Merge branch 'main' into model_arch_cfg

Signed-off-by: Xingyu Liu <38244988+charlotte12l@users.noreply.github.com>

.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_async_eplb.sh

@@ -1,73 +0,0 @@
-#!/usr/bin/env bash
-set -euxo pipefail
-
-# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
-THRESHOLD=${1:-0.25}
-NUM_Q=${2:-1319}
-PORT=${3:-8030}
-OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
-mkdir -p "${OUT_DIR}"
-
-wait_for_server() {
-  local port=$1
-  timeout 600 bash -c '
-    until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
-      sleep 1
-    done'
-}
-
-MODEL="deepseek-ai/DeepSeek-V2-lite"
-
-# Set BACKENDS based on platform
-if command -v rocm-smi &> /dev/null || [[ -d /opt/rocm ]] || [[ -n "${ROCM_PATH:-}" ]]; then
-  # ROCm platform
-  BACKENDS=("allgather_reducescatter")
-  # Disable MOE padding for ROCm since it is causing eplb to fail
-  export VLLM_ROCM_MOE_PADDING=0
-else
-  # Non-ROCm platform (CUDA/other)
-  BACKENDS=("deepep_high_throughput" "deepep_low_latency")
-fi
-
-cleanup() {
-  if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
-    kill "${SERVER_PID}" 2>/dev/null || true
-    for _ in {1..20}; do
-      kill -0 "${SERVER_PID}" 2>/dev/null || break
-      sleep 0.5
-    done
-    kill -9 "${SERVER_PID}" 2>/dev/null || true
-  fi
-}
-trap cleanup EXIT
-
-for BACK in "${BACKENDS[@]}"; do
-  VLLM_DEEP_GEMM_WARMUP=skip \
-  VLLM_ALL2ALL_BACKEND=$BACK \
-  vllm serve "$MODEL" \
-    --enforce-eager \
-    --tensor-parallel-size 2 \
-    --data-parallel-size 2 \
-    --enable-expert-parallel \
-    --enable-eplb \
-    --eplb-config '{"window_size":200,"step_interval":600,"use_async":true}' \
-    --trust-remote-code \
-    --max-model-len 2048 \
-    --port $PORT &
-  SERVER_PID=$!
-  wait_for_server $PORT
-
-  TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
-  OUT="${OUT_DIR}/${TAG}_${BACK}_async_eplb.json"
-  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
-  python3 - <<PY
-import json; acc=json.load(open('${OUT}'))['accuracy']
-print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")
-assert acc >= ${THRESHOLD}, f"${MODEL} ${BACK} accuracy {acc}"
-PY
-
-  cleanup
-  SERVER_PID=
-  sleep 1
-  PORT=$((PORT+1))
-done

.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh

@@ -50,7 +50,6 @@ for BACK in "${BACKENDS[@]}"; do
     --data-parallel-size 2 \
     --enable-expert-parallel \
     --enable-eplb \
-    --eplb-config '{"window_size":200,"step_interval":600}' \
     --trust-remote-code \
     --max-model-len 2048 \
     --port $PORT &

.buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh

@@ -1,74 +0,0 @@
-#!/usr/bin/env bash
-set -euxo pipefail
-
-# args: [THRESHOLD] [NUM_QUESTIONS] [START_PORT]
-THRESHOLD=${1:-0.25}
-NUM_Q=${2:-1319}
-PORT=${3:-8040}
-OUT_DIR=${OUT_DIR:-/tmp/vllm-scheduled}
-mkdir -p "${OUT_DIR}"
-
-wait_for_server() {
-  local port=$1
-  timeout 600 bash -c '
-    until curl -sf "http://127.0.0.1:'"$port"'/health" > /dev/null; do
-      sleep 1
-    done'
-}
-
-MODEL="Qwen/Qwen3-Next-80B-A3B-Instruct"
-
-# Set BACKENDS based on platform
-if command -v rocm-smi &> /dev/null || [[ -d /opt/rocm ]] || [[ -n "${ROCM_PATH:-}" ]]; then
-  # ROCm platform
-  BACKENDS=("allgather_reducescatter")
-  # Disable MOE padding for ROCm since it is causing eplb to fail
-  export VLLM_ROCM_MOE_PADDING=0
-else
-  # Non-ROCm platform (CUDA/other)
-  BACKENDS=("deepep_high_throughput" "deepep_low_latency")
-fi
-
-cleanup() {
-  if [[ -n "${SERVER_PID:-}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
-    kill "${SERVER_PID}" 2>/dev/null || true
-    for _ in {1..20}; do
-      kill -0 "${SERVER_PID}" 2>/dev/null || break
-      sleep 0.5
-    done
-    kill -9 "${SERVER_PID}" 2>/dev/null || true
-  fi
-}
-trap cleanup EXIT
-
-for BACK in "${BACKENDS[@]}"; do
-  VLLM_DEEP_GEMM_WARMUP=skip \
-  VLLM_ALL2ALL_BACKEND=$BACK \
-  vllm serve "$MODEL" \
-    --enforce-eager \
-    --tensor-parallel-size 4 \
-    --enable-expert-parallel \
-    --enable-eplb \
-    --eplb-config '{"window_size":200,"step_interval":600,"use_async":true}' \
-    --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":1}' \
-    --trust-remote-code \
-    --max-model-len 2048 \
-    --gpu-memory-utilization 0.9 \
-    --port $PORT &
-  SERVER_PID=$!
-  wait_for_server $PORT
-
-  TAG=$(echo "$MODEL" | tr '/: \\n' '_____')
-  OUT="${OUT_DIR}/${TAG}_${BACK}.json"
-  python3 tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port $PORT --num-questions ${NUM_Q} --save-results ${OUT}
-  python3 - <<PY
-import json; acc=json.load(open('${OUT}'))['accuracy']
-print(f"${MODEL} ${BACK}: accuracy {acc:.3f}")
-assert acc >= ${THRESHOLD}, f"${MODEL} ${BACK} accuracy {acc}"
-PY
-
-  cleanup
-  SERVER_PID=
-  sleep 1
-  PORT=$((PORT+1))
-done

.buildkite/test-pipeline.yaml

@@ -1380,21 +1380,3 @@ steps:
   working_dir: "/vllm-workspace"
   commands:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
-
-- label: DeepSeek V2-Lite Async EPLB Accuracy
-  timeout_in_minutes: 60
-  gpu: h100
-  optional: true
-  num_gpus: 4
-  working_dir: "/vllm-workspace"
-  commands:
-  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_async_eplb.sh 0.25 1319 8030
-
-- label: Qwen3-Next-80B-A3B-Instruct MTP Async EPLB Accuracy
-  timeout_in_minutes: 60
-  gpu: h100
-  optional: true
-  num_gpus: 4
-  working_dir: "/vllm-workspace"
-  commands:
-  - bash .buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh 0.8 1319 8040

benchmarks/benchmark_ngram_proposer.py

@@ -37,7 +37,7 @@ def benchmark_propose(args):
             tokenizer="facebook/opt-125m",
             tokenizer_mode="auto",
             dtype="auto",
-            seed=None,
+            seed=0,
             trust_remote_code=False,
         )
         proposer = NgramProposer(

docs/design/plugin_system.md

@@ -152,5 +152,5 @@ The interface for the model/module may change during vLLM's development. If you
 ## Deprecation announcement
 
 !!! warning "Deprecations"
-    - `use_v1` parameter in `Platform.get_attn_backend_cls` is deprecated. It will be removed in v0.13.0 or v1.0.0.
+    - `use_v1` parameter in `Platform.get_attn_backend_cls` is deprecated. It has been removed in v0.13.0.
-    - `_Backend` in `vllm.attention` is deprecated. It will be removed in v0.13.0 or v1.0.0. Please use `vllm.attention.backends.registry.register_backend` to add new attention backend to `AttentionBackendEnum` instead.
+    - `_Backend` in `vllm.attention` is deprecated. It has been removed in v0.13.0. Please use `vllm.attention.backends.registry.register_backend` to add new attention backend to `AttentionBackendEnum` instead.

docs/features/structured_outputs.md

@@ -61,7 +61,7 @@ Now let´s see an example for each of the cases, starting with the `choice`, as
     print(completion.choices[0].message.content)
     ```
 
-The next example shows how to use the `regex`. The idea is to generate an email address, given a simple regex template:
+The next example shows how to use the `regex`. The supported regex syntax depends on the structured output backend. For example, `xgrammar`, `guidance`, and `outlines` use Rust-style regex, while `lm-format-enforcer` uses Python's `re` module. The idea is to generate an email address, given a simple regex template:
 
 ??? code
 

docs/serving/data_parallel_deployment.md

@@ -24,7 +24,7 @@ There are two distinct modes supported for online deployments - self-contained w
 
 vLLM supports "self-contained" data parallel deployments that expose a single API endpoint.
 
-It can be configured by simply including e.g. `--data-parallel-size=4` in the vllm serve command line arguments. This will require 4 GPUs. It can be combined with tensor parallel, for example `--data-parallel-size=4 --tensor-parallel-size=2`, which would require 8 GPUs.
+It can be configured by simply including e.g. `--data-parallel-size=4` in the vllm serve command line arguments. This will require 4 GPUs. It can be combined with tensor parallel, for example `--data-parallel-size=4 --tensor-parallel-size=2`, which would require 8 GPUs. When sizing DP deployments, remember that `--max-num-seqs` applies per DP rank.
 
 Running a single data parallel deployment across multiple nodes requires a different `vllm serve` to be run on each node, specifying which DP ranks should run on that node. In this case, there will still be a single HTTP entrypoint - the API server(s) will run only on one node, but it doesn't necessarily need to be co-located with the DP ranks.
 
@@ -80,6 +80,18 @@ When deploying large DP sizes using this method, the API server process can beco
 ![DP Internal LB Diagram](../assets/deployment/dp_internal_lb.png)
 </figure>
 
+## Hybrid Load Balancing
+
+Hybrid load balancing sits between the internal and external approaches. Each node runs its own API server(s) that only queue requests to the data-parallel engines colocated on that node. An upstream load balancer (for example, an ingress controller or traffic router) spreads user requests across those per-node endpoints.
+
+Enable this mode with `--data-parallel-hybrid-lb` while still launching every node with the global data-parallel size. The key differences from internal load balancing are:
+
+- You must provide `--data-parallel-size-local` and `--data-parallel-start-rank` so each node knows which ranks it owns.
+- Not compatible with `--headless` since every node exposes an API endpoint.
+- Scale `--api-server-count` per node based on the number of local ranks
+
+In this configuration, each node keeps scheduling decisions local, which reduces cross-node traffic and avoids single node bottlenecks at larger DP sizes.
+
 ## External Load Balancing
 
 For larger scale deployments especially, it can make sense to handle the orchestration and load balancing of data parallel ranks externally.

docs/serving/expert_parallel_deployment.md

@@ -40,10 +40,12 @@ EP_SIZE = TP_SIZE × DP_SIZE
 
 Where:
 
-- `TP_SIZE`: Tensor parallel size (always 1 for now)
+- `TP_SIZE`: Tensor parallel size
 - `DP_SIZE`: Data parallel size
 - `EP_SIZE`: Expert parallel size (computed automatically)
 
+When EP is enabled, MoE layers use expert parallelism instead of tensor parallelism, while attention layers continue to use tensor parallelism if `TP_SIZE > 1`.
+
 ### Example Command
 
 The following command serves a `DeepSeek-V3-0324` model with 1-way tensor parallel, 8-way (attention) data parallel, and 8-way expert parallel. The attention weights are replicated across all GPUs, while the expert weights are split across GPUs. It will work on a H200 (or H20) node with 8 GPUs. For H100, you can try to serve a smaller model or refer to the multi-node deployment section.
@@ -81,7 +83,7 @@ vllm serve deepseek-ai/DeepSeek-V3-0324 \
     --data-parallel-size-local 8 \           # Local DP size on this node (8 GPUs per node)
     --data-parallel-address 192.168.1.100 \  # Replace with actual IP of Node 1
     --data-parallel-rpc-port 13345 \         # RPC communication port, can be any port as long as reachable by all nodes
-    --api-server-count=8                     # Number of API servers for load handling (scaling this out to total ranks are recommended)
+    --api-server-count=8                     # Number of API servers for load handling (scaling this out to # local ranks is recommended)
 
 # Node 2 (Secondary - headless mode, no API server)
 vllm serve deepseek-ai/DeepSeek-V3-0324 \
@@ -119,9 +121,6 @@ While MoE models are typically trained so that each expert receives a similar nu
 
 Enable EPLB with the `--enable-eplb` flag.
 
-!!! note "Model Support"
-    Currently only DeepSeek V3 architecture is supported.
-
 When enabled, vLLM collects load statistics with every forward pass and periodically rebalances expert distribution.
 
 ### EPLB Parameters
@@ -134,6 +133,8 @@ Configure EPLB with the `--eplb-config` argument, which accepts a JSON string. T
 | `step_interval`| Frequency of rebalancing (every N engine steps) | 3000 |
 | `log_balancedness` | Log balancedness metrics (avg tokens per expert ÷ max tokens per expert) | `false` |
 | `num_redundant_experts` | Additional global experts per EP rank beyond equal distribution | `0` |
+| `use_async` | Use non-blocking EPLB for reduced latency overhead | `false` |
+| `policy` | The policy type for expert parallel load balancing | `"default"` |
 
 For example:
 
@@ -183,6 +184,26 @@ vllm serve deepseek-ai/DeepSeek-V3-0324 \
 
 For multi-node deployment, add these EPLB flags to each node's command. We recommend setting `--eplb-config '{"num_redundant_experts":32}'` to 32 in large scale use cases so the most popular experts are always available.
 
+## Advanced Configuration
+
+### Performance Optimization
+
+- **DeepEP kernels**: The `high_throughput` and `low_latency` kernels are optimized for disaggregated serving and may show poor performance for mixed workloads
+- **Dual Batch Overlap**: Use `--enable-dbo` to overlap all-to-all communication with compute. See [Dual Batch Overlap](../design/dbo.md) for more details.
+- **Async scheduling (experimental)**: Try `--async-scheduling` to overlap scheduling with model execution.
+
+### Troubleshooting
+
+- **`non-zero status: 7 cannot register cq buf`**: When using Infiniband/RoCE, make sure host VM and pods show `ulimit -l` "unlimited".
+- **`init failed for transport: IBGDA`**: The InfiniBand GDA kernel modules are missing. Run `tools/ep_kernels/configure_system_drivers.sh` on each GPU node and reboot. Also fixes error `NVSHMEM API called before NVSHMEM initialization has completed`.
+- **NVSHMEM peer disconnect**: Usually a networking misconfiguration. If deploying via Kubernetes, verify that every pod runs with `hostNetwork: true`, `securityContext.privileged: true` to access Infiniband.
+
+### Benchmarking
+
+- Use simulator flags `VLLM_MOE_ROUTING_SIMULATION_STRATEGY=uniform_random` and `VLLM_RANDOMIZE_DP_DUMMY_INPUTS=1` so token routing is balanced across EP ranks.
+
+- Increasing `VLLM_MOE_DP_CHUNK_SIZE` may increase throughput by increasing the maximum batch size for inter-rank token transfers. This may cause DeepEP  to throw `assert self.nvshmem_qp_depth >= (num_max_dispatch_tokens_per_rank + 1) * 2`, which can be fixed by increasing environment variable `NVSHMEM_QP_DEPTH`.
+
 ## Disaggregated Serving (Prefill/Decode Split)
 
