else 0
+            llm_pos_ids_list.append(
+                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+            t_index = (torch.arange(llm_grid_t).view(-1, 1).expand(
+                -1, llm_grid_h * llm_grid_w) * video_second_per_grid_t *
+                       tokens_per_second).long().flatten()
+
+            h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(
+                llm_grid_t, -1, llm_grid_w).flatten()
+            w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(
+                llm_grid_t, llm_grid_h, -1).flatten()
+            llm_pos_ids_list.append(
+                torch.stack([t_index, h_index, w_index]) + text_len + st_idx)
+            st = ed + llm_grid_t * llm_grid_h * llm_grid_w
+
+        if st < len(input_tokens):
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(
+                llm_pos_ids_list) > 0 else 0
+            text_len = len(input_tokens) - st
+            llm_pos_ids_list.append(
+                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+        mrope_position_delta = (llm_positions.max() + 1 -
+                                len(input_tokens)).item()
+        llm_positions = llm_positions[:, context_len:seq_len]
+
+        return llm_positions, mrope_position_delta
+
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
         if modality.startswith("image"):

vllm/model_executor/warmup/deep_gemm_warmup.py

@@ -36,7 +36,7 @@ def _extract_data_from_linear_base_module(
     assert m.quant_method.quant_config is not None
 
     w = m.weight
-    ws = m.weight_scale_inv
+    ws = m.weight_scale
     quant_block_size = m.quant_method.quant_config.weight_block_size
 
     assert isinstance(w, torch.Tensor)

vllm/platforms/cuda.py

@@ -191,14 +191,17 @@ class CudaPlatformBase(Platform):
         compilation_config = vllm_config.compilation_config
         if (envs.VLLM_ALL2ALL_BACKEND == "deepep_high_throughput"
                 and parallel_config.data_parallel_size > 1
-                and compilation_config.cudagraph_mode
-                not in [CUDAGraphMode.NONE, CUDAGraphMode.PIECEWISE]):
+                and compilation_config.cudagraph_mode != CUDAGraphMode.NONE):
+            # TODO: Piecewise Cuda graph might be enabled
+            # if torch compile cache key issue fixed
+            # See https://github.com/vllm-project/vllm/pull/25093
             logger.info(
-                "Data Parallel with DeepEP high-throughput: using PIECEWISE "
-                "CUDA graphs and excluding MoE ops from capture. Set "
-                "VLLM_ALL2ALL_BACKEND=deepep_low_latency if you need MoE "
-                "graphs captured as well.")
-            compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
+                "Data Parallel: disabling cudagraphs since DP "
+                "with DeepEP high-throughput kernels are not CUDA Graph "
+                "compatible. The DeepEP low-latency kernels are CUDA Graph "
+                "compatible. Set the all_to_all backend to deepep_low_latency "
+                "to use those kernels instead.")
+            compilation_config.cudagraph_mode = CUDAGraphMode.NONE
 
     @classmethod
     def get_current_memory_usage(cls,

vllm/v1/attention/backends/mla/cutlass_mla.py

@@ -206,12 +206,11 @@ class CutlassMLAImpl(MLACommonImpl[MLACommonMetadata]):
         )
 
         if H < MAX_HEADS:
-            # Extract the subsets of the outputs
-            returned_lse = lse[:, :H].contiguous(
-            ) if self.need_to_return_lse_for_decode else lse
             out = out[:, :H]
+            if self.need_to_return_lse_for_decode:
+                lse = lse[:, :H].contiguous()
 
-        return out, returned_lse
+        return out, lse
 
     def _forward_decode(
         self,

vllm/v1/kv_offload/mediums.py

@@ -4,7 +4,7 @@ from abc import ABC
 
 import numpy as np
 
-from vllm.v1.offloading.abstract import LoadStoreSpec
+from vllm.v1.kv_offload.abstract import LoadStoreSpec
 
 
 class BlockIDsLoadStoreSpec(LoadStoreSpec, ABC):

vllm/v1/kv_offload/worker/worker.py

@@ -3,7 +3,7 @@
 from abc import ABC, abstractmethod
 
 from vllm.logger import init_logger
-from vllm.v1.offloading.abstract import LoadStoreSpec
+from vllm.v1.kv_offload.abstract import LoadStoreSpec
 
 # a single transfer spec (src_blocks_spec, dst_blocks_spec)
 TransferSpec = tuple[LoadStoreSpec, LoadStoreSpec]

vllm/v1/metrics/loggers.py

@@ -18,9 +18,7 @@ from vllm.v1.spec_decode.metrics import SpecDecodingLogging, SpecDecodingProm
 
 logger = init_logger(__name__)
 