 For production deployments requiring strict SLA guarantees for time-to-first-token and inter-token latency, disaggregated serving allows independent scaling of prefill and decode operations.
@@ -273,3 +294,9 @@ except Exception as e:
     print(f"❌ Error during disaggregated serving: {e}")
     print("Check that both prefill and decode instances are running and accessible")
 ```
+
+### Benchmarking
+
+- To simulate the decode deployment of disaggregated serving, pass `--kv-transfer-config '{"kv_connector":"DecodeBenchConnector","kv_role":"kv_both"}'` to the `vllm serve` invocation. The connector populates KV cache with random values so decode can be profiled in isolation.
+
+- **CUDAGraph capture**: Use `--compilation_config '{"cudagraph_mode": "FULL_DECODE_ONLY"}'` to enable CUDA graph capture for decode only and save KV cache.

examples/offline_inference/audio_language.py

@@ -422,7 +422,7 @@ def parse_args():
     parser.add_argument(
         "--seed",
         type=int,
-        default=None,
+        default=0,
         help="Set the seed when initializing `vllm.LLM`.",
     )
     parser.add_argument(

examples/offline_inference/encoder_decoder_multimodal.py

@@ -77,7 +77,7 @@ def parse_args():
     parser.add_argument(
         "--seed",
         type=int,
-        default=None,
+        default=0,
         help="Set the seed when initializing `vllm.LLM`.",
     )
     return parser.parse_args()

examples/offline_inference/qwen2_5_omni/only_thinker.py

@@ -158,7 +158,7 @@ def parse_args():
     parser.add_argument(
         "--seed",
         type=int,
-        default=None,
+        default=0,
         help="Set the seed when initializing `vllm.LLM`.",
     )
 

examples/offline_inference/qwen3_omni/only_thinker.py

@@ -158,7 +158,7 @@ def parse_args():
     parser.add_argument(
         "--seed",
         type=int,
-        default=None,
+        default=0,
         help="Set the seed when initializing `vllm.LLM`.",
     )
 

examples/offline_inference/vision_language.py

@@ -2031,7 +2031,7 @@ def parse_args():
     parser.add_argument(
         "--seed",
         type=int,
-        default=None,
+        default=0,
         help="Set the seed when initializing `vllm.LLM`.",
     )
 

examples/offline_inference/vision_language_multi_image.py

@@ -1382,7 +1382,7 @@ def run_generate(
     model,
     question: str,
     image_urls: list[str],
-    seed: int | None,
+    seed: int,
     tensor_parallel_size: int | None,
 ):
     req_data = model_example_map[model](question, image_urls)
@@ -1416,7 +1416,7 @@ def run_chat(
     model: str,
     question: str,
     image_urls: list[str],
-    seed: int | None,
+    seed: int,
     tensor_parallel_size: int | None,
 ):
     req_data = model_example_map[model](question, image_urls)
@@ -1494,7 +1494,7 @@ def parse_args():
     parser.add_argument(
         "--seed",
         type=int,
-        default=None,
+        default=0,
         help="Set the seed when initializing `vllm.LLM`.",
     )
     parser.add_argument(

examples/pooling/plugin/prithvi_geospatial_mae_client.py

@@ -16,7 +16,7 @@ import requests
 # - start vllm in serving mode with the below args
 #   --model='christian-pinto/Prithvi-EO-2.0-300M-TL-VLLM'
 #   --model-impl terratorch
-#   --task embed --trust-remote-code
+#   --trust-remote-code
 #   --skip-tokenizer-init --enforce-eager
 #   --io-processor-plugin terratorch_segmentation
 #   --enable-mm-embeds

examples/pooling/pooling/vision_language_pooling.py

@@ -305,7 +305,7 @@ def get_query(modality: QueryModality):
     raise ValueError(msg)
 
 
-def run_encode(model: str, modality: QueryModality, seed: int | None):
+def run_encode(model: str, modality: QueryModality, seed: int):
     query = get_query(modality)
     req_data = model_example_map[model](query)
 
@@ -335,7 +335,7 @@ def run_encode(model: str, modality: QueryModality, seed: int | None):
         print("-" * 50)
 
 
-def run_score(model: str, modality: QueryModality, seed: int | None):
+def run_score(model: str, modality: QueryModality, seed: int):
     query = get_query(modality)
     req_data = model_example_map[model](query)
 
@@ -390,7 +390,7 @@ def parse_args():
     parser.add_argument(
         "--seed",
         type=int,
-        default=None,
+        default=0,
         help="Set the seed when initializing `vllm.LLM`.",
     )
     return parser.parse_args()

requirements/kv_connectors.txt

@@ -1,2 +1,2 @@
-lmcache
+lmcache >= 0.3.10.post1
 nixl >= 0.7.1 # Required for disaggregated prefill

tests/compile/test_config.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import copy
-import logging
 from contextlib import nullcontext
 from unittest.mock import patch
 
@@ -13,7 +12,6 @@ from vllm.compilation.fix_functionalization import FixFunctionalizationPass
 from vllm.config import CompilationConfig, CUDAGraphMode, ParallelConfig, VllmConfig
 from vllm.config.compilation import CompilationMode, PassConfig
 from vllm.engine.arg_utils import EngineArgs
-from vllm.logger import _print_warning_once
 from vllm.platforms import current_platform
 from vllm.utils.torch_utils import _is_torch_equal_or_newer
 
@@ -290,7 +288,7 @@ def test_moe_splitting_ops_deepep_ht_attn_fusion_no_inductor():
         ),
         compilation_config=CompilationConfig(
             mode=CompilationMode.VLLM_COMPILE,
-            pass_config={"enable_attn_fusion": True, "enable_noop": True},
+            pass_config={"fuse_attn_quant": True, "eliminate_noops": True},
             custom_ops=["+quant_fp8"],
             cudagraph_mode=CUDAGraphMode.PIECEWISE,
         ),
@@ -442,62 +440,3 @@ def test_cudagraph_sizes_post_init(
             vllm_config.compilation_config.max_cudagraph_capture_size
             == expected_max_size
         )
-
-
-def test_pass_config_deprecation(caplog_vllm):
-    caplog_vllm.set_level(logging.WARNING)
-
-    # Clear cache to ensure warnings are re-issued
-    _print_warning_once.cache_clear()
-
-    # Test enable_fusion -> fuse_norm_quant, fuse_act_quant
-    caplog_vllm.clear()
-    config = PassConfig(enable_fusion=True)
-    assert "enable_fusion is deprecated" in caplog_vllm.text
-    assert config.fuse_norm_quant is True
-    assert config.fuse_act_quant is True
-    assert config.enable_fusion is True
-
-    # Test enable_attn_fusion -> fuse_attn_quant
-    caplog_vllm.clear()
-    config = PassConfig(enable_attn_fusion=True)
-    assert "enable_attn_fusion is deprecated" in caplog_vllm.text
-    assert config.fuse_attn_quant is True
-    assert config.enable_attn_fusion is True
-
-    # Test enable_noop -> eliminate_noops
-    caplog_vllm.clear()
-    config = PassConfig(enable_noop=True)
-    assert "enable_noop is deprecated" in caplog_vllm.text
-    assert config.eliminate_noops is True
-    assert config.enable_noop is True
-
-    # Test enable_sequence_parallelism -> enable_sp
-    caplog_vllm.clear()
-    config = PassConfig(enable_sequence_parallelism=True)
-    assert "enable_sequence_parallelism is deprecated" in caplog_vllm.text
-    assert config.enable_sp is True
-    assert config.enable_sequence_parallelism is True
-
-    # Test enable_async_tp -> fuse_gemm_comms
-    caplog_vllm.clear()
-    config = PassConfig(enable_async_tp=True)
-    assert "enable_async_tp is deprecated" in caplog_vllm.text
-    assert config.fuse_gemm_comms is True
-    assert config.enable_async_tp is True
-
-    # Test enable_fi_allreduce_fusion -> fuse_allreduce_rms
-    caplog_vllm.clear()
-    config = PassConfig(enable_fi_allreduce_fusion=True)
-    assert "enable_fi_allreduce_fusion is deprecated" in caplog_vllm.text
-    assert config.fuse_allreduce_rms is True
-    assert config.enable_fi_allreduce_fusion is True
-
-    # Test hash consistency
-    config_old = PassConfig(enable_fusion=True)
-    config_new = PassConfig(fuse_norm_quant=True, fuse_act_quant=True)
-    assert config_old.compute_hash() == config_new.compute_hash()
-
-    config_old = PassConfig(enable_async_tp=True)
-    config_new = PassConfig(fuse_gemm_comms=True)
-    assert config_old.compute_hash() == config_new.compute_hash()

tests/conftest.py

@@ -741,7 +741,7 @@ class VllmRunner:
         tokenizer_name: str | None = None,
         tokenizer_mode: str = "auto",
         trust_remote_code: bool = True,
-        seed: int | None = 0,
+        seed: int = 0,
         max_model_len: int | None = 1024,
         dtype: str = "auto",
         disable_log_stats: bool = True,

tests/entrypoints/openai/test_chat_error.py

@@ -0,0 +1,228 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass, field
+from http import HTTPStatus
+from typing import Any
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from vllm.config.multimodal import MultiModalConfig
+from vllm.entrypoints.openai.protocol import ChatCompletionRequest, ErrorResponse
+from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
+from vllm.entrypoints.openai.serving_models import BaseModelPath, OpenAIServingModels
+from vllm.outputs import CompletionOutput, RequestOutput
+from vllm.transformers_utils.tokenizer import get_tokenizer
+from vllm.v1.engine.async_llm import AsyncLLM
+
+MODEL_NAME = "openai-community/gpt2"
+MODEL_NAME_SHORT = "gpt2"
+BASE_MODEL_PATHS = [
+    BaseModelPath(name=MODEL_NAME, model_path=MODEL_NAME),
+    BaseModelPath(name=MODEL_NAME_SHORT, model_path=MODEL_NAME_SHORT),
+]
+
+
+@dataclass
+class MockHFConfig:
+    model_type: str = "any"
+
+
+@dataclass
+class MockModelConfig:
+    task = "generate"
+    runner_type = "generate"
+    tokenizer = MODEL_NAME
+    trust_remote_code = False
+    tokenizer_mode = "auto"
+    max_model_len = 100
+    tokenizer_revision = None
+    multimodal_config = MultiModalConfig()
+    hf_config = MockHFConfig()
+    logits_processor_pattern = None
+    logits_processors: list[str] | None = None
+    diff_sampling_param: dict | None = None
+    allowed_local_media_path: str = ""
+    allowed_media_domains: list[str] | None = None
+    encoder_config = None
+    generation_config: str = "auto"
+    media_io_kwargs: dict[str, dict[str, Any]] = field(default_factory=dict)
+    skip_tokenizer_init = False
+
+    def get_diff_sampling_param(self):
+        return self.diff_sampling_param or {}
+
+
+def _build_serving_chat(engine: AsyncLLM) -> OpenAIServingChat:
+    models = OpenAIServingModels(
+        engine_client=engine,
+        base_model_paths=BASE_MODEL_PATHS,
+    )
+    serving_chat = OpenAIServingChat(
+        engine,
+        models,
+        response_role="assistant",
+        request_logger=None,
+        chat_template=None,
+        chat_template_content_format="auto",
+    )
+
+    async def _fake_process_inputs(
+        request_id,
+        engine_prompt,
+        sampling_params,
+        *,
+        lora_request,
+        trace_headers,
+        priority,
+    ):
+        return dict(engine_prompt), {}
+
+    async def _fake_preprocess_chat(*args, **kwargs):
+        # return conversation, request_prompts, engine_prompts
+        return (
+            [{"role": "user", "content": "Test"}],
+            [[1, 2, 3]],
+            [{"prompt_token_ids": [1, 2, 3]}],
+        )
+
+    serving_chat._process_inputs = AsyncMock(side_effect=_fake_process_inputs)
+    serving_chat._preprocess_chat = AsyncMock(side_effect=_fake_preprocess_chat)
+    return serving_chat
+
+
+@pytest.mark.asyncio
+async def test_chat_error_non_stream():
+    """test finish_reason='error' returns 500 InternalServerError (non-streaming)"""
+    mock_engine = MagicMock(spec=AsyncLLM)
+    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
+    mock_engine.errored = False
+    mock_engine.model_config = MockModelConfig()
+    mock_engine.input_processor = MagicMock()
+    mock_engine.io_processor = MagicMock()
+
+    serving_chat = _build_serving_chat(mock_engine)
+
+    completion_output = CompletionOutput(
+        index=0,
+        text="",
+        token_ids=[],
+        cumulative_logprob=None,
+        logprobs=None,
+        finish_reason="error",
+    )
+
+    request_output = RequestOutput(
+        request_id="test-id",
+        prompt="Test prompt",
+        prompt_token_ids=[1, 2, 3],
+        prompt_logprobs=None,
+        outputs=[completion_output],
+        finished=True,
+        metrics=None,
+        lora_request=None,
+        encoder_prompt=None,
+        encoder_prompt_token_ids=None,
+    )
+
+    async def mock_generate(*args, **kwargs):
+        yield request_output
+
+    mock_engine.generate = MagicMock(side_effect=mock_generate)
+
+    request = ChatCompletionRequest(
+        model=MODEL_NAME,
+        messages=[{"role": "user", "content": "Test prompt"}],
+        max_tokens=10,
+        stream=False,
+    )
+
+    response = await serving_chat.create_chat_completion(request)
+
+    assert isinstance(response, ErrorResponse)
+    assert response.error.type == "InternalServerError"
+    assert response.error.message == "Internal server error"
+    assert response.error.code == HTTPStatus.INTERNAL_SERVER_ERROR
+
+
+@pytest.mark.asyncio
+async def test_chat_error_stream():
+    """test finish_reason='error' returns 500 InternalServerError (streaming)"""
+    mock_engine = MagicMock(spec=AsyncLLM)
+    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
+    mock_engine.errored = False
+    mock_engine.model_config = MockModelConfig()
+    mock_engine.input_processor = MagicMock()
+    mock_engine.io_processor = MagicMock()
+
+    serving_chat = _build_serving_chat(mock_engine)
+
+    completion_output_1 = CompletionOutput(
+        index=0,
+        text="Hello",
+        token_ids=[100],
+        cumulative_logprob=None,
+        logprobs=None,
+        finish_reason=None,
+    )
+
+    request_output_1 = RequestOutput(
+        request_id="test-id",
+        prompt="Test prompt",
+        prompt_token_ids=[1, 2, 3],
+        prompt_logprobs=None,
+        outputs=[completion_output_1],
+        finished=False,
+        metrics=None,
+        lora_request=None,
+        encoder_prompt=None,
+        encoder_prompt_token_ids=None,
+    )
+
+    completion_output_2 = CompletionOutput(
+        index=0,
+        text="Hello",
+        token_ids=[100],
+        cumulative_logprob=None,
+        logprobs=None,
+        finish_reason="error",
+    )
+
+    request_output_2 = RequestOutput(
+        request_id="test-id",
+        prompt="Test prompt",
+        prompt_token_ids=[1, 2, 3],
+        prompt_logprobs=None,
+        outputs=[completion_output_2],
+        finished=True,
+        metrics=None,
+        lora_request=None,
+        encoder_prompt=None,
+        encoder_prompt_token_ids=None,
+    )
+
+    async def mock_generate(*args, **kwargs):
+        yield request_output_1
+        yield request_output_2
+
+    mock_engine.generate = MagicMock(side_effect=mock_generate)
+
+    request = ChatCompletionRequest(
+        model=MODEL_NAME,
+        messages=[{"role": "user", "content": "Test prompt"}],
+        max_tokens=10,
+        stream=True,
+    )
+
+    response = await serving_chat.create_chat_completion(request)
+
+    chunks = []
+    async for chunk in response:
+        chunks.append(chunk)
+
+    assert len(chunks) >= 2
+    assert any("Internal server error" in chunk for chunk in chunks), (
+        f"Expected error message in chunks: {chunks}"
+    )
+    assert chunks[-1] == "data: [DONE]\n\n"

tests/entrypoints/openai/test_completion_error.py

@@ -0,0 +1,216 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass, field
+from http import HTTPStatus
+from typing import Any
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from vllm.config.multimodal import MultiModalConfig
+from vllm.entrypoints.openai.protocol import CompletionRequest, ErrorResponse
+from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
+from vllm.entrypoints.openai.serving_models import BaseModelPath, OpenAIServingModels
+from vllm.outputs import CompletionOutput, RequestOutput
+from vllm.transformers_utils.tokenizer import get_tokenizer
+from vllm.v1.engine.async_llm import AsyncLLM
+
+MODEL_NAME = "openai-community/gpt2"
+MODEL_NAME_SHORT = "gpt2"
+BASE_MODEL_PATHS = [
+    BaseModelPath(name=MODEL_NAME, model_path=MODEL_NAME),
+    BaseModelPath(name=MODEL_NAME_SHORT, model_path=MODEL_NAME_SHORT),
+]
+
+
+@dataclass
+class MockHFConfig:
+    model_type: str = "any"
+
+
+@dataclass
+class MockModelConfig:
+    task = "generate"
+    runner_type = "generate"
+    tokenizer = MODEL_NAME
+    trust_remote_code = False
+    tokenizer_mode = "auto"
+    max_model_len = 100
+    tokenizer_revision = None
+    multimodal_config = MultiModalConfig()
+    hf_config = MockHFConfig()
+    logits_processor_pattern = None
+    logits_processors: list[str] | None = None
+    diff_sampling_param: dict | None = None
+    allowed_local_media_path: str = ""
+    allowed_media_domains: list[str] | None = None
+    encoder_config = None
+    generation_config: str = "auto"
+    media_io_kwargs: dict[str, dict[str, Any]] = field(default_factory=dict)
+    skip_tokenizer_init = False
+
+    def get_diff_sampling_param(self):
+        return self.diff_sampling_param or {}
+
+
+def _build_serving_completion(engine: AsyncLLM) -> OpenAIServingCompletion:
+    models = OpenAIServingModels(
+        engine_client=engine,
+        base_model_paths=BASE_MODEL_PATHS,
+    )
+    serving_completion = OpenAIServingCompletion(
+        engine,
+        models,
+        request_logger=None,
+    )
+
+    async def _fake_process_inputs(
+        request_id,
+        engine_prompt,
+        sampling_params,
+        *,
+        lora_request,
+        trace_headers,
+        priority,
+    ):
+        return dict(engine_prompt), {}
+
+    serving_completion._process_inputs = AsyncMock(side_effect=_fake_process_inputs)
+    return serving_completion
+
+
+@pytest.mark.asyncio
+async def test_completion_error_non_stream():
+    """test finish_reason='error' returns 500 InternalServerError (non-streaming)"""
+    mock_engine = MagicMock(spec=AsyncLLM)
+    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
+    mock_engine.errored = False
+    mock_engine.model_config = MockModelConfig()
+    mock_engine.input_processor = MagicMock()
+    mock_engine.io_processor = MagicMock()
+
+    serving_completion = _build_serving_completion(mock_engine)
+
+    completion_output = CompletionOutput(
+        index=0,
+        text="",
+        token_ids=[],
+        cumulative_logprob=None,
+        logprobs=None,
+        finish_reason="error",
+    )
+
+    request_output = RequestOutput(
+        request_id="test-id",
+        prompt="Test prompt",
+        prompt_token_ids=[1, 2, 3],
+        prompt_logprobs=None,
+        outputs=[completion_output],
+        finished=True,
+        metrics=None,
+        lora_request=None,
+        encoder_prompt=None,
+        encoder_prompt_token_ids=None,
+    )
+
+    async def mock_generate(*args, **kwargs):
+        yield request_output
+
+    mock_engine.generate = MagicMock(side_effect=mock_generate)
+
+    request = CompletionRequest(
+        model=MODEL_NAME,
+        prompt="Test prompt",
+        max_tokens=10,
+        stream=False,
+    )
+
+    response = await serving_completion.create_completion(request)
+
+    assert isinstance(response, ErrorResponse)
+    assert response.error.type == "InternalServerError"
+    assert response.error.message == "Internal server error"
+    assert response.error.code == HTTPStatus.INTERNAL_SERVER_ERROR
+
+
+@pytest.mark.asyncio
+async def test_completion_error_stream():
+    """test finish_reason='error' returns 500 InternalServerError (streaming)"""
+    mock_engine = MagicMock(spec=AsyncLLM)
+    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
+    mock_engine.errored = False
+    mock_engine.model_config = MockModelConfig()
+    mock_engine.input_processor = MagicMock()
+    mock_engine.io_processor = MagicMock()
+
+    serving_completion = _build_serving_completion(mock_engine)
+
+    completion_output_1 = CompletionOutput(
+        index=0,
+        text="Hello",
+        token_ids=[100],
+        cumulative_logprob=None,
+        logprobs=None,
+        finish_reason=None,
+    )
+
+    request_output_1 = RequestOutput(
+        request_id="test-id",
+        prompt="Test prompt",
+        prompt_token_ids=[1, 2, 3],
+        prompt_logprobs=None,
+        outputs=[completion_output_1],
+        finished=False,
+        metrics=None,
+        lora_request=None,
+        encoder_prompt=None,
+        encoder_prompt_token_ids=None,
+    )
+
+    completion_output_2 = CompletionOutput(
+        index=0,
+        text="Hello",
+        token_ids=[100],
+        cumulative_logprob=None,
+        logprobs=None,
+        finish_reason="error",
+    )
+
+    request_output_2 = RequestOutput(
+        request_id="test-id",
+        prompt="Test prompt",
+        prompt_token_ids=[1, 2, 3],
+        prompt_logprobs=None,
+        outputs=[completion_output_2],
+        finished=True,
+        metrics=None,
+        lora_request=None,
+        encoder_prompt=None,
+        encoder_prompt_token_ids=None,
+    )
+
+    async def mock_generate(*args, **kwargs):
+        yield request_output_1
+        yield request_output_2
+
+    mock_engine.generate = MagicMock(side_effect=mock_generate)
+
+    request = CompletionRequest(
+        model=MODEL_NAME,
+        prompt="Test prompt",
+        max_tokens=10,
+        stream=True,
+    )
+
+    response = await serving_completion.create_completion(request)