-PerEngineStatLoggerFactory = Callable[[VllmConfig, int], "StatLoggerBase"]
-StatLoggerFactory = Union[PerEngineStatLoggerFactory,
-                          type["AggregatedStatLoggerBase"]]
+StatLoggerFactory = Callable[[VllmConfig, int], "StatLoggerBase"]
 
 
 class StatLoggerBase(ABC):
@@ -50,16 +48,6 @@ class StatLoggerBase(ABC):
         pass
 
 
-class AggregatedStatLoggerBase(StatLoggerBase):
-    """Abstract base class for loggers that
-       aggregates statistics across multiple engines."""
-
-    @abstractmethod
-    def __init__(self, vllm_config: VllmConfig,
-                 engine_indexes: Optional[list[int]]):
-        ...
-
-
 class LoggingStatLogger(StatLoggerBase):
 
     def __init__(self, vllm_config: VllmConfig, engine_index: int = 0):
@@ -73,7 +61,6 @@ class LoggingStatLogger(StatLoggerBase):
         self.spec_decoding_logging = SpecDecodingLogging()
         self.last_prompt_throughput: float = 0.0
         self.last_generation_throughput: float = 0.0
-        self.engine_is_idle = False
 
     def _reset(self, now):
         self.last_log_time = now
@@ -113,25 +100,25 @@ class LoggingStatLogger(StatLoggerBase):
 
             self.last_scheduler_stats = scheduler_stats
 
-    def get_log_stats(self):
+    def log(self):
         now = time.monotonic()
         prompt_throughput = self._get_throughput(self.num_prompt_tokens, now)
         generation_throughput = self._get_throughput(
             self.num_generation_tokens, now)
 
         self._reset(now)
-        self.last_generation_throughput = generation_throughput
-        self.last_prompt_throughput = prompt_throughput
-        self.engine_is_idle = not any(
-            (prompt_throughput, generation_throughput,
-             self.last_prompt_throughput, self.last_generation_throughput))
 
-    def log(self):
-        self.get_log_stats()
+        scheduler_stats = self.last_scheduler_stats
+
         log_fn = logger.info
-        if self.engine_is_idle:
+        if not any(
+            (prompt_throughput, generation_throughput,
+             self.last_prompt_throughput, self.last_generation_throughput)):
             # Avoid log noise on an idle production system
             log_fn = logger.debug
+        self.last_generation_throughput = generation_throughput
+        self.last_prompt_throughput = prompt_throughput
+
         # Format and print output.
         log_fn(
             "Engine %03d: "
@@ -141,11 +128,11 @@ class LoggingStatLogger(StatLoggerBase):
             "GPU KV cache usage: %.1f%%, "
             "Prefix cache hit rate: %.1f%%",
             self.engine_index,
-            self.last_prompt_throughput,
-            self.last_generation_throughput,
-            self.last_scheduler_stats.num_running_reqs,
-            self.last_scheduler_stats.num_waiting_reqs,
-            self.last_scheduler_stats.kv_cache_usage * 100,
+            prompt_throughput,
+            generation_throughput,
+            scheduler_stats.num_running_reqs,
+            scheduler_stats.num_waiting_reqs,
+            scheduler_stats.kv_cache_usage * 100,
             self.prefix_caching_metrics.hit_rate * 100,
         )
         self.spec_decoding_logging.log(log_fn=log_fn)
@@ -158,61 +145,7 @@ class LoggingStatLogger(StatLoggerBase):
                 self.vllm_config.cache_config.num_gpu_blocks)
 