+
+    chunks = []
+    async for chunk in response:
+        chunks.append(chunk)
+
+    assert len(chunks) >= 2
+    assert any("Internal server error" in chunk for chunk in chunks), (
+        f"Expected error message in chunks: {chunks}"
+    )
+    assert chunks[-1] == "data: [DONE]\n\n"

tests/entrypoints/openai/test_responses_error.py

@@ -0,0 +1,89 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from http import HTTPStatus
+from unittest.mock import MagicMock
+
+import pytest
+
+from vllm.entrypoints.openai.protocol import ErrorResponse
+from vllm.entrypoints.openai.serving_engine import GenerationError, OpenAIServing
+
+
+@pytest.mark.asyncio
+async def test_raise_if_error_raises_generation_error():
+    """test _raise_if_error raises GenerationError"""
+    # create a minimal OpenAIServing instance
+    mock_engine = MagicMock()
+    mock_engine.model_config = MagicMock()
+    mock_engine.model_config.max_model_len = 100
+    mock_models = MagicMock()
+
+    serving = OpenAIServing(
+        engine_client=mock_engine,
+        models=mock_models,
+        request_logger=None,
+    )
+
+    # test that error finish_reason raises GenerationError
+    with pytest.raises(GenerationError) as exc_info:
+        serving._raise_if_error("error", "test-request-id")
+
+    assert str(exc_info.value) == "Internal server error"
+    assert exc_info.value.status_code == HTTPStatus.INTERNAL_SERVER_ERROR
+
+    # test that other finish_reasons don't raise
+    serving._raise_if_error("stop", "test-request-id")  # should not raise
+    serving._raise_if_error("length", "test-request-id")  # should not raise
+    serving._raise_if_error(None, "test-request-id")  # should not raise
+
+
+@pytest.mark.asyncio
+async def test_convert_generation_error_to_response():
+    """test _convert_generation_error_to_response creates proper ErrorResponse"""
+    mock_engine = MagicMock()
+    mock_engine.model_config = MagicMock()
+    mock_engine.model_config.max_model_len = 100
+    mock_models = MagicMock()
+
+    serving = OpenAIServing(
+        engine_client=mock_engine,
+        models=mock_models,
+        request_logger=None,
+    )
+
+    # create a GenerationError
+    gen_error = GenerationError("Internal server error")
+
+    # convert to ErrorResponse
+    error_response = serving._convert_generation_error_to_response(gen_error)
+
+    assert isinstance(error_response, ErrorResponse)
+    assert error_response.error.type == "InternalServerError"
+    assert error_response.error.message == "Internal server error"
+    assert error_response.error.code == HTTPStatus.INTERNAL_SERVER_ERROR
+
+
+@pytest.mark.asyncio
+async def test_convert_generation_error_to_streaming_response():
+    """test _convert_generation_error_to_streaming_response output"""
+    mock_engine = MagicMock()
+    mock_engine.model_config = MagicMock()
+    mock_engine.model_config.max_model_len = 100
+    mock_models = MagicMock()
+
+    serving = OpenAIServing(
+        engine_client=mock_engine,
+        models=mock_models,
+        request_logger=None,
+    )
+
+    # create a GenerationError
+    gen_error = GenerationError("Internal server error")
+
+    # convert to streaming error response
+    error_json = serving._convert_generation_error_to_streaming_response(gen_error)
+
+    assert isinstance(error_json, str)
+    assert "Internal server error" in error_json
+    assert "InternalServerError" in error_json

tests/kernels/moe/test_ocp_mx_moe.py

@@ -70,12 +70,12 @@ def test_mxfp4_loading_and_execution_moe(vllm_runner, model_case: ModelCase):
             f"{torch.cuda.device_count()}"
         )
 
-    # `cuda_graph_sizes=[16]` to reduce load time.
+    # `cudagraph_capture_sizes=[16]` to reduce load time.
     with vllm_runner(
         model_case.model_id,
         tensor_parallel_size=model_case.tp,
         load_format="dummy",
-        cuda_graph_sizes=[16],
+        cudagraph_capture_sizes=[16],
     ) as llm:
         # Disabled as check_model is broken: https://github.com/vllm-project/vllm/pull/18465#issuecomment-3329880562
         # def check_model(model):

tests/quantization/test_quark.py

@@ -212,11 +212,11 @@ def test_ocp_mx_wikitext_correctness(config: AccuracyTestConfig, tp_size: int):
     task = "wikitext"
     rtol = 0.1
 
-    # Smaller cuda_graph_sizes to speed up the test.
+    # Smaller cudagraph_capture_sizes to speed up the test.
     results = lm_eval.simple_evaluate(
         model="vllm",
         model_args=config.get_model_args(
-            tp_size=tp_size, kwargs={"cuda_graph_sizes": [16]}
+            tp_size=tp_size, kwargs={"cudagraph_capture_sizes": [16]}
         ),
         tasks=task,
         batch_size=64,

tests/test_config.py

@@ -90,64 +90,6 @@ def test_update_config():
         new_config3 = update_config(config3, {"a": "new_value"})
 
 
-# Can remove once --task option is fully deprecated
-@pytest.mark.parametrize(
-    ("model_id", "expected_runner_type", "expected_convert_type", "expected_task"),
-    [
-        ("distilbert/distilgpt2", "generate", "none", "generate"),
-        ("intfloat/multilingual-e5-small", "pooling", "none", "embed"),
-        ("jason9693/Qwen2.5-1.5B-apeach", "pooling", "classify", "classify"),
-        ("cross-encoder/ms-marco-MiniLM-L-6-v2", "pooling", "none", "classify"),
-        ("Qwen/Qwen2.5-Math-RM-72B", "pooling", "none", "embed"),
-        ("openai/whisper-small", "generate", "none", "transcription"),
-    ],
-)
-def test_auto_task(
-    model_id, expected_runner_type, expected_convert_type, expected_task
-):
-    config = ModelConfig(model_id, task="auto")
-
-    assert config.runner_type == expected_runner_type
-    assert config.convert_type == expected_convert_type
-
-
-# Can remove once --task option is fully deprecated
-@pytest.mark.parametrize(
-    ("model_id", "expected_runner_type", "expected_convert_type", "expected_task"),
-    [
-        ("distilbert/distilgpt2", "pooling", "embed", "embed"),
-        ("intfloat/multilingual-e5-small", "pooling", "embed", "embed"),
-        ("jason9693/Qwen2.5-1.5B-apeach", "pooling", "classify", "classify"),
-        ("cross-encoder/ms-marco-MiniLM-L-6-v2", "pooling", "classify", "classify"),
-        ("Qwen/Qwen2.5-Math-RM-72B", "pooling", "embed", "embed"),
-        ("openai/whisper-small", "pooling", "embed", "embed"),
-    ],
-)
-def test_score_task(
-    model_id, expected_runner_type, expected_convert_type, expected_task
-):
-    config = ModelConfig(model_id, task="score")
-
-    assert config.runner_type == expected_runner_type
-    assert config.convert_type == expected_convert_type
-
-
-# Can remove once --task option is fully deprecated
-@pytest.mark.parametrize(
-    ("model_id", "expected_runner_type", "expected_convert_type", "expected_task"),
-    [
-        ("openai/whisper-small", "generate", "none", "transcription"),
-    ],
-)
-def test_transcription_task(
-    model_id, expected_runner_type, expected_convert_type, expected_task
-):
-    config = ModelConfig(model_id, task="transcription")
-
-    assert config.runner_type == expected_runner_type
-    assert config.convert_type == expected_convert_type
-
-
 @pytest.mark.parametrize(
     ("model_id", "expected_runner_type", "expected_convert_type"),
     [
@@ -1086,7 +1028,7 @@ def test_vllm_config_explicit_overrides():
     )
 
     # Override one field but not others
-    pass_config = PassConfig(enable_noop=False)
+    pass_config = PassConfig(eliminate_noops=False)
     compilation_config = CompilationConfig(pass_config=pass_config)
     config = VllmConfig(
         model_config=regular_model,

tests/test_envs.py

@@ -8,6 +8,7 @@ import pytest
 
 import vllm.envs as envs
 from vllm.envs import (
+    disable_envs_cache,
     enable_envs_cache,
     env_list_with_choices,
     env_set_with_choices,
@@ -57,6 +58,43 @@ def test_getattr_with_cache(monkeypatch: pytest.MonkeyPatch):
     envs.__getattr__ = envs.__getattr__.__wrapped__
 
 
+def test_getattr_with_reset(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("VLLM_HOST_IP", "1.1.1.1")
+    # __getattr__ is not decorated with functools.cache
+    assert not hasattr(envs.__getattr__, "cache_info")
+
+    # Enable envs cache and ignore ongoing environment changes
+    enable_envs_cache()
+    assert envs.VLLM_HOST_IP == "1.1.1.1"
+    # With cache enabled, the environment variable value is cached and unchanged
+    monkeypatch.setenv("VLLM_HOST_IP", "2.2.2.2")
+    assert envs.VLLM_HOST_IP == "1.1.1.1"
+
+    disable_envs_cache()
+    assert envs.VLLM_HOST_IP == "2.2.2.2"
+    # After cache disabled, the environment variable value would be synced
+    # with os.environ
+    monkeypatch.setenv("VLLM_HOST_IP", "3.3.3.3")
+    assert envs.VLLM_HOST_IP == "3.3.3.3"
+
+
+def test_is_envs_cache_enabled() -> None:
+    assert not envs._is_envs_cache_enabled()
+    enable_envs_cache()
+    assert envs._is_envs_cache_enabled()
+
+    # Only wrap one-layer of cache, so we only need to
+    # call disable once to reset.
+    enable_envs_cache()
+    enable_envs_cache()
+    enable_envs_cache()
+    disable_envs_cache()
+    assert not envs._is_envs_cache_enabled()
+
+    disable_envs_cache()
+    assert not envs._is_envs_cache_enabled()
+
+
 class TestEnvWithChoices:
     """Test cases for env_with_choices function."""
 

tests/utils.py

@@ -119,7 +119,7 @@ class RemoteOpenAIServer:
         vllm_serve_args: list[str],
         *,
         env_dict: dict[str, str] | None = None,
-        seed: int | None = 0,
+        seed: int = 0,
         auto_port: bool = True,
         max_wait_seconds: float | None = None,
         override_hf_configs: dict[str, Any] | None = None,
@@ -283,7 +283,7 @@ class RemoteOpenAIServerCustom(RemoteOpenAIServer):
         child_process_fxn: Callable[[dict[str, str] | None, str, list[str]], None],
         *,
         env_dict: dict[str, str] | None = None,
-        seed: int | None = 0,
+        seed: int = 0,
         auto_port: bool = True,
         max_wait_seconds: float | None = None,
     ) -> None:

tests/v1/e2e/test_async_scheduling.py

@@ -152,8 +152,8 @@ def run_tests(
                 m.setenv("VLLM_ATTENTION_BACKEND", "ROCM_AITER_FA")
         else:
             m.setenv("VLLM_ATTENTION_BACKEND", "FLEX_ATTENTION")
-        # lock matmul precision to full FP32
+        # lock matmul precision to full FP32 (IEEE)
-        m.setenv("VLLM_FLOAT32_MATMUL_PRECISION", "highest")
+        m.setenv("VLLM_FLOAT32_MATMUL_PRECISION", "ieee")
         # m.setenv("VLLM_BATCH_INVARIANT", "1")
         outputs: list[tuple[str, list, list]] = []
         for n, (

tests/v1/engine/test_init_error_messaging.py

@@ -0,0 +1,54 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+
+from vllm.v1.core.kv_cache_utils import check_enough_kv_cache_memory
+from vllm.v1.kv_cache_interface import FullAttentionSpec
+
+
+def test_kv_cache_oom_no_memory():
+    from unittest.mock import MagicMock
+
+    config = MagicMock()
+    config.model_config.max_model_len = 2048
+
+    spec = {
+        "layer_0": FullAttentionSpec(
+            block_size=16,
+            num_kv_heads=8,
+            head_size=128,
+            dtype="float16",
+        )
+    }
+
+    with pytest.raises(ValueError):
+        check_enough_kv_cache_memory(config, spec, 0)
+
+
+def test_kv_cache_oom_insufficient_memory(monkeypatch):
+    from unittest.mock import MagicMock
+
+    config = MagicMock()
+    config.model_config.max_model_len = 2048
+    config.cache_config.block_size = 16
+    config.parallel_config.tensor_parallel_size = 1
+    config.parallel_config.pipeline_parallel_size = 1
+    config.parallel_config.decode_context_parallel_size = 1
+
+    monkeypatch.setattr(
+        "vllm.v1.core.kv_cache_utils.max_memory_usage_bytes",
+        lambda c, s: 100 * 1024**3,  # 100 GiB
+    )
+
+    spec = {
+        "layer_0": FullAttentionSpec(
+            block_size=16,
+            num_kv_heads=8,
+            head_size=128,
+            dtype="float16",
+        )
+    }
+
+    with pytest.raises(ValueError):
+        check_enough_kv_cache_memory(config, spec, 1024**3)  # 1 GiB

tests/v1/kv_connector/unit/test_cache_pollution_prevention.py

@@ -0,0 +1,163 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""
+test that invalid blocks are evicted from prefix cache to prevent pollution.
+
+verifies that when sync-loading fails, invalid blocks are removed from the
+prefix cache hash table so future requests cannot match and reuse corrupted data.
+"""
+
+from collections.abc import Callable
+from unittest.mock import Mock
+
+import pytest
+
+from vllm.v1.core.sched.scheduler import Scheduler
+from vllm.v1.request import Request, RequestStatus
+
+from .utils import (
+    create_model_runner_output,
+    create_request,
+    create_scheduler,
+    create_vllm_config,
+)
+
+pytestmark = pytest.mark.cpu_test
+
+
+def _make_get_num_new_matched_tokens(
+    req_num_new_matched_tokens: dict[str, int],
+    async_load: bool,
+) -> Callable[[Request, int], tuple[int, bool]]:
+    def get_num_new_matched_tokens(request: Request, _: int) -> tuple[int, bool]:
+        value = req_num_new_matched_tokens.get(request.request_id, 0)
+        return value, async_load
+
+    return get_num_new_matched_tokens
+
+
+@pytest.fixture
+def fail_scheduler():
+    """scheduler with kv_load_failure_policy='fail'"""
+    vllm_config = create_vllm_config()
+    vllm_config.kv_transfer_config.kv_load_failure_policy = "fail"
+    return create_scheduler(vllm_config)
+
+
+def test_invalid_blocks_evicted_prevents_cache_pollution(
+    fail_scheduler: Scheduler,
+):
+    """
+    verify invalid blocks are evicted to prevent future cache hits.
+
+    scenario:
+    1. request 1 loads externally-computed blocks (sync mode)
+    2. some blocks fail to load and are marked invalid
+    3. with fail policy, invalid blocks should be evicted from prefix cache
+    4. request is marked as FINISHED_ERROR
+    """
+    num_prompt_blocks = 100
+    num_external_computed_blocks = 99
+    invalid_block_idx = 50
+
+    num_prompt_tokens = num_prompt_blocks * fail_scheduler.block_size
+    num_external_computed_tokens = (
+        num_external_computed_blocks * fail_scheduler.block_size
+    )
+
+    # request 1: will have invalid blocks
+    request1 = create_request(num_tokens=num_prompt_tokens, request_id=1)
+    fail_scheduler.add_request(request=request1)
+
+    req_num_new_matched_tokens = {
+        request1.request_id: num_external_computed_tokens,
+    }
+
+    # mock connector indicating sync load
+    fail_scheduler.connector = Mock()
+    fail_scheduler.connector.get_num_new_matched_tokens.side_effect = (
+        _make_get_num_new_matched_tokens(req_num_new_matched_tokens, False)
+    )
+    fail_scheduler.connector.request_finished.return_value = (False, None)
+    fail_scheduler.connector.take_events.return_value = ()
+
+    scheduler_output = fail_scheduler.schedule()
+
+    # request should be running with sync KV load
+    assert len(fail_scheduler.running) == 1
+    assert request1.status == RequestStatus.RUNNING
+
+    # get allocated block IDs
+    req_block_ids = scheduler_output.scheduled_new_reqs[0].block_ids[0]
+    invalid_block_id = req_block_ids[invalid_block_idx]
+    invalid_block_ids = {invalid_block_id}
+
+    # get the block object to verify eviction later
+    block = fail_scheduler.kv_cache_manager.block_pool.blocks[invalid_block_id]
+
+    # cache the blocks to simulate they've been computed and cached
+    # (in real scenario blocks would be cached after compute)
+    fail_scheduler.kv_cache_manager.cache_blocks(request1, num_external_computed_tokens)
+
+    # verify block has a hash (is cached) before reporting invalid blocks
+    assert block.block_hash is not None, (
+        f"block {invalid_block_id} should be cached (have a hash) before "
+        f"eviction test, but hash is None"
+    )
+
+    # report invalid blocks
+    model_runner_output = create_model_runner_output(
+        [request1],
+        invalid_block_ids=invalid_block_ids,
+        use_eos=False,
+    )
+
+    fail_scheduler.update_from_output(scheduler_output, model_runner_output)
+
+    # verify request finished with error (fail policy)
+    assert request1.status == RequestStatus.FINISHED_ERROR
+
+    # critical assertion: invalid block and all subsequent blocks should be evicted
+    # all blocks from invalid_block_idx onwards become invalid since they were
+    # computed based on the failed block
+    for idx in range(invalid_block_idx, len(req_block_ids)):
+        block_id = req_block_ids[idx]
+        block_obj = fail_scheduler.kv_cache_manager.block_pool.blocks[block_id]
+        assert block_obj.block_hash is None, (
+            f"block {block_id} at index {idx} should have been evicted "
+            f"(hash reset to None), but hash is {block_obj.block_hash}. "
+            f"All blocks from index {invalid_block_idx} onwards should be evicted "
+            f"since they depend on the invalid block at index {invalid_block_idx}."
+        )
+
+    # verify cache contains exactly the valid blocks (before first affected block)
+    # and none of the invalid blocks (from first affected block onwards)
+
+    # valid blocks: all blocks before invalid_block_idx should be cached
+    for idx in range(invalid_block_idx):
+        block_id = req_block_ids[idx]
+        block_obj = fail_scheduler.kv_cache_manager.block_pool.blocks[block_id]
+        assert block_obj.block_hash is not None, (
+            f"valid block {block_id} at index {idx} should still be cached "
+            f"(have a hash), but hash is None. Only blocks from index "
+            f"{invalid_block_idx} onwards should be evicted."
+        )
+
+    # invalid blocks: verify they're not in the cached_block_hash_to_block map
+    cached_blocks = (
+        fail_scheduler.kv_cache_manager.block_pool.cached_block_hash_to_block
+    )
+    cached_block_ids = {
+        b.block_id
+        for blocks_val in cached_blocks._cache.values()
+        for b in (
+            [blocks_val] if not isinstance(blocks_val, dict) else blocks_val.values()
+        )
+    }
+
+    for idx in range(invalid_block_idx, len(req_block_ids)):
+        block_id = req_block_ids[idx]
+        assert block_id not in cached_block_ids, (
+            f"invalid block {block_id} at index {idx} should not be in cache hash table"
+        )

tests/v1/kv_connector/unit/test_error_propagation.py

@@ -0,0 +1,147 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Callable
+from unittest.mock import Mock
+
+import pytest
+
+from vllm.v1.core.sched.scheduler import Scheduler
+from vllm.v1.request import FinishReason, Request, RequestStatus
+
+from .utils import (
+    create_model_runner_output,
+    create_request,
+    create_scheduler,
+    create_vllm_config,
+)
+
+pytestmark = pytest.mark.cpu_test
+
+
+def _make_get_num_new_matched_tokens(
+    req_num_new_matched_tokens: dict[str, int],
+    async_load: bool,
+) -> Callable[[Request, int], tuple[int, bool]]:
+    def get_num_new_matched_tokens(request: Request, _: int) -> tuple[int, bool]:
+        value = req_num_new_matched_tokens.get(request.request_id, 0)
+        return value, async_load
+
+    return get_num_new_matched_tokens
+
+
+@pytest.fixture
+def fail_scheduler():
+    """scheduler with kv_load_failure_policy='fail'"""
+    vllm_config = create_vllm_config()
+    vllm_config.kv_transfer_config.kv_load_failure_policy = "fail"
+    return create_scheduler(vllm_config)
+
+
+def test_error_propagation_sync_load(fail_scheduler: Scheduler):
+    """test invalid_block_ids with fail policy -> FINISHED_ERROR (sync load)"""
+    num_prompt_blocks = 100
+    num_external_computed_blocks = 99
+    invalid_block_idx = 50
+
+    num_prompt_tokens = num_prompt_blocks * fail_scheduler.block_size
+    num_external_computed_tokens = (
+        num_external_computed_blocks * fail_scheduler.block_size
+    )
+
+    request = create_request(num_tokens=num_prompt_tokens)
+    fail_scheduler.add_request(request=request)
+
+    req_num_new_matched_tokens = {
+        request.request_id: num_external_computed_tokens,
+    }
+
+    fail_scheduler.connector = Mock()
+    fail_scheduler.connector.get_num_new_matched_tokens.side_effect = (
+        _make_get_num_new_matched_tokens(req_num_new_matched_tokens, False)
+    )
+    fail_scheduler.connector.request_finished.return_value = (False, None)
+    fail_scheduler.connector.take_events.return_value = ()
+
+    scheduler_output = fail_scheduler.schedule()
+
+    assert len(fail_scheduler.running) == 1
+    assert len(scheduler_output.scheduled_new_reqs) == 1
+    assert fail_scheduler.connector.get_num_new_matched_tokens.call_count == 1
+
+    req_block_ids = scheduler_output.scheduled_new_reqs[0].block_ids[0]
+    invalid_block_ids = {req_block_ids[invalid_block_idx]}
+    model_runner_output = create_model_runner_output(
+        [request],
+        invalid_block_ids=invalid_block_ids,
+        use_eos=True,
+    )
+
+    outputs = fail_scheduler.update_from_output(scheduler_output, model_runner_output)
+
+    assert request.status == RequestStatus.FINISHED_ERROR
+    assert request.get_finished_reason() == FinishReason.ERROR
+
+    assert len(outputs) == 1
+    engine_outputs = next(iter(outputs.values()))
+    assert len(engine_outputs.outputs) == 1
+    output = engine_outputs.outputs[0]
+    assert output.request_id == request.request_id
+    assert output.finish_reason == FinishReason.ERROR
+
+    assert len(fail_scheduler.running) == 0
+
+
+def test_error_propagation_async_load(fail_scheduler: Scheduler):
+    """test invalid_block_ids with fail policy -> FINISHED_ERROR (async load)"""
+    num_prompt_blocks = 100
+    num_external_computed_blocks = 99
+    invalid_block_idx = 50
+
+    num_prompt_tokens = num_prompt_blocks * fail_scheduler.block_size
+    num_external_computed_tokens = (
+        num_external_computed_blocks * fail_scheduler.block_size
+    )
+
+    request = create_request(num_tokens=num_prompt_tokens)
+    fail_scheduler.add_request(request=request)
+
+    req_num_new_matched_tokens = {
+        request.request_id: num_external_computed_tokens,
+    }
+
+    fail_scheduler.connector = Mock()
+    fail_scheduler.connector.get_num_new_matched_tokens.side_effect = (
+        _make_get_num_new_matched_tokens(req_num_new_matched_tokens, True)
+    )
+    fail_scheduler.connector.request_finished.return_value = (False, None)
+    fail_scheduler.connector.take_events.return_value = ()
+
+    scheduler_output = fail_scheduler.schedule()
+
+    assert len(fail_scheduler.waiting) == 1
+    assert request.status == RequestStatus.WAITING_FOR_REMOTE_KVS
+    assert request.num_computed_tokens == 0
+
+    (req_block_ids,) = fail_scheduler.kv_cache_manager.get_block_ids(request.request_id)
+    invalid_block_ids = {req_block_ids[invalid_block_idx]}
+    model_runner_output = create_model_runner_output(
+        reqs=[],
+        finished_recving=set(),
+        invalid_block_ids=invalid_block_ids,
+        use_eos=True,
+    )
+
+    outputs = fail_scheduler.update_from_output(scheduler_output, model_runner_output)
+
+    assert request.status == RequestStatus.FINISHED_ERROR
+    assert request.get_finished_reason() == FinishReason.ERROR
+
+    assert len(outputs) == 1
+    engine_outputs = next(iter(outputs.values()))
+    assert len(engine_outputs.outputs) == 1
+    output = engine_outputs.outputs[0]
+    assert output.request_id == request.request_id
+    assert output.finish_reason == FinishReason.ERROR
+
+    assert len(fail_scheduler.waiting) == 0

tests/v1/kv_connector/unit/test_invalid_blocks_correctness.py

@@ -0,0 +1,454 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""
+Tests for correctness in invalid block handling.
+
+These tests verify correct behavior in three scenarios:
+1. Sync recompute case: Blocks should not be freed for running requests
+   that need to recompute invalid blocks
+2. Sync fail case: Invalid blocks must be evicted from cache when request fails
+3. Async recompute case: Invalid blocks should not be cached after transfer
+"""
+
+from collections.abc import Callable
+from unittest.mock import Mock
+
+import pytest
+
+from vllm.v1.core.sched.scheduler import Scheduler
+from vllm.v1.request import FinishReason, Request, RequestStatus
+
+from .utils import (
+    create_model_runner_output,
+    create_request,
+    create_scheduler,
+    create_vllm_config,
+)
+
+pytestmark = pytest.mark.cpu_test
+
+
+def _make_get_num_new_matched_tokens(
+    req_num_new_matched_tokens: dict[str, int],
+    async_load: bool,
+) -> Callable[[Request, int], tuple[int, bool]]:
+    def get_num_new_matched_tokens(request: Request, _: int) -> tuple[int, bool]:
+        value = req_num_new_matched_tokens.get(request.request_id, 0)
+        return value, async_load
+
+    return get_num_new_matched_tokens
+
+
+@pytest.fixture
+def fail_scheduler():
+    """scheduler with kv_load_failure_policy='fail'"""
+    vllm_config = create_vllm_config()
+    vllm_config.kv_transfer_config.kv_load_failure_policy = "fail"
+    return create_scheduler(vllm_config)
+
+
+@pytest.fixture
+def recompute_scheduler():
+    """scheduler with kv_load_failure_policy='recompute'"""
+    vllm_config = create_vllm_config()
+    vllm_config.kv_transfer_config.kv_load_failure_policy = "recompute"
+    return create_scheduler(vllm_config)
+
+
+def test_sync_recompute_blocks_not_freed_for_running_requests(
+    recompute_scheduler: Scheduler,
+):
+    """
+    Test sync recompute case - blocks must not be freed for running requests.
+
+    When a running request has invalid blocks and retry_policy is 'recompute':
+    1. Request should remain in RUNNING state
+    2. num_computed_tokens should be truncated to invalid block boundary
+    3. Blocks should NOT be freed (request still needs them for recomputation)
+    4. Request should remain in scheduler.requests and scheduler.running
+    """
+    num_prompt_blocks = 100
+    num_external_computed_blocks = 99
+    invalid_block_idx = 50
+
+    num_prompt_tokens = num_prompt_blocks * recompute_scheduler.block_size
+    num_external_computed_tokens = (
+        num_external_computed_blocks * recompute_scheduler.block_size
+    )
+
+    request = create_request(num_tokens=num_prompt_tokens)
+    recompute_scheduler.add_request(request=request)
+
+    req_num_new_matched_tokens = {
+        request.request_id: num_external_computed_tokens,
+    }
+
+    # mock connector indicating sync load
+    recompute_scheduler.connector = Mock()
+    recompute_scheduler.connector.get_num_new_matched_tokens.side_effect = (
+        _make_get_num_new_matched_tokens(req_num_new_matched_tokens, False)
+    )
+    recompute_scheduler.connector.request_finished.return_value = (False, None)
+    recompute_scheduler.connector.take_events.return_value = ()
+
+    scheduler_output = recompute_scheduler.schedule()
+
+    # request should be running with sync KV load
+    assert len(recompute_scheduler.running) == 1
+    assert len(scheduler_output.scheduled_new_reqs) == 1
+    assert request.status == RequestStatus.RUNNING
+
+    # get the allocated block IDs before invalid blocks are reported
+    req_block_ids = scheduler_output.scheduled_new_reqs[0].block_ids[0]
+    invalid_block_ids = {req_block_ids[invalid_block_idx]}
+
+    # store original num_computed_tokens for comparison
+    original_num_computed_tokens = request.num_computed_tokens
+
+    model_runner_output = create_model_runner_output(
+        [request],
+        invalid_block_ids=invalid_block_ids,
+        use_eos=False,  # not finished - should continue running
+    )
+
+    outputs = recompute_scheduler.update_from_output(
+        scheduler_output, model_runner_output
+    )
+
+    # critical assertions for recompute case:
+
+    # 1. request should still be RUNNING (not finished, not aborted)
+    assert request.status == RequestStatus.RUNNING, (
+        f"Request should remain RUNNING for recompute, got {request.status}"
+    )
+
+    # 2. num_computed_tokens should be truncated to first invalid block
+    expected_truncated_tokens = invalid_block_idx * recompute_scheduler.block_size
+    assert request.num_computed_tokens == expected_truncated_tokens, (
+        f"num_computed_tokens should be truncated to {expected_truncated_tokens}, "
+        f"got {request.num_computed_tokens}"
+    )
+    assert request.num_computed_tokens < original_num_computed_tokens, (
+        "num_computed_tokens should be reduced after invalid block detection"
+    )
+
+    # 3. no output should be generated (request is still running)
+    # the request should be skipped in the output loop
+    assert len(outputs) == 0 or request.request_id not in [
+        out.request_id for outs in outputs.values() for out in outs.outputs
+    ], "No output should be generated for recompute requests"
+
+    # 4. request should still be in running queue
+    assert request in recompute_scheduler.running, (
+        "Request should remain in running queue for recomputation"
+    )
+
+    # 5. request should still be in scheduler.requests (not deleted)
+    assert request.request_id in recompute_scheduler.requests, (
+        "Request should not be deleted from scheduler.requests"
+    )
+
+    # 6. blocks should NOT be freed - verify blocks are still allocated
+    try:
+        allocated_blocks = recompute_scheduler.kv_cache_manager.get_block_ids(
+            request.request_id
+        )
+        assert allocated_blocks is not None
+        assert len(allocated_blocks[0]) > 0, (
+            "Blocks should still be allocated for recomputation"
+        )
+    except KeyError:
+        pytest.fail(
+            "Blocks were freed incorrectly! Running requests need their blocks "
+            "to recompute invalid portions."
+        )
+
+    # 7. verify request can be rescheduled in next step
+    scheduler_output_2 = recompute_scheduler.schedule()
+
+    # request should appear in the new schedule to recompute invalid blocks
+    scheduled_req_ids = [
+        req.request_id for req in scheduler_output_2.scheduled_new_reqs
+    ]
+    if scheduler_output_2.num_scheduled_tokens:
+        scheduled_req_ids.extend(scheduler_output_2.num_scheduled_tokens.keys())
+
+    assert (
+        request.request_id in scheduled_req_ids or len(recompute_scheduler.running) > 0
+    ), "Request should be reschedulable for recomputation"
+
+
+def test_sync_fail_invalid_blocks_evicted(fail_scheduler: Scheduler):
+    """
+    Test sync fail case - invalid blocks must be evicted from cache.
+
+    When a request fails with policy='fail' and has invalid blocks from sync loading:
+    1. Request should be finished with FINISHED_ERROR
+    2. Invalid blocks should be evicted from the KV cache
+    3. Valid blocks (if shared) should remain in cache
+    4. Future requests should not reuse the invalid blocks
+
+    This test verifies that invalid blocks are properly evicted to prevent
+    cache corruption and reuse of invalid data.
+    """
+    num_prompt_blocks = 100
+    num_external_computed_blocks = 99
+    invalid_block_idx = 50
+
+    num_prompt_tokens = num_prompt_blocks * fail_scheduler.block_size
+    num_external_computed_tokens = (
+        num_external_computed_blocks * fail_scheduler.block_size
+    )
+
+    request = create_request(num_tokens=num_prompt_tokens)
+    fail_scheduler.add_request(request=request)
+
+    req_num_new_matched_tokens = {
+        request.request_id: num_external_computed_tokens,
+    }
+
+    # mock connector indicating sync load
+    fail_scheduler.connector = Mock()
+    fail_scheduler.connector.get_num_new_matched_tokens.side_effect = (
+        _make_get_num_new_matched_tokens(req_num_new_matched_tokens, False)
+    )
+    fail_scheduler.connector.request_finished.return_value = (False, None)
+    fail_scheduler.connector.take_events.return_value = ()
+
+    scheduler_output = fail_scheduler.schedule()
+
+    # request should be running with sync KV load
+    assert len(fail_scheduler.running) == 1
+    assert request.status == RequestStatus.RUNNING
+
+    # get allocated block IDs
+    req_block_ids = scheduler_output.scheduled_new_reqs[0].block_ids[0]
+    invalid_block_id = req_block_ids[invalid_block_idx]
+    invalid_block_ids = {invalid_block_id}
+
+    # verify the block is in the block pool before we report it as invalid
+    block = fail_scheduler.kv_cache_manager.block_pool.blocks[invalid_block_id]
+    assert block is not None
+
+    # report invalid blocks - request should fail
+    model_runner_output = create_model_runner_output(
+        [request],
+        invalid_block_ids=invalid_block_ids,
+        use_eos=True,
+    )
+
+    outputs = fail_scheduler.update_from_output(scheduler_output, model_runner_output)
+
+    # verify request is finished with error
+    assert request.status == RequestStatus.FINISHED_ERROR
+    assert request.get_finished_reason() == FinishReason.ERROR
+
+    # verify output is generated
+    assert len(outputs) == 1
+    engine_outputs = next(iter(outputs.values()))
+    assert len(engine_outputs.outputs) == 1
+    output = engine_outputs.outputs[0]
+    assert output.request_id == request.request_id
+    assert output.finish_reason == FinishReason.ERROR
+
+    # verify the request was removed from scheduler
+    assert request.request_id not in fail_scheduler.requests
+    assert len(fail_scheduler.running) == 0
+
+    # critical: verify invalid block was actually freed from cache
+    # this is the key assertion - the invalid block should no longer be
+    # tracked by the KV cache manager for this request
+    # if it's still there, a future request could reuse the invalid data
+    try:
+        block_ids = fail_scheduler.kv_cache_manager.get_block_ids(request.request_id)
+        # if we get here, check if blocks were actually freed
+        if block_ids is not None and len(block_ids[0]) > 0:
+            pytest.fail(
+                f"Invalid blocks still tracked for finished request! "
+                f"Request {request.request_id} should have been freed but "
+                f"still has {len(block_ids[0])} blocks allocated."
+            )
+        # blocks list exists but is empty - this is fine, they were freed
+    except KeyError:
+        # expected - request completely removed from tracking
+        pass
+
+    # critical: verify invalid block was evicted from prefix cache
+    # the block should no longer have a hash (hash is reset on eviction)
+    assert block.block_hash is None, (
+        f"Invalid block {invalid_block_id} should have been evicted from cache "
+        f"(hash should be None), but hash is still {block.block_hash}"
+    )
+
+
+def test_async_recompute_blocks_not_cached_when_invalid(
+    recompute_scheduler: Scheduler,
+):
+    """
+    Test async recompute case - invalid blocks not cached after transfer.
+
+    When async KV loading has invalid blocks and retry_policy is 'recompute':
+    1. Blocks are allocated but not cached yet
+    2. When async transfer completes, only valid blocks should be cached
+    3. Invalid blocks should never enter the prefix cache
+
+    This test verifies correctness, the failed_recving_kv_req_ids protection
+    ensures only valid blocks are cached when the transfer completes, and we
+    only evict blocks from cache that are already hashed in the block table.
+    """
+    from unittest.mock import patch
+
+    num_prompt_blocks = 100
+    num_external_computed_blocks = 99
+    invalid_block_idx = 50
+
+    num_prompt_tokens = num_prompt_blocks * recompute_scheduler.block_size
+    num_external_computed_tokens = (
+        num_external_computed_blocks * recompute_scheduler.block_size
+    )
+
+    request = create_request(num_tokens=num_prompt_tokens)
+    recompute_scheduler.add_request(request=request)
+
+    req_num_new_matched_tokens = {
+        request.request_id: num_external_computed_tokens,
+    }
+
+    # mock connector indicating async load
+    recompute_scheduler.connector = Mock()
+    recompute_scheduler.connector.get_num_new_matched_tokens.side_effect = (
+        _make_get_num_new_matched_tokens(req_num_new_matched_tokens, True)
+    )
+    recompute_scheduler.connector.request_finished.return_value = (False, None)
+    recompute_scheduler.connector.take_events.return_value = ()
+
+    scheduler_output = recompute_scheduler.schedule()
+
+    # request should be waiting for remote KVs
+    assert len(recompute_scheduler.waiting) == 1
+    assert request.status == RequestStatus.WAITING_FOR_REMOTE_KVS
+    assert request.num_computed_tokens == 0
+
+    # get the allocated block IDs
+    (req_block_ids,) = recompute_scheduler.kv_cache_manager.get_block_ids(
+        request.request_id
+    )
+    invalid_block_id = req_block_ids[invalid_block_idx]
+    invalid_block_ids = {invalid_block_id}
+
+    # get the block object to verify it's not cached yet and stays uncached
+    block = recompute_scheduler.kv_cache_manager.block_pool.blocks[invalid_block_id]
+
+    # verify block has no hash before invalid blocks are reported
+    assert block.block_hash is None, (
+        "Async loading blocks should not be cached yet (no hash)"
+    )
+
+    # report invalid blocks (transfer not finished yet)
+    model_runner_output = create_model_runner_output(
+        reqs=[],
+        finished_recving=None,  # transfer NOT finished
+        invalid_block_ids=invalid_block_ids,
+        use_eos=False,
+    )
+
+    # critical: spy on evict_blocks to verify it's NOT called for async blocks
+    original_evict_blocks = recompute_scheduler.kv_cache_manager.evict_blocks
+    evict_blocks_calls = []
+
+    def evict_blocks_spy(block_ids):
+        evict_blocks_calls.append(set(block_ids))
+        return original_evict_blocks(block_ids)
+
+    with patch.object(
+        recompute_scheduler.kv_cache_manager, "evict_blocks", evict_blocks_spy
+    ):
+        recompute_scheduler.update_from_output(scheduler_output, model_runner_output)
+
+    # verify evict_blocks was NOT called (async blocks excluded from eviction)
+    assert len(evict_blocks_calls) == 0, (
+        f"evict_blocks should not be called for async-only invalid blocks, "
+        f"but was called {len(evict_blocks_calls)} time(s) with {evict_blocks_calls}"
+    )
+
+    # request should still be waiting (not finished with error due to recompute policy)
+    assert request.status == RequestStatus.WAITING_FOR_REMOTE_KVS
+    assert request.request_id in recompute_scheduler.failed_recving_kv_req_ids
+
+    # verify num_computed_tokens was truncated to before invalid block
+    expected_valid_tokens = invalid_block_idx * recompute_scheduler.block_size
+    assert request.num_computed_tokens == expected_valid_tokens
+
+    # verify invalid block still has no hash (was not evicted)
+    assert block.block_hash is None, (
+        f"Async loading blocks shouldn't be cached or evicted. "
+        f"Block {invalid_block_id} hash should be None but is {block.block_hash}"
+    )
+
+    # now simulate async transfer completing
+    model_runner_output_2 = create_model_runner_output(
+        reqs=[],
+        finished_recving={request.request_id},
+        invalid_block_ids=None,
+        use_eos=False,
+    )
+
+    recompute_scheduler.update_from_output(scheduler_output, model_runner_output_2)
+
+    # verify request is now marked as finished receiving and ready to be processed
+    assert request.request_id in recompute_scheduler.finished_recving_kv_req_ids
+    assert request.request_id in recompute_scheduler.failed_recving_kv_req_ids
+
+    # critical: verify invalid block still has no hash before recompute
+    # the async transfer invalid data was never cached
+    assert block.block_hash is None, (
+        f"Invalid block {invalid_block_id} should not be cached before recompute "
+        f"(hash should be None), but hash is {block.block_hash}"
+    )
+
+    # critical end-to-end test: spy on cache_blocks to verify it's called with
+    # the truncated num_computed_tokens value
+    original_cache_blocks = recompute_scheduler.kv_cache_manager.cache_blocks
+    cache_blocks_calls = []
+
+    def cache_blocks_spy(req, num_tokens):
+        cache_blocks_calls.append((req.request_id, num_tokens))
+        return original_cache_blocks(req, num_tokens)
+
+    with patch.object(
+        recompute_scheduler.kv_cache_manager, "cache_blocks", cache_blocks_spy
+    ):
+        # call schedule() again - this triggers _update_waiting_for_remote_kv()
+        # which should call cache_blocks with the truncated value
+        recompute_scheduler.schedule()
+
+    # verify cache_blocks was called with the truncated value
+    assert len(cache_blocks_calls) == 1, (
+        f"cache_blocks should be called exactly once, "
+        f"got {len(cache_blocks_calls)} calls"
+    )
+    cached_req_id, cached_num_tokens = cache_blocks_calls[0]
+    assert cached_req_id == request.request_id
+    assert cached_num_tokens == expected_valid_tokens, (
+        f"cache_blocks should be called with truncated value {expected_valid_tokens}, "
+        f"but was called with {cached_num_tokens}"
+    )
+
+    # request should now be RUNNING (scheduled immediately after transfer completes)
+    # the flow is: WAITING_FOR_REMOTE_KVS -> WAITING -> RUNNING in same schedule() call
+    assert request.status == RequestStatus.RUNNING
+
+    # num_computed_tokens should be >= expected_valid_tokens because the scheduler
+    # will schedule additional new tokens (up to max_num_batched_tokens) for the request
+    assert request.num_computed_tokens >= expected_valid_tokens, (
+        f"num_computed_tokens should be at least {expected_valid_tokens}, "
+        f"got {request.num_computed_tokens}"
+    )
+
+    # request should no longer be in the failed/finished receiving sets
+    assert request.request_id not in recompute_scheduler.failed_recving_kv_req_ids
+    assert request.request_id not in recompute_scheduler.finished_recving_kv_req_ids
+
+    # request should be in the running queue
+    assert request in recompute_scheduler.running

tools/ep_kernels/README.md

@@ -7,7 +7,7 @@ Here we break down the requirements in 2 steps:
 1. Build and install the Python libraries (both [pplx-kernels](https://github.com/ppl-ai/pplx-kernels) and [DeepEP](https://github.com/deepseek-ai/DeepEP)), including necessary dependencies like NVSHMEM. This step does not require any privileged access. Any user can do this.
 2. Configure NVIDIA driver to enable IBGDA. This step requires root access, and must be done on the host machine.
 
-2 is necessary for multi-node deployment.
+Step 2 is necessary for multi-node deployment.
 
 All scripts accept a positional argument as workspace path for staging the build, defaulting to `$(pwd)/ep_kernels_workspace`.
 
@@ -23,6 +23,6 @@ TORCH_CUDA_ARCH_LIST="10.0" bash install_python_libraries.sh
 Additional step for multi-node deployment:
 