 
-class AggregatedStatLogger(LoggingStatLogger, AggregatedStatLoggerBase):
-
-    def __init__(self,
-                 vllm_config: VllmConfig,
-                 engine_idxs: Optional[list[int]] = None):
-        if engine_idxs is None:
-            engine_idxs = [0]
-        self.engine_idxs = engine_idxs
-        LoggingStatLogger.__init__(self, vllm_config, engine_index=-1)
-
-    def record(
-        self,
-        scheduler_stats: Optional[SchedulerStats],
-        iteration_stats: Optional[IterationStats],
-        engine_idx: int = 0,
-    ):
-        if engine_idx not in self.engine_idxs:
-            logger.warning("Unexpected engine_idx: %d", engine_idx)
-            return
-        LoggingStatLogger.record(self, scheduler_stats, iteration_stats,
-                                 engine_idx)
-
-    def log(self):
-        self.get_log_stats()
-        log_fn = logger.info
-        if self.engine_is_idle:
-            # Avoid log noise on an idle production system
-            log_fn = logger.debug
-        # Format and print output.
-        log_fn(
-            "%s Engines Aggregated: "
-            "Avg prompt throughput: %.1f tokens/s, "
-            "Avg generation throughput: %.1f tokens/s, "
-            "Running: %d reqs, Waiting: %d reqs, "
-            "GPU KV cache usage: %.1f%%, "
-            "Prefix cache hit rate: %.1f%%",
-            len(self.engine_idxs),
-            self.last_prompt_throughput,
-            self.last_generation_throughput,
-            self.last_scheduler_stats.num_running_reqs,
-            self.last_scheduler_stats.num_waiting_reqs,
-            self.last_scheduler_stats.kv_cache_usage * 100,
-            self.prefix_caching_metrics.hit_rate * 100,
-        )
-        self.spec_decoding_logging.log(log_fn=log_fn)
-
-    def log_engine_initialized(self):
-        if self.vllm_config.cache_config.num_gpu_blocks:
-            logger.info(
-                "%d Engines: vllm cache_config_info with initialization "
-                "after num_gpu_blocks is: %d", len(self.engine_idxs),
-                self.vllm_config.cache_config.num_gpu_blocks)
-
-
-class PrometheusStatLogger(AggregatedStatLoggerBase):
+class PrometheusStatLogger(StatLoggerBase):
     _gauge_cls = prometheus_client.Gauge
     _counter_cls = prometheus_client.Counter
     _histogram_cls = prometheus_client.Histogram
@@ -741,32 +674,23 @@ class StatLoggerManager:
 
         # engine_idx: StatLogger
         self.per_engine_logger_dict: dict[int, list[StatLoggerBase]] = {}
-        self.aggregated_loggers: list[AggregatedStatLoggerBase] = []
-
-        aggregated_loggers_factories = set()
+        prometheus_factory = PrometheusStatLogger
         for engine_idx in self.engine_idxs:
             loggers: list[StatLoggerBase] = []
             for logger_factory in factories:
-                # If we get a custom prometheus logger or aggregated logger,
-                # We initialize it separately with all engine idxs.
-                # A custom prometheus logger is typically used for the ray.
-                if (isinstance(logger_factory, type) and issubclass(
-                        logger_factory, AggregatedStatLoggerBase)):
-                    aggregated_loggers_factories.add(logger_factory)
-                else:
-                    loggers.append(logger_factory(vllm_config,
-                                                  engine_idx))  # type: ignore
+                # If we get a custom prometheus logger, use that
+                # instead. This is typically used for the ray case.
+                if (isinstance(logger_factory, type)
+                        and issubclass(logger_factory, PrometheusStatLogger)):
+                    prometheus_factory = logger_factory
+                    continue
+                loggers.append(logger_factory(vllm_config,
+                                              engine_idx))  # type: ignore
             self.per_engine_logger_dict[engine_idx] = loggers
-        # If no custom aggregated logger is provide,
-        # we by default use PrometheusStatLogger
-        if not aggregated_loggers_factories:
-            aggregated_loggers_factories.add(PrometheusStatLogger)
-        # For custom aggregated logger(or default Prometheus Logger)
-        # need to share the metrics between EngineCores.
+
+        # For Prometheus, need to share the metrics between EngineCores.
         # Each EngineCore's metrics are expressed as a unique label.
-        for aggregated_loggers_factory in aggregated_loggers_factories:
-            self.aggregated_loggers.append(
-                aggregated_loggers_factory(vllm_config, engine_idxs))
+        self.prometheus_logger = prometheus_factory(vllm_config, engine_idxs)
 
     def record(
         self,
@@ -780,19 +704,18 @@ class StatLoggerManager:
         per_engine_loggers = self.per_engine_logger_dict[engine_idx]
         for logger in per_engine_loggers:
             logger.record(scheduler_stats, iteration_stats, engine_idx)
-        for logger in self.aggregated_loggers:
-            logger.record(scheduler_stats, iteration_stats, engine_idx)
+
+        self.prometheus_logger.record(scheduler_stats, iteration_stats,
+                                      engine_idx)
 
     def log(self):
         for per_engine_loggers in self.per_engine_logger_dict.values():
             for logger in per_engine_loggers:
                 logger.log()
-        for logger in self.aggregated_loggers:
-            logger.log()
 
     def log_engine_initialized(self):
-        for agg_logger in self.aggregated_loggers:
-            agg_logger.log_engine_initialized()
+        self.prometheus_logger.log_engine_initialized()
+
         for per_engine_loggers in self.per_engine_logger_dict.values():
             for logger in per_engine_loggers:
                 logger.log_engine_initialized()

vllm/v1/worker/gpu_model_runner.py

@@ -42,6 +42,7 @@ from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
 from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader
 from vllm.model_executor.models.interfaces import (is_mixture_of_experts,
                                                    supports_eagle3,
+                                                   supports_mrope,
                                                    supports_transcription)
 from vllm.model_executor.models.interfaces_base import (
     VllmModelForPooling, is_pooling_model, is_text_generation_model)
@@ -730,16 +731,28 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             if mm_input.get("use_audio_in_video") is True:
                 use_audio_in_video = True
 