 ```bash
-sudo bash configure_system_drivers.sh
+sudo bash configure_system_drivers.sh # update-initramfs can take several minutes
 sudo reboot # Reboot is required to load the new driver
 ```

vllm/attention/backends/registry.py

@@ -252,35 +252,3 @@ def register_backend(
         return lambda x: x
 
     return decorator
-
-
-# Backwards compatibility alias for plugins
-class _BackendMeta(type):
-    """Metaclass to provide deprecation warnings when accessing _Backend."""
-
-    def __getattribute__(cls, name: str):
-        if name not in ("__class__", "__mro__", "__name__"):
-            logger.warning(
-                "_Backend has been renamed to AttentionBackendEnum. "
-                "Please update your code to use AttentionBackendEnum instead. "
-                "_Backend will be removed in a future release."
-            )
-        return getattr(AttentionBackendEnum, name)
-
-    def __getitem__(cls, name: str):
-        logger.warning(
-            "_Backend has been renamed to AttentionBackendEnum. "
-            "Please update your code to use AttentionBackendEnum instead. "
-            "_Backend will be removed in a future release."
-        )
-        return AttentionBackendEnum[name]
-
-
-class _Backend(metaclass=_BackendMeta):
-    """Deprecated: Use AttentionBackendEnum instead.
-
-    This class is provided for backwards compatibility with plugins
-    and will be removed in a future release.
-    """
-
-    pass

vllm/attention/selector.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import inspect
 from functools import cache
 from typing import cast, get_args
 
@@ -73,39 +72,18 @@ def _cached_get_attn_backend(
 ) -> type[AttentionBackend]:
     from vllm.platforms import current_platform
 
-    sig = inspect.signature(current_platform.get_attn_backend_cls)
+    attention_cls = current_platform.get_attn_backend_cls(
-    if "use_v1" in sig.parameters:
+        backend,
-        logger.warning_once(
+        head_size,
-            "use_v1 parameter for get_attn_backend_cls is deprecated and will "
+        dtype,
-            "be removed in v0.13.0 or v1.0.0, whichever is soonest. Please "
+        kv_cache_dtype,
-            "remove it from your plugin code."
+        block_size,
-        )
+        use_mla,
-        attention_cls = current_platform.get_attn_backend_cls(
+        has_sink,
-            backend,
+        use_sparse,
-            head_size,
+        use_mm_prefix,
-            dtype,
+        attn_type,
-            kv_cache_dtype,
+    )
-            block_size,
-            True,  # use_v1
-            use_mla,
-            has_sink,
-            use_sparse,
-            use_mm_prefix,
-            attn_type,
-        )
-    else:
-        attention_cls = current_platform.get_attn_backend_cls(
-            backend,
-            head_size,
-            dtype,
-            kv_cache_dtype,
-            block_size,
-            use_mla,
-            has_sink,
-            use_sparse,
-            use_mm_prefix,
-            attn_type,
-        )
     if not attention_cls:
         raise ValueError(
             f"Invalid attention backend for {current_platform.device_name}"

vllm/config/compilation.py

@@ -17,7 +17,6 @@ from vllm.config.utils import (
     Range,
     config,
     get_hash_factors,
-    handle_deprecated,
     hash_factors,
 )
 from vllm.logger import init_logger
@@ -127,27 +126,6 @@ class PassConfig:
     fuse_allreduce_rms: bool = Field(default=None)
     """Enable flashinfer allreduce fusion."""
 
-    # Deprecated flags
-    enable_fusion: bool = Field(default=None)
-    """Deprecated in: v0.12.0. Use fuse_norm_quant and fuse_act_quant 
-    instead. Will be removed in v0.13.0 or v1.0.0, whichever is sooner.
-    """
-    enable_attn_fusion: bool = Field(default=None)
-    """Deprecated in: v0.12.0. Use fuse_attn_quant instead. 
-    Will be removed in v0.13.0 or v1.0.0, whichever is sooner."""
-    enable_noop: bool = Field(default=None)
-    """Deprecated in: v0.12.0. Use eliminate_noops instead. 
-    Will be removed in v0.13.0 or v1.0.0, whichever is sooner."""
-    enable_sequence_parallelism: bool = Field(default=None)
-    """Deprecated in: v0.12.0. Use enable_sp instead. 
-    Will be removed in v0.13.0 or v1.0.0, whichever is sooner."""
-    enable_async_tp: bool = Field(default=None)
-    """Deprecated in: v0.12.0. Use fuse_gemm_comms instead. 
-    Will be removed in v0.13.0 or v1.0.0, whichever is sooner."""
-    enable_fi_allreduce_fusion: bool = Field(default=None)
-    """Deprecated in: v0.12.0. Use fuse_allreduce_rms instead. 
-    Will be removed in v0.13.0 or v1.0.0, whichever is sooner."""
-
     fi_allreduce_fusion_max_size_mb: float | None = None
     """The threshold of the communicated tensor sizes under which
     vllm should use flashinfer fused allreduce. Specified as a
@@ -206,15 +184,7 @@ class PassConfig:
         Any future fields that don't affect compilation should be excluded.
         """
 
-        ignored_fields = [
+        return hash_factors(get_hash_factors(self, set()))
-            "enable_fusion",
-            "enable_attn_fusion",
-            "enable_noop",
-            "enable_sequence_parallelism",
-            "enable_async_tp",
-            "enable_fi_allreduce_fusion",
-        ]
-        return hash_factors(get_hash_factors(self, ignored_factors=ignored_fields))
 
     @field_validator(
         "fuse_norm_quant",
@@ -224,12 +194,6 @@ class PassConfig:
         "enable_sp",
         "fuse_gemm_comms",
         "fuse_allreduce_rms",
-        "enable_fusion",
-        "enable_attn_fusion",
-        "enable_noop",
-        "enable_sequence_parallelism",
-        "enable_async_tp",
-        "enable_fi_allreduce_fusion",
         mode="wrap",
     )
     @classmethod
@@ -242,49 +206,6 @@ class PassConfig:
     def __post_init__(self) -> None:
         # Handle deprecation and defaults
 
-        # Map old flags to new flags and issue warnings
-        handle_deprecated(
-            self,
-            "enable_fusion",
-            ["fuse_norm_quant", "fuse_act_quant"],
-            "v0.13.0 or v1.0.0, whichever is sooner",
-        )
-
-        handle_deprecated(
-            self,
-            "enable_attn_fusion",
-            "fuse_attn_quant",
-            "v0.13.0 or v1.0.0, whichever is sooner",
-        )
-
-        handle_deprecated(
-            self,
-            "enable_sequence_parallelism",
-            "enable_sp",
-            "v0.13.0 or v1.0.0, whichever is sooner",
-        )
-
-        handle_deprecated(
-            self,
-            "enable_async_tp",
-            "fuse_gemm_comms",
-            "v0.13.0 or v1.0.0, whichever is sooner",
-        )
-
-        handle_deprecated(
-            self,
-            "enable_fi_allreduce_fusion",
-            "fuse_allreduce_rms",
-            "v0.13.0 or v1.0.0, whichever is sooner",
-        )
-
-        handle_deprecated(
-            self,
-            "enable_noop",
-            "eliminate_noops",
-            "v0.13.0 or v1.0.0, whichever is sooner",
-        )
-
         if not self.eliminate_noops:
             if self.fuse_norm_quant or self.fuse_act_quant:
                 logger.warning_once(

vllm/config/kv_transfer.py

@@ -64,6 +64,11 @@ class KVTransferConfig:
     enable_permute_local_kv: bool = False
     """Experiment feature flag to enable HND to NHD KV Transfer"""
 
+    kv_load_failure_policy: Literal["recompute", "fail"] = "recompute"
+    """Policy for handling KV cache load failures.
+    'recompute': reschedule the request to recompute failed blocks (default)
+    'fail': immediately fail the request with an error finish reason"""
+
     def compute_hash(self) -> str:
         """
         WARNING: Whenever a new field is added to this config,

vllm/config/model.py

@@ -77,17 +77,6 @@ logger = init_logger(__name__)
 RunnerOption = Literal["auto", RunnerType]
 ConvertType = Literal["none", "embed", "classify", "reward"]
 ConvertOption = Literal["auto", ConvertType]
-TaskOption = Literal[
-    "auto",
-    "generate",
-    "embedding",
-    "embed",
-    "classify",
-    "score",
-    "reward",
-    "transcription",
-    "draft",
-]
 TokenizerMode = Literal["auto", "hf", "slow", "mistral", "deepseek_v32"]
 ModelDType = Literal["auto", "half", "float16", "bfloat16", "float", "float32"]
 LogprobsMode = Literal[
@@ -97,12 +86,6 @@ HfOverrides = dict[str, Any] | Callable[[PretrainedConfig], PretrainedConfig]
 ModelImpl = Literal["auto", "vllm", "transformers", "terratorch"]
 LayerBlockType = Literal["attention", "linear_attention", "mamba"]
 
-_RUNNER_TASKS: dict[RunnerType, list[TaskOption]] = {
-    "generate": ["generate", "transcription"],
-    "pooling": ["embedding", "embed", "classify", "score", "reward"],
-    "draft": ["draft"],
-}
-
 _RUNNER_CONVERTS: dict[RunnerType, list[ConvertType]] = {
     "generate": [],
     "pooling": ["embed", "classify", "reward"],
@@ -130,12 +113,6 @@ class ModelConfig:
     """Convert the model using adapters defined in
     [vllm.model_executor.models.adapters][]. The most common use case is to
     adapt a text generation model to be used for pooling tasks."""
-    task: TaskOption | None = None
-    """[DEPRECATED] The task to use the model for. If the model supports more
-    than one model runner, this is used to select which model runner to run.
-
-    Note that the model may support other tasks using the same model runner.
-    """
     tokenizer: SkipValidation[str] = None  # type: ignore
     """Name or path of the Hugging Face tokenizer to use. If unspecified, model
     name or path will be used."""
@@ -339,7 +316,6 @@ class ModelConfig:
         ignored_factors = {
             "runner",
             "convert",
-            "task",
             "tokenizer",
             "tokenizer_mode",
             "seed",
@@ -515,97 +491,6 @@ class ModelConfig:
         is_generative_model = registry.is_text_generation_model(architectures, self)
         is_pooling_model = registry.is_pooling_model(architectures, self)
 
-        def _task_to_convert(task: TaskOption) -> ConvertType:
-            if task == "embedding" or task == "embed":
-                return "embed"
-            if task == "classify":
-                return "classify"
-            if task == "reward":
-                logger.warning(
-                    "Pooling models now default support all pooling; "
-                    "you can use it without any settings."
-                )
-                return "embed"
-            if task == "score":
-                new_task = self._get_default_pooling_task(architectures)
-                return "classify" if new_task == "classify" else "embed"
-
-            return "none"
-
-        if self.task is not None:
-            runner: RunnerOption = "auto"
-            convert: ConvertOption = "auto"
-            msg_prefix = (
-                "The 'task' option has been deprecated and will be "
-                "removed in v0.13.0 or v1.0, whichever comes first."
-            )
-            msg_hint = "Please remove this option."
-
-            is_generative_task = self.task in _RUNNER_TASKS["generate"]
-            is_pooling_task = self.task in _RUNNER_TASKS["pooling"]
-
-            if is_generative_model and is_pooling_model:
-                if is_generative_task:
-                    runner = "generate"
-                    convert = "auto"
-                    msg_hint = (
-                        "Please replace this option with `--runner "
-                        "generate` to continue using this model "
-                        "as a generative model."
-                    )
-                elif is_pooling_task:
-                    runner = "pooling"
-                    convert = "auto"
-                    msg_hint = (
-                        "Please replace this option with `--runner "
-                        "pooling` to continue using this model "
-                        "as a pooling model."
-                    )
-                else:  # task == "auto"
-                    pass
-            elif is_generative_model or is_pooling_model:
-                if is_generative_task:
-                    runner = "generate"
-                    convert = "auto"
-                    msg_hint = "Please remove this option"
-                elif is_pooling_task:
-                    runner = "pooling"
-                    convert = _task_to_convert(self.task)
-                    msg_hint = (
-                        "Please replace this option with `--convert "
-                        f"{convert}` to continue using this model "
-                        "as a pooling model."
-                    )
-                else:  # task == "auto"
-                    pass
-            else:
-                # Neither generative nor pooling model - try to convert if possible
-                if is_pooling_task:
-                    runner = "pooling"
-                    convert = _task_to_convert(self.task)
-                    msg_hint = (
-                        "Please replace this option with `--runner pooling "
-                        f"--convert {convert}` to continue using this model "
-                        "as a pooling model."
-                    )
-                else:
-                    debug_info = {
-                        "architectures": architectures,
-                        "is_generative_model": is_generative_model,
-                        "is_pooling_model": is_pooling_model,
-                    }
-                    raise AssertionError(
-                        "The model should be a generative or "
-                        "pooling model when task is set to "
-                        f"{self.task!r}. Found: {debug_info}"
-                    )
-
-            self.runner = runner
-            self.convert = convert
-
-            msg = f"{msg_prefix} {msg_hint}"
-            warnings.warn(msg, DeprecationWarning, stacklevel=2)
-
         self.runner_type = self._get_runner_type(architectures, self.runner)
         self.convert_type = self._get_convert_type(
             architectures, self.runner_type, self.convert
@@ -930,22 +815,6 @@ class ModelConfig:
 
         return convert_type
 
-    def _get_default_pooling_task(
-        self,
-        architectures: list[str],
-    ) -> Literal["embed", "classify", "reward"]:
-        if self.registry.is_cross_encoder_model(architectures, self):
-            return "classify"
-
-        for arch in architectures:
-            match = try_match_architecture_defaults(arch, runner_type="pooling")
-            if match:
-                _, (_, convert_type) = match
-                assert convert_type != "none"
-                return convert_type
-
-        return "embed"
-
     def _verify_quantization(self) -> None:
         supported_quantization = me_quant.QUANTIZATION_METHODS
         if self.quantization is not None:

vllm/config/vllm.py

@@ -1015,7 +1015,7 @@ class VllmConfig:
         max_graph_size = min(max_num_seqs * 2, 512)
         # 1, 2, 4, then multiples of 8 up to 256 and then multiples of 16
         # up to max_graph_size
-        cuda_graph_sizes = [1, 2, 4] + list(range(8, 256, 8)) + list(
+        cudagraph_capture_sizes = [1, 2, 4] + list(range(8, 256, 8)) + list(
             range(256, max_graph_size + 1, 16))
 
         In the end, `vllm_config.compilation_config.cudagraph_capture_sizes`

vllm/distributed/device_communicators/shm_broadcast.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import functools
 import pickle
+import threading
 import time
 from contextlib import contextmanager
 from dataclasses import dataclass, field
@@ -43,6 +44,33 @@ VLLM_RINGBUFFER_WARNING_INTERVAL = envs.VLLM_RINGBUFFER_WARNING_INTERVAL
 from_bytes_big = functools.partial(int.from_bytes, byteorder="big")
 
 
+# Memory fence for cross-process shared memory visibility.
+# Required for correct producer-consumer synchronization when using
+# shared memory without locks.
+_memory_fence_lock = threading.Lock()
+
+
+def memory_fence():
+    """
+    Full memory barrier for shared memory synchronization.
+
+    Ensures all prior memory writes are visible to other processes before
+    any subsequent reads. This is critical for lock-free producer-consumer
+    patterns using shared memory.
+
+    Implementation acquires and immediately releases a lock. Python's
+    threading.Lock provides sequentially consistent memory barrier semantics
+    across all major platforms (POSIX, Windows). This is a lightweight
+    operation (~20ns) that guarantees:
+    - All stores before the barrier are visible to other threads/processes
+    - All loads after the barrier see the latest values
+    """
+    # Lock acquire/release provides full memory barrier semantics.
+    # Using context manager ensures lock release even on exceptions.
+    with _memory_fence_lock:
+        pass
+
+
 def to_bytes_big(value: int, size: int) -> bytes:
     return value.to_bytes(size, byteorder="big")
 
@@ -414,6 +442,10 @@ class MessageQueue:
         n_warning = 1
         while True:
             with self.buffer.get_metadata(self.current_idx) as metadata_buffer:
+                # Memory fence ensures we see the latest read flags from readers.
+                # Without this, we may read stale flags from our CPU cache and
+                # spin indefinitely even though readers have completed.
+                memory_fence()
                 read_count = sum(metadata_buffer[1:])
                 written_flag = metadata_buffer[0]
                 if written_flag and read_count != self.buffer.n_reader:
@@ -458,6 +490,10 @@ class MessageQueue:
                     metadata_buffer[i] = 0
                 # mark the block as written
                 metadata_buffer[0] = 1
+                # Memory fence ensures the write is visible to readers on other cores
+                # before we proceed. Without this, readers may spin indefinitely
+                # waiting for a write that's stuck in our CPU's store buffer.
+                memory_fence()
                 self.current_idx = (self.current_idx + 1) % self.buffer.max_chunks
                 break
 
@@ -473,6 +509,10 @@ class MessageQueue:
         n_warning = 1
         while True:
             with self.buffer.get_metadata(self.current_idx) as metadata_buffer:
+                # Memory fence ensures we see the latest writes from the writer.
+                # Without this, we may read stale flags from our CPU cache
+                # and spin indefinitely even though writer has updated them.
+                memory_fence()
                 read_flag = metadata_buffer[self.local_reader_rank + 1]
                 written_flag = metadata_buffer[0]
                 if not written_flag or read_flag:
@@ -513,6 +553,10 @@ class MessageQueue:
                 # caller has read from the buffer
                 # set the read flag
                 metadata_buffer[self.local_reader_rank + 1] = 1