-        req_state.mrope_positions, req_state.mrope_position_delta = \
-            MRotaryEmbedding.get_input_positions_tensor(
-                req_state.prompt_token_ids,
-                hf_config=self.model_config.hf_config,
-                image_grid_thw=image_grid_thw,
-                video_grid_thw=video_grid_thw,
-                second_per_grid_ts=second_per_grid_ts,
-                audio_feature_lengths=audio_feature_lengths,
-                use_audio_in_video=use_audio_in_video,
-            )
+        if supports_mrope(self.model):
+            req_state.mrope_positions, req_state.mrope_position_delta = \
+                self.model.get_mrope_input_positions(
+                    req_state.prompt_token_ids,
+                    hf_config=self.model_config.hf_config,
+                    image_grid_thw=image_grid_thw,
+                    video_grid_thw=video_grid_thw,
+                    second_per_grid_ts=second_per_grid_ts,
+                    audio_feature_lengths=audio_feature_lengths,
+                    use_audio_in_video=use_audio_in_video,
+                )
+        else:
+            req_state.mrope_positions, req_state.mrope_position_delta = \
+                MRotaryEmbedding.get_input_positions_tensor(
+                    req_state.prompt_token_ids,
+                    hf_config=self.model_config.hf_config,
+                    image_grid_thw=image_grid_thw,
+                    video_grid_thw=video_grid_thw,
+                    second_per_grid_ts=second_per_grid_ts,
+                    audio_feature_lengths=audio_feature_lengths,
+                    use_audio_in_video=use_audio_in_video,
+                )
 
     def _extract_mm_kwargs(
         self,

vllm/worker/model_runner.py

@@ -41,7 +41,8 @@ from vllm.model_executor.layers.sampler import (Sampler, SamplerOutput,
                                                 get_sampler)
 from vllm.model_executor.model_loader import get_model
 from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
-from vllm.model_executor.models import supports_lora, supports_multimodal
+from vllm.model_executor.models import (supports_lora, supports_mrope,
+                                        supports_multimodal)
 from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
 from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs,
                              MultiModalKwargs, MultiModalPlaceholderMap,
@@ -670,18 +671,33 @@ class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]):
                     inter_data.seq_ids[seq_idx]]
                 token_ids = seq_data.get_token_ids()
 
-                mrope_input_positions, mrope_position_delta = \
-                    MRotaryEmbedding.get_input_positions(
-                        token_ids,
-                        hf_config=hf_config,
-                        image_grid_thw=image_grid_thw,
-                        video_grid_thw=video_grid_thw,
-                        second_per_grid_ts=second_per_grid_ts,
-                        context_len=inter_data.context_lens[seq_idx],
-                        seq_len=inter_data.seq_lens[seq_idx],
-                        audio_feature_lengths=audio_feature_lengths,
-                        use_audio_in_video=use_audio_in_video,
-                    )
+                if supports_mrope(self.runner.model):
+                    mrope_input_positions, mrope_position_delta = \
+                        self.runner.model.get_mrope_input_positions(
+                            token_ids,
+                            hf_config=hf_config,
+                            image_grid_thw=image_grid_thw,
+                            video_grid_thw=video_grid_thw,
+                            second_per_grid_ts=second_per_grid_ts,
+                            context_len=inter_data.context_lens[seq_idx],
+                            seq_len=inter_data.seq_lens[seq_idx],
+                            audio_feature_lengths=audio_feature_lengths,
+                            use_audio_in_video=use_audio_in_video,
+                        )
+                    mrope_input_positions = mrope_input_positions.tolist()
+                else:
+                    mrope_input_positions, mrope_position_delta = \
+                        MRotaryEmbedding.get_input_positions(
+                            token_ids,
+                            hf_config=hf_config,
+                            image_grid_thw=image_grid_thw,
+                            video_grid_thw=video_grid_thw,
+                            second_per_grid_ts=second_per_grid_ts,
+                            context_len=inter_data.context_lens[seq_idx],
+                            seq_len=inter_data.seq_lens[seq_idx],
+                            audio_feature_lengths=audio_feature_lengths,
+                            use_audio_in_video=use_audio_in_video,
+                        )
 
                 seq_data.mrope_position_delta = mrope_position_delta
                 inter_data.mrope_input_positions[