+                # Memory fence ensures the read flag is visible to the writer.
+                # Without this, writer may not see our read completion and
+                # could wait indefinitely for all readers to finish.
+                memory_fence()
                 self.current_idx = (self.current_idx + 1) % self.buffer.max_chunks
 
                 self._read_spin_timer.record_activity()

vllm/distributed/eplb/rebalance_execute.py

@@ -322,6 +322,9 @@ async def transfer_layer(
     num_local_physical_experts = next(iter(expert_weights[0])).shape[0]
     assert new_global_expert_indices.shape == (num_moe_layers, num_physical_experts)
     assert num_physical_experts == ep_size * num_local_physical_experts
+    # A buffer to hold the expert weights in one layer during the exchange.
+    # NOTE: Currently we assume the same weights across different layers
+    # have the same shape.
 
     is_unchanged, is_received_locally, experts_recv_loc = move_to_buffer(
         num_local_experts=num_local_physical_experts,

vllm/distributed/kv_transfer/kv_connector/v1/lmcache_integration/vllm_v1_adapter.py

@@ -27,7 +27,7 @@ from lmcache.v1.lookup_client.lmcache_async_lookup_client import (
     LMCacheAsyncLookupServer,
 )
 from lmcache.v1.offload_server.zmq_server import ZMQOffloadServer
-from lmcache.v1.plugin.plugin_launcher import PluginLauncher
+from lmcache.v1.plugin.runtime_plugin_launcher import RuntimePluginLauncher
 
 from vllm.attention.backends.abstract import AttentionMetadata
 from vllm.config import VllmConfig
@@ -683,7 +683,7 @@ class LMCacheConnectorV1Impl:
             self.api_server = InternalAPIServer(self)
             self.api_server.start()
             # Launch plugins
-            self.plugin_launcher = PluginLauncher(
+            self.plugin_launcher = RuntimePluginLauncher(
                 self.config,
                 role,
                 self.worker_count,

vllm/distributed/parallel_state.py

@@ -1586,6 +1586,8 @@ def destroy_distributed_environment():
 
 
 def cleanup_dist_env_and_memory(shutdown_ray: bool = False):
+    # Reset environment variable cache
+    envs.disable_envs_cache()
     # Ensure all objects are not frozen before cleanup
     gc.unfreeze()
 

vllm/engine/arg_utils.py

@@ -71,7 +71,6 @@ from vllm.config.model import (
     LogprobsMode,
     ModelDType,
     RunnerOption,
-    TaskOption,
     TokenizerMode,
 )
 from vllm.config.multimodal import MMCacheType, MMEncoderTPMode
@@ -360,7 +359,6 @@ class EngineArgs:
     hf_config_path: str | None = ModelConfig.hf_config_path
     runner: RunnerOption = ModelConfig.runner
     convert: ConvertOption = ModelConfig.convert
-    task: TaskOption | None = ModelConfig.task
     skip_tokenizer_init: bool = ModelConfig.skip_tokenizer_init
     enable_prompt_embeds: bool = ModelConfig.enable_prompt_embeds
     tokenizer_mode: TokenizerMode | str = ModelConfig.tokenizer_mode
@@ -373,9 +371,8 @@ class EngineArgs:
     config_format: str = ModelConfig.config_format
     dtype: ModelDType = ModelConfig.dtype
     kv_cache_dtype: CacheDType = CacheConfig.cache_dtype
-    seed: int | None = 0
+    seed: int = ModelConfig.seed
     max_model_len: int | None = ModelConfig.max_model_len
-    cuda_graph_sizes: list[int] | None = CompilationConfig.cudagraph_capture_sizes
     cudagraph_capture_sizes: list[int] | None = (
         CompilationConfig.cudagraph_capture_sizes
     )
@@ -463,7 +460,6 @@ class EngineArgs:
         MultiModalConfig, "media_io_kwargs"
     )
     mm_processor_kwargs: dict[str, Any] | None = MultiModalConfig.mm_processor_kwargs
-    disable_mm_preprocessor_cache: bool = False  # DEPRECATED
     mm_processor_cache_gb: float = MultiModalConfig.mm_processor_cache_gb
     mm_processor_cache_type: MMCacheType | None = (
         MultiModalConfig.mm_processor_cache_type
@@ -559,9 +555,6 @@ class EngineArgs:
     use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
     pt_load_map_location: str = LoadConfig.pt_load_map_location
 
-    # DEPRECATED
-    enable_multimodal_encoder_data_parallel: bool = False
-
     logits_processors: list[str | type[LogitsProcessor]] | None = (
         ModelConfig.logits_processors
     )
@@ -629,7 +622,6 @@ class EngineArgs:
             model_group.add_argument("--model", **model_kwargs["model"])
         model_group.add_argument("--runner", **model_kwargs["runner"])
         model_group.add_argument("--convert", **model_kwargs["convert"])
-        model_group.add_argument("--task", **model_kwargs["task"], deprecated=True)
         model_group.add_argument("--tokenizer", **model_kwargs["tokenizer"])
         model_group.add_argument("--tokenizer-mode", **model_kwargs["tokenizer_mode"])
         model_group.add_argument(
@@ -883,11 +875,6 @@ class EngineArgs:
         parallel_group.add_argument(
             "--worker-extension-cls", **parallel_kwargs["worker_extension_cls"]
         )
-        parallel_group.add_argument(
-            "--enable-multimodal-encoder-data-parallel",
-            action="store_true",
-            deprecated=True,
-        )
 
         # KV cache arguments
         cache_kwargs = get_kwargs(CacheConfig)
@@ -961,9 +948,6 @@ class EngineArgs:
         multimodal_group.add_argument(
             "--mm-processor-cache-gb", **multimodal_kwargs["mm_processor_cache_gb"]
         )
-        multimodal_group.add_argument(
-            "--disable-mm-preprocessor-cache", action="store_true", deprecated=True
-        )
         multimodal_group.add_argument(
             "--mm-processor-cache-type", **multimodal_kwargs["mm_processor_cache_type"]
         )
@@ -1121,15 +1105,6 @@ class EngineArgs:
         compilation_group.add_argument(
             "--cudagraph-capture-sizes", **compilation_kwargs["cudagraph_capture_sizes"]
         )
-        compilation_kwargs["cudagraph_capture_sizes"]["help"] = (
-            "--cuda-graph-sizes is deprecated and will be removed in v0.13.0 or v1.0.0,"
-            " whichever is soonest. Please use --cudagraph-capture-sizes instead."
-        )
-        compilation_group.add_argument(
-            "--cuda-graph-sizes",
-            **compilation_kwargs["cudagraph_capture_sizes"],
-            deprecated=True,
-        )
         compilation_group.add_argument(
             "--max-cudagraph-capture-size",
             **compilation_kwargs["max_cudagraph_capture_size"],
@@ -1202,62 +1177,20 @@ class EngineArgs:
         if is_gguf(self.model):
             self.quantization = self.load_format = "gguf"
 
-        # NOTE(woosuk): In V1, we use separate processes for workers (unless
+        if not envs.VLLM_ENABLE_V1_MULTIPROCESSING:
-        # VLLM_ENABLE_V1_MULTIPROCESSING=0), so setting a seed here
+            logger.warning(
-        # doesn't affect the user process.
+                "The global random seed is set to %d. Since "
-        if self.seed is None:
+                "VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may "
-            logger.warning_once(
+                "affect the random state of the Python process that "
-                "`seed=None` is equivalent to `seed=0` in V1 Engine. "
+                "launched vLLM.",
-                "You will no longer be allowed to pass `None` in v0.13.",
+                self.seed,
-                scope="local",
             )
 
-            self.seed = 0
-            if not envs.VLLM_ENABLE_V1_MULTIPROCESSING:
-                logger.warning(
-                    "The global random seed is set to %d. Since "
-                    "VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may "
-                    "affect the random state of the Python process that "
-                    "launched vLLM.",
-                    self.seed,
-                )
-
-        if self.disable_mm_preprocessor_cache:
-            logger.warning_once(
-                "`--disable-mm-preprocessor-cache` is deprecated "
-                "and will be removed in v0.13. "
-                "Please use `--mm-processor-cache-gb 0` instead.",
-                scope="local",
-            )
-
-            self.mm_processor_cache_gb = 0
-        elif envs.VLLM_MM_INPUT_CACHE_GIB != 4:
-            logger.warning_once(
-                "VLLM_MM_INPUT_CACHE_GIB` is deprecated "
-                "and will be removed in v0.13. "
-                "Please use `--mm-processor-cache-gb %d` instead.",
-                envs.VLLM_MM_INPUT_CACHE_GIB,
-                scope="local",
-            )
-
-            self.mm_processor_cache_gb = envs.VLLM_MM_INPUT_CACHE_GIB
-
-        if self.enable_multimodal_encoder_data_parallel:
-            logger.warning_once(
-                "--enable-multimodal-encoder-data-parallel` is deprecated "
-                "and will be removed in v0.13. "
-                "Please use `--mm-encoder-tp-mode data` instead.",
-                scope="local",
-            )
-
-            self.mm_encoder_tp_mode = "data"
-
         return ModelConfig(
             model=self.model,
             hf_config_path=self.hf_config_path,
             runner=self.runner,
             convert=self.convert,
-            task=self.task,
             tokenizer=self.tokenizer,
             tokenizer_mode=self.tokenizer_mode,
             trust_remote_code=self.trust_remote_code,
@@ -1741,18 +1674,6 @@ class EngineArgs:
 
         # Compilation config overrides
         compilation_config = copy.deepcopy(self.compilation_config)
-        if self.cuda_graph_sizes is not None:
-            logger.warning(
-                "--cuda-graph-sizes is deprecated and will be removed in v0.13.0 or "
-                "v1.0.0, whichever is soonest. Please use --cudagraph-capture-sizes "
-                "instead."
-            )
-            if compilation_config.cudagraph_capture_sizes is not None:
-                raise ValueError(
-                    "cuda_graph_sizes and compilation_config."
-                    "cudagraph_capture_sizes are mutually exclusive"
-                )
-            compilation_config.cudagraph_capture_sizes = self.cuda_graph_sizes
         if self.cudagraph_capture_sizes is not None:
             if compilation_config.cudagraph_capture_sizes is not None:
                 raise ValueError(

vllm/entrypoints/llm.py

@@ -9,7 +9,7 @@ import cloudpickle
 import torch.nn as nn
 from pydantic import ValidationError
 from tqdm.auto import tqdm
-from typing_extensions import TypeVar, deprecated
+from typing_extensions import TypeVar
 
 from vllm.beam_search import (
     BeamSearchInstance,
@@ -73,7 +73,6 @@ from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import BeamSearchParams, RequestOutputKind, SamplingParams
 from vllm.tasks import PoolingTask
 from vllm.tokenizers import MistralTokenizer, TokenizerLike
-from vllm.tokenizers.hf import get_cached_tokenizer
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils.collection_utils import as_iter, is_list_of
 from vllm.utils.counter import Counter
@@ -199,7 +198,7 @@ class LLM:
         quantization: QuantizationMethods | None = None,
         revision: str | None = None,
         tokenizer_revision: str | None = None,
-        seed: int | None = None,
+        seed: int = 0,
         gpu_memory_utilization: float = 0.9,
         swap_space: float = 4,
         cpu_offload_gb: float = 0,
@@ -367,16 +366,6 @@ class LLM:
     def get_tokenizer(self) -> TokenizerLike:
         return self.llm_engine.get_tokenizer()
 
-    @deprecated("`set_tokenizer` is deprecated and will be removed in v0.13.")
-    def set_tokenizer(self, tokenizer: TokenizerLike) -> None:
-        # While CachedTokenizer is dynamic, have no choice but
-        # compare class name. Misjudgment will arise from
-        # user-defined tokenizer started with 'Cached'
-        if tokenizer.__class__.__name__.startswith("Cached"):
-            self.llm_engine.tokenizer = tokenizer
-        else:
-            self.llm_engine.tokenizer = get_cached_tokenizer(tokenizer)
-
     def reset_mm_cache(self) -> None:
         self.input_processor.clear_mm_cache()
         self.llm_engine.reset_mm_cache()

vllm/entrypoints/openai/serving_chat.py

@@ -51,7 +51,11 @@ from vllm.entrypoints.openai.protocol import (
     ToolCall,
     UsageInfo,
 )
-from vllm.entrypoints.openai.serving_engine import OpenAIServing, clamp_prompt_logprobs
+from vllm.entrypoints.openai.serving_engine import (
+    GenerationError,
+    OpenAIServing,
+    clamp_prompt_logprobs,
+)
 from vllm.entrypoints.openai.serving_models import OpenAIServingModels
 from vllm.entrypoints.openai.tool_parsers import ToolParser
 from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import MistralToolCall
@@ -380,6 +384,8 @@ class OpenAIServingChat(OpenAIServing):
                 tokenizer,
                 request_metadata,
             )
+        except GenerationError as e:
+            return self._convert_generation_error_to_response(e)
         except ValueError as e:
             # TODO: Use a vllm-specific Validation Error
             return self.create_error_response(str(e))
@@ -1120,6 +1126,10 @@ class OpenAIServingChat(OpenAIServing):
 
                     # if the model is finished generating
                     else:
+                        # check for error finish reason and abort streaming
+                        # finish_reason='error' indicates a retryable error
+                        self._raise_if_error(output.finish_reason, request_id)
+
                         # check to make sure we haven't "forgotten" to stream
                         #   any tokens that were generated but previously
                         #   matched by partial json parsing
@@ -1287,6 +1297,8 @@ class OpenAIServingChat(OpenAIServing):
                         delta=False,
                     )
 
+        except GenerationError as e:
+            yield f"data: {self._convert_generation_error_to_streaming_response(e)}\n\n"
         except Exception as e:
             # TODO: Use a vllm-specific Validation Error
             logger.exception("Error in chat completion stream generator.")
@@ -1327,6 +1339,9 @@ class OpenAIServingChat(OpenAIServing):
 
         role = self.get_chat_request_role(request)
         for output in final_res.outputs:
+            # check for error finish reason and raise GenerationError
+            # finish_reason='error' indicates a retryable request-level internal error
+            self._raise_if_error(output.finish_reason, request_id)
             token_ids = output.token_ids
             out_logprobs = output.logprobs
             tool_call_info = None

vllm/entrypoints/openai/serving_completion.py

@@ -24,7 +24,11 @@ from vllm.entrypoints.openai.protocol import (
     RequestResponseMetadata,
     UsageInfo,
 )
-from vllm.entrypoints.openai.serving_engine import OpenAIServing, clamp_prompt_logprobs
+from vllm.entrypoints.openai.serving_engine import (
+    GenerationError,
+    OpenAIServing,
+    clamp_prompt_logprobs,
+)
 from vllm.entrypoints.openai.serving_models import OpenAIServingModels
 from vllm.entrypoints.renderer import RenderConfig
 from vllm.entrypoints.utils import get_max_tokens, should_include_usage
@@ -300,6 +304,8 @@ class OpenAIServingCompletion(OpenAIServing):
             )
         except asyncio.CancelledError:
             return self.create_error_response("Client disconnected")
+        except GenerationError as e:
+            return self._convert_generation_error_to_response(e)
         except ValueError as e:
             # TODO: Use a vllm-specific Validation Error
             return self.create_error_response(str(e))
@@ -437,6 +443,8 @@ class OpenAIServingCompletion(OpenAIServing):
                     finish_reason = output.finish_reason
                     stop_reason = output.stop_reason
 
+                    self._raise_if_error(finish_reason, request_id)
+
                     chunk = CompletionStreamResponse(
                         id=request_id,
                         created=created_time,
@@ -498,8 +506,11 @@ class OpenAIServingCompletion(OpenAIServing):
             # report to FastAPI middleware aggregate usage across all choices
             request_metadata.final_usage_info = final_usage_info
 
+        except GenerationError as e:
+            yield f"data: {self._convert_generation_error_to_streaming_response(e)}\n\n"
         except Exception as e:
             # TODO: Use a vllm-specific Validation Error
+            logger.exception("Error in completion stream generator.")
             data = self.create_streaming_error_response(str(e))
             yield f"data: {data}\n\n"
         yield "data: [DONE]\n\n"
@@ -530,6 +541,8 @@ class OpenAIServingCompletion(OpenAIServing):
             out_logprobs: GenericSequence[dict[int, Logprob] | None] | None
 
             for output in final_res.outputs:
+                self._raise_if_error(output.finish_reason, request_id)
+
                 assert request.max_tokens is not None
                 if request.echo:
                     if request.return_token_ids:

vllm/entrypoints/openai/serving_engine.py

@@ -133,6 +133,15 @@ from vllm.utils.async_utils import (
 from vllm.utils.collection_utils import is_list_of
 from vllm.v1.engine import EngineCoreRequest
 
+
+class GenerationError(Exception):
+    """raised when finish_reason indicates internal server error (500)"""
+
+    def __init__(self, message: str = "Internal server error"):
+        super().__init__(message)
+        self.status_code = HTTPStatus.INTERNAL_SERVER_ERROR
+
+
 logger = init_logger(__name__)
 
 CompletionLikeRequest: TypeAlias = (
@@ -456,6 +465,29 @@ class OpenAIServing:
             # Iterate through all beam inference results
             for i, result in enumerate(output):
                 current_beam = all_beams[i]
+
+                # check for error finish reason and abort beam search
+                if result.outputs[0].finish_reason == "error":
+                    # yield error output and terminate beam search
+                    yield RequestOutput(
+                        request_id=request_id,
+                        prompt=prompt_text,
+                        outputs=[
+                            CompletionOutput(
+                                index=0,
+                                text="",
+                                token_ids=[],
+                                cumulative_logprob=None,
+                                logprobs=None,
+                                finish_reason="error",
+                            )
+                        ],
+                        finished=True,
+                        prompt_token_ids=prompt_token_ids,
+                        prompt_logprobs=None,
+                    )
+                    return
+
                 if result.outputs[0].logprobs is not None:
                     logprobs = result.outputs[0].logprobs[0]
                     all_beams_token_id.extend(list(logprobs.keys()))
@@ -780,6 +812,35 @@ class OpenAIServing:
         )
         return json_str
 
+    def _raise_if_error(self, finish_reason: str | None, request_id: str) -> None:
+        """Raise GenerationError if finish_reason indicates an error."""
+        if finish_reason == "error":
+            logger.error(
+                "Request %s failed with an internal error during generation",
+                request_id,
+            )
+            raise GenerationError("Internal server error")
+
+    def _convert_generation_error_to_response(
+        self, e: GenerationError
+    ) -> ErrorResponse:
+        """Convert GenerationError to ErrorResponse."""
+        return self.create_error_response(
+            str(e),
+            err_type="InternalServerError",
+            status_code=e.status_code,
+        )
+
+    def _convert_generation_error_to_streaming_response(
+        self, e: GenerationError
+    ) -> str:
+        """Convert GenerationError to streaming error response."""
+        return self.create_streaming_error_response(
+            str(e),
+            err_type="InternalServerError",
+            status_code=e.status_code,
+        )
+
     async def _check_model(
         self,
         request: AnyRequest,

vllm/entrypoints/openai/serving_responses.py

@@ -50,6 +50,7 @@ from openai.types.responses.response_reasoning_item import (
 )
 from openai.types.responses.tool import Mcp, Tool
 from openai_harmony import Message as OpenAIHarmonyMessage
+from pydantic import TypeAdapter
 
 from vllm import envs
 from vllm.engine.protocol import EngineClient
@@ -94,7 +95,10 @@ from vllm.entrypoints.openai.protocol import (
     ResponseUsage,
     StreamingResponsesResponse,
 )
-from vllm.entrypoints.openai.serving_engine import OpenAIServing
+from vllm.entrypoints.openai.serving_engine import (
+    GenerationError,
+    OpenAIServing,
+)
 from vllm.entrypoints.openai.serving_models import OpenAIServingModels
 from vllm.entrypoints.responses_utils import (
     construct_input_messages,
@@ -541,6 +545,8 @@ class OpenAIServingResponses(OpenAIServing):
                 tokenizer,
                 request_metadata,
             )
+        except GenerationError as e:
+            return self._convert_generation_error_to_response(e)
         except Exception as e:
             return self.create_error_response(str(e))
 
@@ -648,6 +654,8 @@ class OpenAIServingResponses(OpenAIServing):
                     status = "incomplete"
                 elif context.finish_reason == "abort":
                     status = "cancelled"
+                else:
+                    self._raise_if_error(context.finish_reason, request.request_id)
             else:
                 status = "incomplete"
         elif isinstance(context, ParsableContext):
@@ -673,6 +681,9 @@ class OpenAIServingResponses(OpenAIServing):
             assert len(final_res.outputs) == 1
             final_output = final_res.outputs[0]
 
+            # finish_reason='error' indicates retryable internal error
+            self._raise_if_error(final_output.finish_reason, request.request_id)
+
             output = self._make_response_output_items(request, final_output, tokenizer)
 
             if request.enable_response_messages:
@@ -1066,6 +1077,8 @@ class OpenAIServingResponses(OpenAIServing):
             async for event in generator:
                 event_deque.append(event)
                 new_event_signal.set()  # Signal new event available
+        except GenerationError as e:
+            response = self._convert_generation_error_to_response(e)
         except Exception as e:
             logger.exception("Background request failed for %s", request.request_id)
             response = self.create_error_response(str(e))
@@ -1089,6 +1102,8 @@ class OpenAIServingResponses(OpenAIServing):
     ):
         try:
             response = await self.responses_full_generator(request, *args, **kwargs)
+        except GenerationError as e:
+            response = self._convert_generation_error_to_response(e)
         except Exception as e:
             logger.exception("Background request failed for %s", request.request_id)
             response = self.create_error_response(str(e))
@@ -1227,6 +1242,8 @@ class OpenAIServingResponses(OpenAIServing):
                 continue
             if ctx.last_output.outputs:
                 output = ctx.last_output.outputs[0]
+                # finish_reason='error' indicates a retryable error
+                self._raise_if_error(output.finish_reason, request.request_id)
                 if reasoning_parser:
                     delta_message = reasoning_parser.extract_reasoning_streaming(
                         previous_text=previous_text,
@@ -1522,6 +1539,9 @@ class OpenAIServingResponses(OpenAIServing):
         async for ctx in result_generator:
             assert isinstance(ctx, StreamingHarmonyContext)
 
+            # finish_reason='error' indicates a retryable error
+            self._raise_if_error(ctx.finish_reason, request.request_id)
+
             if ctx.is_expecting_start():
                 current_output_index += 1
                 sent_output_item_added = False
@@ -2016,18 +2036,25 @@ class OpenAIServingResponses(OpenAIServing):
                 )
             )
 
-            async for event_data in processer(
+            try:
-                request,
+                async for event_data in processer(
-                sampling_params,
+                    request,
-                result_generator,
+                    sampling_params,
-                context,
+                    result_generator,
-                model_name,
+                    context,
-                tokenizer,
+                    model_name,
-                request_metadata,
+                    tokenizer,
-                created_time,
+                    request_metadata,
-                _increment_sequence_number_and_return,
+                    created_time,
-            ):
+                    _increment_sequence_number_and_return,
-                yield event_data
+                ):
+                    yield event_data
+            except GenerationError as e:
+                error_json = self._convert_generation_error_to_streaming_response(e)
+                yield _increment_sequence_number_and_return(
+                    TypeAdapter(StreamingResponsesResponse).validate_json(error_json)
+                )
+                return
 
             async def empty_async_generator():
                 # A hack to trick Python to think this is a generator but

vllm/envs.py

@@ -72,10 +72,9 @@ if TYPE_CHECKING:
     VLLM_MAX_AUDIO_CLIP_FILESIZE_MB: int = 25
     VLLM_VIDEO_LOADER_BACKEND: str = "opencv"
     VLLM_MEDIA_CONNECTOR: str = "http"
-    VLLM_MM_INPUT_CACHE_GIB: int = 4
     VLLM_TARGET_DEVICE: str = "cuda"
     VLLM_MAIN_CUDA_VERSION: str = "12.9"
-    VLLM_FLOAT32_MATMUL_PRECISION: Literal["highest", "high", "medium"] = "highest"
+    VLLM_FLOAT32_MATMUL_PRECISION: Literal["ieee", "tf32"] = "ieee"
     MAX_JOBS: str | None = None
     NVCC_THREADS: str | None = None
     VLLM_USE_PRECOMPILED: bool = False
@@ -457,11 +456,13 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_MAIN_CUDA_VERSION": lambda: os.getenv("VLLM_MAIN_CUDA_VERSION", "").lower()
     or "12.9",
     # Controls PyTorch float32 matmul precision mode within vLLM workers.
-    # Valid options mirror torch.set_float32_matmul_precision
+    # Accepted values:
+    #   - "ieee" (default): force full IEEE FP32 matmul precision.
+    #   - "tf32": enable TensorFloat32-based fast matmul.
     "VLLM_FLOAT32_MATMUL_PRECISION": env_with_choices(
         "VLLM_FLOAT32_MATMUL_PRECISION",
-        "highest",
+        "ieee",
-        ["highest", "high", "medium"],
+        ["ieee", "tf32"],
         case_sensitive=False,
     ),
     # Maximum number of compilation jobs to run in parallel.
@@ -786,9 +787,6 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # imported at runtime.
     # If a non-existing backend is used, an AssertionError will be thrown.
     "VLLM_MEDIA_CONNECTOR": lambda: os.getenv("VLLM_MEDIA_CONNECTOR", "http"),
-    # [DEPRECATED] Cache size (in GiB per process) for multimodal input cache
-    # Default is 4 GiB per API process + 4 GiB per engine core process
-    "VLLM_MM_INPUT_CACHE_GIB": lambda: int(os.getenv("VLLM_MM_INPUT_CACHE_GIB", "4")),
     # Path to the XLA persistent cache directory.
     # Only used for XLA devices such as TPUs.
     "VLLM_XLA_CACHE_PATH": lambda: os.path.expanduser(
@@ -1580,6 +1578,12 @@ def __getattr__(name: str):
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
 
 
+def _is_envs_cache_enabled() -> bool:
+    """Checked if __getattr__ is wrapped with functools.cache"""
+    global __getattr__
+    return hasattr(__getattr__, "cache_clear")
+
+
 def enable_envs_cache() -> None:
     """
     Enables caching of environment variables. This is useful for performance
@@ -1590,6 +1594,9 @@ def enable_envs_cache() -> None:
     runtime overhead. This also means that environment variables should NOT
     be updated after the service is initialized.
     """
+    if _is_envs_cache_enabled():
+        # Avoid wrapping functools.cache multiple times
+        return
     # Tag __getattr__ with functools.cache
     global __getattr__
     __getattr__ = functools.cache(__getattr__)
@@ -1599,6 +1606,17 @@ def enable_envs_cache() -> None:
         __getattr__(key)
 
 
+def disable_envs_cache() -> None:
+    """
+    Resets the environment variables cache. It could be used to isolate environments
+    between unit tests.
+    """
+    global __getattr__
+    # If __getattr__ is wrapped by functions.cache, unwrap the caching layer.
+    if _is_envs_cache_enabled():
+        __getattr__ = __getattr__.__wrapped__
+
+
 def __dir__():
     return list(environment_variables.keys())
 
@@ -1661,7 +1679,6 @@ def compile_factors() -> dict[str, object]:
         "VLLM_MEDIA_CONNECTOR",
         "VLLM_ASSETS_CACHE",
         "VLLM_ASSETS_CACHE_MODEL_CLEAN",
-        "VLLM_MM_INPUT_CACHE_GIB",
         "VLLM_WORKER_MULTIPROC_METHOD",
         "VLLM_ENABLE_V1_MULTIPROCESSING",
         "VLLM_V1_OUTPUT_PROC_CHUNK_SIZE",

vllm/model_executor/layers/fused_moe/layer.py

@@ -1556,6 +1556,14 @@ class FusedMoE(CustomOp):
                     f"EPLB is not supported for {self.quant_method.method_name}."
                 )
 
+        def valid_grouping() -> bool:
+            # Check if num_experts is greater than num_expert_group
+            # and is divisible by num_expert_group
+            num_experts = router_logits.shape[-1]
+            if num_experts <= self.num_expert_group:
+                return False
+            return num_experts % self.num_expert_group == 0
+
         indices_type = self.quant_method.topk_indices_dtype
 
         # Check if we should use a routing simulation strategy
@@ -1570,7 +1578,7 @@ class FusedMoE(CustomOp):
             )
 
         # DeepSeekv2 uses grouped_top_k
-        elif self.use_grouped_topk:
+        elif self.use_grouped_topk and valid_grouping():
             assert self.topk_group is not None
             assert self.num_expert_group is not None
             if rocm_aiter_ops.is_fused_moe_enabled():

vllm/platforms/rocm.py

@@ -403,7 +403,21 @@ class RocmPlatform(Platform):
                 compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
 
         if cache_config and cache_config.block_size is None:
-            cache_config.block_size = 16
+            if (
+                envs.VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION and envs.VLLM_ROCM_USE_AITER
+                # NOTE: This block has been deprecated
+                # or get_env_variable_attn_backend()
+                # == AttentionBackendEnum.ROCM_AITER_UNIFIED_ATTN
+                # TODO: monitor https://github.com/vllm-project/vllm/pull/30396
+                # to see how we can transition to the new way of selecting
+                # attention backends
+            ):
+                cache_config.block_size = 64
+                logger.warning(
+                    "[ROCM_AITER_UNIFIED_ATTN]: Setting kv cache block size to 64."
+                )
+            else:
+                cache_config.block_size = 16
 
         if parallel_config.worker_cls == "auto":
             parallel_config.worker_cls = "vllm.v1.worker.gpu_worker.Worker"

vllm/utils/flashinfer.py

@@ -269,6 +269,8 @@ def supports_trtllm_attention() -> bool:
 
 def force_use_trtllm_attention() -> bool | None:
     """
+    This function should only be called during initialization stage when vllm config
+    is set.
     Return `None` if --attention-config.use_trtllm_attention is not set,
     return `True` if TRTLLM attention is forced to be used,
     return `False` if TRTLLM attention is forced to be not used.
@@ -296,11 +298,12 @@ def use_trtllm_attention(
     kv_cache_dtype: str,
     q_dtype: torch.dtype,
     is_prefill: bool,
+    # None means auto-detection, True means force on, False means force off
+    force_use_trtllm: bool | None = None,
     has_sinks: bool = False,
     has_spec: bool = False,
 ) -> bool:
     """Return `True` if TRTLLM attention is used."""
-    force_use_trtllm = force_use_trtllm_attention()
 
     # CLI argument is set to 0 - respect it
     if force_use_trtllm is not None and not force_use_trtllm:

vllm/v1/attention/backends/flashinfer.py

@@ -429,6 +429,7 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
         super().__init__(kv_cache_spec, layer_names, vllm_config, device)
         self.cache_config = vllm_config.cache_config
         self.model_config = vllm_config.model_config
+        self.attention_config = vllm_config.attention_config
         self._workspace_buffer = None
         self._prefill_wrapper: (
             BatchPrefillWithPagedKVCacheWrapper | BatchDCPPrefillWrapper | None
@@ -779,6 +780,7 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
             self.cache_dtype,
             self.q_data_type,
             is_prefill=True,
+            force_use_trtllm=self.attention_config.use_trtllm_attention,
             has_sinks=self.has_sinks,
             has_spec=uses_spec_reorder,
         )

vllm/v1/core/block_pool.py

@@ -397,6 +397,25 @@ class BlockPool:
             [block for block in blocks_list if block.ref_cnt == 0 and not block.is_null]
         )
 
+    def evict_blocks(self, block_ids: set[int]) -> None:
+        """evict blocks from the prefix cache by their block IDs.
+
+        only evicts blocks that are currently cached (have a hash). blocks
+        with ref_cnt > 0 are not freed from the block pool, only evicted
+        from the prefix cache hash table.
+
+        Args:
+            block_ids: Set of block IDs to evict from cache.
+        """
+        for block_id in block_ids:
+            assert block_id < len(self.blocks), (
+                f"Invalid block_id {block_id} >= {len(self.blocks)}. "
+                f"This indicates a bug in the KV connector - workers should "
+                f"only report block IDs that were allocated by the scheduler."
+            )
+            block = self.blocks[block_id]
+            self._maybe_evict_cached_block(block)
+
     def reset_prefix_cache(self) -> bool:
         """Reset prefix cache. This function may be used in RLHF
         flows to invalid prefix caching after the weights are updated,

vllm/v1/core/kv_cache_manager.py

@@ -333,6 +333,14 @@ class KVCacheManager:
         """
         self.coordinator.free(request.request_id)
 
+    def evict_blocks(self, block_ids: set[int]) -> None:
+        """evict blocks from the prefix cache by their block IDs.
+
+        Args:
+            block_ids: Set of block IDs to evict from cache.
+        """
+        self.block_pool.evict_blocks(block_ids)
+
     def reset_prefix_cache(self) -> bool:
         """Reset prefix cache. This function may be used in RLHF
         flows to invalidate prefix caching after the weights are updated,

vllm/v1/core/kv_cache_utils.py

@@ -687,7 +687,9 @@ def check_enough_kv_cache_memory(
         raise ValueError(
             "No available memory for the cache blocks. "
             "Try increasing `gpu_memory_utilization` when "
-            "initializing the engine."
+            "initializing the engine. "
+            "See https://docs.vllm.ai/en/latest/configuration/conserving_memory/ "
+            "for more details."
         )
 
     max_model_len = vllm_config.model_config.max_model_len
@@ -711,8 +713,10 @@ def check_enough_kv_cache_memory(
             f"cache is needed, which is larger than the available KV cache "
             f"memory ({available_memory / GiB_bytes:.2f} GiB). "
             f"{estimated_msg} "
-            f"Try increasing `gpu_memory_utilization` or decreasing "
+            f"Try increasing `gpu_memory_utilization` or decreasing `max_model_len` "
-            f"`max_model_len` when initializing the engine."
+            f"when initializing the engine. "
+            f"See https://docs.vllm.ai/en/latest/configuration/conserving_memory/ "
+            f"for more details."
         )
 
 

vllm/v1/core/sched/scheduler.py

@@ -106,6 +106,7 @@ class Scheduler(SchedulerInterface):
         # KV Connector pushes/pull of remote KVs for P/D and offloading.
         self.connector = None
         self.connector_prefix_cache_stats: PrefixCacheStats | None = None
+        self.recompute_kv_load_failures = True
         if self.vllm_config.kv_transfer_config is not None:
             assert not self.is_encoder_decoder, (
                 "Encoder-decoder models are not currently supported with KV connectors"
@@ -117,6 +118,10 @@ class Scheduler(SchedulerInterface):
             )
             if self.log_stats:
                 self.connector_prefix_cache_stats = PrefixCacheStats()
+            kv_load_failure_policy = (
+                self.vllm_config.kv_transfer_config.kv_load_failure_policy
+            )
+            self.recompute_kv_load_failures = kv_load_failure_policy == "recompute"
 
         self.kv_event_publisher = EventPublisherFactory.create(
             self.kv_events_config,
@@ -1066,7 +1071,7 @@ class Scheduler(SchedulerInterface):
         for req_id, num_tokens_scheduled in num_scheduled_tokens.items():
             assert num_tokens_scheduled > 0
             if failed_kv_load_req_ids and req_id in failed_kv_load_req_ids:
-                # Skip requests that were recovered from KV load failure
+                # skip failed or rescheduled requests from KV load failure
                 continue
             request = self.requests.get(req_id)
             if request is None:
@@ -1177,6 +1182,21 @@ class Scheduler(SchedulerInterface):
             # This is a rare case and unlikely to impact performance.
             self.waiting.remove_requests(stopped_preempted_reqs)
 
+        if failed_kv_load_req_ids and not self.recompute_kv_load_failures:
+            requests = [self.requests[req_id] for req_id in failed_kv_load_req_ids]
+            self.finish_requests(failed_kv_load_req_ids, RequestStatus.FINISHED_ERROR)
+            for request in requests:
+                outputs[request.client_index].append(
+                    EngineCoreOutput(
+                        request_id=request.request_id,
+                        new_token_ids=[],
+                        finish_reason=request.get_finished_reason(),
+                        events=request.take_events(),
+                        trace_headers=request.trace_headers,
+                        num_cached_tokens=request.num_cached_tokens,
+                    )
+                )
+
         # KV Connector: update state for finished KV Transfers.
         if kv_connector_output:
             self._update_from_kv_xfer_finished(kv_connector_output)
@@ -1610,8 +1630,11 @@ class Scheduler(SchedulerInterface):
             self._free_blocks(self.requests[req_id])
 
     def _update_requests_with_invalid_blocks(
-        self, requests: Iterable[Request], invalid_block_ids: set[int]
+        self,
-    ) -> tuple[set[str], int]:
+        requests: Iterable[Request],
+        invalid_block_ids: set[int],
+        evict_blocks: bool = True,
+    ) -> tuple[set[str], int, set[int]]:
         """
         Identify and update requests affected by invalid KV cache blocks.
 
@@ -1623,16 +1646,21 @@ class Scheduler(SchedulerInterface):
         Args:
             requests: The set of requests to scan for invalid blocks.
             invalid_block_ids: IDs of invalid blocks.
+            evict_blocks: Whether to collect blocks for eviction (False for
+                async requests which aren't cached yet).
 
         Returns:
             tuple:
                 - affected_req_ids (set[str]): IDs of requests impacted by
                 invalid blocks.
                 - total_affected_tokens (int): Total number of tokens that must
-                be recomputed across all affected requests (for observability).
+                be recomputed across all affected requests.
+                - blocks_to_evict (set[int]): Block IDs to evict from cache,
+                including invalid blocks and downstream dependent blocks.
         """
         affected_req_ids: set[str] = set()
         total_affected_tokens = 0
+        blocks_to_evict: set[int] = set()
         # If a block is invalid and shared by multiple requests in the batch,
         # these requests must be rescheduled, but only the first will recompute
         # it. This set tracks blocks already marked for recomputation.
@@ -1690,6 +1718,9 @@ class Scheduler(SchedulerInterface):
                 )
                 total_affected_tokens += num_affected_tokens
                 request.num_external_computed_tokens -= num_affected_tokens
+                # collect invalid block and all downstream dependent blocks
+                if evict_blocks:
+                    blocks_to_evict.update(req_block_ids[idx:])
 
             if is_affected:
                 if not marked_invalid_block:
@@ -1705,47 +1736,70 @@ class Scheduler(SchedulerInterface):
 
                 affected_req_ids.add(request.request_id)
 
-        return affected_req_ids, total_affected_tokens
+        return affected_req_ids, total_affected_tokens, blocks_to_evict
 
     def _handle_invalid_blocks(self, invalid_block_ids: set[int]) -> set[str]:
-        total_requests_to_reschedule = 0
+        """
-        total_tokens_to_reschedule = 0
+        Handle requests affected by invalid KV cache blocks.
 
-        # --- Handle async KV loads (WAITING_FOR_REMOTE_KVS) ---
+        Returns:
+            Set of affected request IDs to skip in update_from_output main loop.
+        """
+        should_fail = not self.recompute_kv_load_failures
+
+        # handle async KV loads (not cached yet, evict_blocks=False)
         async_load_reqs = (
             req
             for req in self.waiting
             if req.status == RequestStatus.WAITING_FOR_REMOTE_KVS
         )
-        async_affected_req_ids, num_tokens_to_reschedule = (
+        async_failed_req_ids, num_failed_tokens, _ = (
             self._update_requests_with_invalid_blocks(
-                async_load_reqs, invalid_block_ids
+                async_load_reqs, invalid_block_ids, evict_blocks=False
             )
         )
 
-        total_requests_to_reschedule += len(async_affected_req_ids)
+        total_failed_requests = len(async_failed_req_ids)
-        total_tokens_to_reschedule += num_tokens_to_reschedule
+        total_failed_tokens = num_failed_tokens
 
-        # Mark requests with async KV load failures; they will be rescheduled
+        # handle sync loads (may be cached, collect blocks for eviction)
-        # once loading completes.
+        sync_failed_req_ids, num_failed_tokens, sync_blocks_to_evict = (
-        self.failed_recving_kv_req_ids |= async_affected_req_ids
+            self._update_requests_with_invalid_blocks(
-
+                self.running, invalid_block_ids, evict_blocks=True
-        # --- Handle sync KV loads (running requests) ---
+            )
-        sync_affected_req_ids, num_tokens_to_reschedule = (
-            self._update_requests_with_invalid_blocks(self.running, invalid_block_ids)
         )
 
-        total_requests_to_reschedule += len(sync_affected_req_ids)
+        total_failed_requests += len(sync_failed_req_ids)
-        total_tokens_to_reschedule += num_tokens_to_reschedule
+        total_failed_tokens += num_failed_tokens
 
-        if total_requests_to_reschedule:
+        if not total_failed_requests:
-            logger.warning(
+            return set()
-                "Recovered from KV load failure: "
+
-                "%d request(s) rescheduled (%d tokens affected).",
+        # evict invalid blocks and downstream dependent blocks from cache
-                total_requests_to_reschedule,
+        # only when not using recompute policy (where blocks will be recomputed
-                total_tokens_to_reschedule,
+        # and reused by other requests sharing them)
+        if sync_blocks_to_evict and not self.recompute_kv_load_failures:
+            self.kv_cache_manager.evict_blocks(sync_blocks_to_evict)
+
+        if should_fail:
+            all_failed_req_ids = async_failed_req_ids | sync_failed_req_ids
+            logger.error(
+                "Failing %d request(s) due to KV load failure "
+                "(failure_policy=fail, %d tokens affected). Request IDs: %s",
+                total_failed_requests,
+                total_failed_tokens,
+                all_failed_req_ids,
             )
+            return all_failed_req_ids
 
-        # Return the IDs of affected running requests to skip in
+        logger.warning(
-        # update_from_output.
+            "Recovered from KV load failure: "
-        return sync_affected_req_ids
+            "%d request(s) rescheduled (%d tokens affected).",
+            total_failed_requests,
+            total_failed_tokens,
+        )
+
+        # Mark async requests with KV load failures for retry once loading completes
+        self.failed_recving_kv_req_ids |= async_failed_req_ids
+        # Return sync affected IDs to skip in update_from_output
+        return sync_failed_req_ids

vllm/v1/engine/__init__.py

@@ -19,24 +19,27 @@ from vllm.v1.serial_utils import UtilityResult
 
 # These are possible values of RequestOutput.finish_reason,
 # so form part of the external API.
-FINISH_REASON_STRINGS = ("stop", "length", "abort")
+FINISH_REASON_STRINGS = ("stop", "length", "abort", "error")
 
 
 class FinishReason(enum.IntEnum):
     """
-    Reason a request finished - stop, length, or abort.
+    Reason a request finished - stop, length, abort, or error.
 
     Int rather than Str for more compact serialization.
 
     stop - a stop string was emitted
     length - max_tokens was consumed, or max_model_len was reached
-    abort - aborted for another reason
+    abort - aborted by client
+    error - retryable request-level internal error (e.g., KV load failure).
+            Invariant: always converted to 500 Internal Server Error.
 
     """
 
     STOP = 0
     LENGTH = 1
     ABORT = 2
+    ERROR = 3
 
     def __str__(self):
         return FINISH_REASON_STRINGS[self.value]

vllm/v1/engine/async_llm.py

@@ -701,10 +701,6 @@ class AsyncLLM(EngineClient):
     def tokenizer(self) -> TokenizerLike | None:
         return self.input_processor.tokenizer
 
-    @tokenizer.setter
-    def tokenizer(self, tokenizer: TokenizerLike | None) -> None:
-        self.input_processor.tokenizer = tokenizer
-
     async def get_tokenizer(self) -> TokenizerLike:
         if self.tokenizer is None:
             raise ValueError(

vllm/v1/engine/core.py

@@ -211,6 +211,9 @@ class EngineCore:
         freeze_gc_heap()
         # If enable, attach GC debugger after static variable freeze.
         maybe_attach_gc_debug_callback()
+        # Enable environment variable cache (e.g. assume no more
+        # environment variable overrides after this point)
+        enable_envs_cache()
 
     def _initialize_kv_caches(
         self, vllm_config: VllmConfig
@@ -672,10 +675,6 @@ class EngineCoreProc(EngineCore):
                 assert addresses.coordinator_input is not None
                 logger.info("Waiting for READY message from DP Coordinator...")
 
-        # Enable environment variable cache (e.g. assume no more
-        # environment variable overrides after this point)
-        enable_envs_cache()
-
     @contextmanager
     def _perform_handshakes(
         self,

vllm/v1/engine/input_processor.py

@@ -64,10 +64,6 @@ class InputProcessor:
     def tokenizer(self) -> TokenizerLike | None:
         return self.input_preprocessor.tokenizer
 
-    @tokenizer.setter
-    def tokenizer(self, tokenizer: TokenizerLike | None) -> None:
-        self.input_preprocessor.tokenizer = tokenizer
-
     def _validate_logprobs(
         self,
         params: SamplingParams,

vllm/v1/engine/llm_engine.py

@@ -358,10 +358,6 @@ class LLMEngine:
     def tokenizer(self) -> TokenizerLike | None:
         return self.input_processor.tokenizer
 
-    @tokenizer.setter
-    def tokenizer(self, tokenizer: TokenizerLike | None) -> None:
-        self.input_processor.tokenizer = tokenizer
-
     def get_tokenizer(self) -> TokenizerLike:
         if self.tokenizer is None:
             raise ValueError(

vllm/v1/request.py

@@ -255,6 +255,7 @@ class RequestStatus(enum.IntEnum):
     FINISHED_LENGTH_CAPPED = enum.auto()
     FINISHED_ABORTED = enum.auto()
     FINISHED_IGNORED = enum.auto()
+    FINISHED_ERROR = enum.auto()
 
     def __str__(self):
         return self.name
@@ -277,4 +278,5 @@ _FINISHED_REASON_MAP = {
     RequestStatus.FINISHED_LENGTH_CAPPED: FinishReason.LENGTH,
     RequestStatus.FINISHED_ABORTED: FinishReason.ABORT,
     RequestStatus.FINISHED_IGNORED: FinishReason.LENGTH,
+    RequestStatus.FINISHED_ERROR: FinishReason.ERROR,
 }

vllm/v1/worker/gpu_model_runner.py

@@ -3571,74 +3571,89 @@ class GPUModelRunner(
         if self.parallel_config.enable_eplb:
             self.eplb_state = EplbState(self.parallel_config, self.device)
             eplb_models = 0
-        with DeviceMemoryProfiler() as m:
+
-            time_before_load = time.perf_counter()
+        try:
-            model_loader = get_model_loader(self.load_config)
+            with DeviceMemoryProfiler() as m:
-            self.model = model_loader.load_model(
+                time_before_load = time.perf_counter()
-                vllm_config=self.vllm_config, model_config=self.model_config
+                model_loader = get_model_loader(self.load_config)
-            )
+                self.model = model_loader.load_model(
-            if self.lora_config:
+                    vllm_config=self.vllm_config, model_config=self.model_config
-                self.model = self.load_lora_model(
-                    self.model, self.vllm_config, self.device
                 )
-            if hasattr(self, "drafter"):
+                if self.lora_config:
-                logger.info_once("Loading drafter model...")
+                    self.model = self.load_lora_model(
-                self.drafter.load_model(self.model)
+                        self.model, self.vllm_config, self.device
-                if (
-                    hasattr(self.drafter, "model")
-                    and is_mixture_of_experts(self.drafter.model)
-                    and self.parallel_config.enable_eplb
-                ):
-                    spec_config = self.vllm_config.speculative_config
-                    assert spec_config is not None
-                    assert spec_config.draft_model_config is not None
-                    logger.info_once(
-                        "EPLB is enabled for drafter model %s.",
-                        spec_config.draft_model_config.model,
                     )
+                if hasattr(self, "drafter"):
+                    logger.info_once("Loading drafter model...")
+                    self.drafter.load_model(self.model)
+                    if (
+                        hasattr(self.drafter, "model")
+                        and is_mixture_of_experts(self.drafter.model)
+                        and self.parallel_config.enable_eplb
+                    ):
+                        spec_config = self.vllm_config.speculative_config
+                        assert spec_config is not None
+                        assert spec_config.draft_model_config is not None
+                        logger.info_once(
+                            "EPLB is enabled for drafter model %s.",
+                            spec_config.draft_model_config.model,
+                        )
 
-                    global_expert_load = (
+                        global_expert_load = (
-                        global_expert_loads[eplb_models]
+                            global_expert_loads[eplb_models]
-                        if global_expert_loads
+                            if global_expert_loads
-                        else None
+                            else None
-                    )
+                        )
-                    old_global_expert_indices = (
+                        old_global_expert_indices = (
-                        old_global_expert_indices_per_model[eplb_models]
+                            old_global_expert_indices_per_model[eplb_models]
-                        if old_global_expert_indices_per_model
+                            if old_global_expert_indices_per_model
-                        else None
+                            else None
-                    )
+                        )
-                    if self.eplb_state is None:
+                        if self.eplb_state is None:
-                        self.eplb_state = EplbState(self.parallel_config, self.device)
+                            self.eplb_state = EplbState(
-                    self.eplb_state.add_model(
+                                self.parallel_config, self.device
-                        self.drafter.model,
+                            )
-                        spec_config.draft_model_config,
+                        self.eplb_state.add_model(
-                        global_expert_load,
+                            self.drafter.model,
-                        old_global_expert_indices,
+                            spec_config.draft_model_config,
-                        rank_mapping,
+                            global_expert_load,
-                    )
+                            old_global_expert_indices,
-                    eplb_models += 1
+                            rank_mapping,
+                        )
+                        eplb_models += 1
 
-            if self.use_aux_hidden_state_outputs:
+                if self.use_aux_hidden_state_outputs:
-                if not supports_eagle3(self.get_model()):
+                    if not supports_eagle3(self.get_model()):
-                    raise RuntimeError(
+                        raise RuntimeError(
-                        "Model does not support EAGLE3 interface but "
+                            "Model does not support EAGLE3 interface but "
-                        "aux_hidden_state_outputs was requested"
+                            "aux_hidden_state_outputs was requested"
-                    )
+                        )
 
-                # Try to get auxiliary layers from speculative config,
+                    # Try to get auxiliary layers from speculative config,
-                # otherwise use model's default layers
+                    # otherwise use model's default layers
-                aux_layers = self._get_eagle3_aux_layers_from_config()
+                    aux_layers = self._get_eagle3_aux_layers_from_config()
-                if aux_layers:
+                    if aux_layers:
-                    logger.info(
+                        logger.info(
-                        "Using auxiliary layers from speculative config: %s",
+                            "Using auxiliary layers from speculative config: %s",
-                        aux_layers,
+                            aux_layers,
-                    )
+                        )
-                else:
+                    else:
-                    aux_layers = self.model.get_eagle3_aux_hidden_state_layers()
+                        aux_layers = self.model.get_eagle3_aux_hidden_state_layers()
 
-                self.model.set_aux_hidden_state_layers(aux_layers)
+                    self.model.set_aux_hidden_state_layers(aux_layers)
-            time_after_load = time.perf_counter()
+                time_after_load = time.perf_counter()
-        self.model_memory_usage = m.consumed_memory
+            self.model_memory_usage = m.consumed_memory
+        except torch.cuda.OutOfMemoryError as e:
+            msg = (
+                "Failed to load model - not enough GPU memory. "
+                "Try lowering --gpu-memory-utilization to free memory for weights, "
+                "increasing --tensor-parallel-size, or using --quantization. "
+                "See https://docs.vllm.ai/en/latest/configuration/conserving_memory/ "
+                "for more tips."
+            )
+            combined_msg = f"{msg} (original error: {e})"
+            logger.error(combined_msg)
+            raise e
         logger.info_once(
             "Model loading took %.4f GiB memory and %.6f seconds",
             self.model_memory_usage / GiB_bytes,

vllm/v1/worker/gpu_worker.py

@@ -81,7 +81,7 @@ class Worker(WorkerBase):
 
         # configure float32 matmul precision according to vLLM env.
         precision = envs.VLLM_FLOAT32_MATMUL_PRECISION
-        torch.set_float32_matmul_precision(precision)
+        torch.backends.cuda.matmul.fp32_precision = precision
 
         if self.model_config.trust_remote_code:
             # note: lazy import to avoid importing torch before initializing