fix python install ci error

Signed-off-by: yewentao256 <zhyanwentao@126.com>

.buildkite/test-amd.yaml

@@ -39,9 +39,9 @@ steps:
   # if this test fails, it means the nightly torch version is not compatible with some
   # of the dependencies. Please check the error message and add the package to whitelist
   # in /vllm/tools/pre_commit/generate_nightly_torch_test.py
-  mirror_hardwares: [amdexperimental, amdproduction, amdtentative]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
-  grade: Blocking
+  # grade: Blocking
   soft_fail: true
   source_file_dependencies:
   - requirements/nightly_torch_test.txt
@@ -50,9 +50,9 @@ steps:
 
 - label: Async Engine, Inputs, Utils, Worker Test # 10min
   timeout_in_minutes: 15
-  mirror_hardwares: [amdexperimental, amdproduction, amdtentative]
+  mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi325_1
-  grade: Blocking
+  # grade: Blocking
   source_file_dependencies:
   - vllm/
   - tests/multimodal
@@ -63,9 +63,9 @@ steps:
 
 - label: Async Engine, Inputs, Utils, Worker, Config Test (CPU) # 15min
   timeout_in_minutes: 20
-  mirror_hardwares: [amdexperimental, amdproduction, amdtentative]
+  mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi325_1
-  grade: Blocking
+  # grade: Blocking
   source_file_dependencies:
   - vllm/
   - tests/test_inputs.py
@@ -115,9 +115,9 @@ steps:
   - pytest -v -s basic_correctness/test_cpu_offload.py
 
 - label: Entrypoints Unit Tests # 5min
-  mirror_hardwares: [amdexperimental, amdproduction, amdtentative]
+  mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi325_1
-  grade: Blocking
+  # grade: Blocking
   timeout_in_minutes: 10
   working_dir: "/vllm-workspace/tests"
   fast_check: true
@@ -214,7 +214,6 @@ steps:
   # test with internal dp
   - python3 ../examples/offline_inference/data_parallel.py --enforce-eager
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
-  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
   - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_internal_lb_dp.py
   - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/distributed/test_hybrid_lb_dp.py
@@ -253,9 +252,9 @@ steps:
   - torchrun --nproc-per-node=8 ../examples/offline_inference/torchrun_dp_example.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
 
 - label: EPLB Algorithm Test # 5min
-  mirror_hardwares: [amdexperimental, amdproduction, amdtentative]
+  mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi325_1
-  grade: Blocking
+  # grade: Blocking
   timeout_in_minutes: 15
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -342,9 +341,9 @@ steps:
 
 - label: V1 Test entrypoints # 35min
   timeout_in_minutes: 50
-  mirror_hardwares: [amdexperimental, amdproduction, amdtentative]
+  mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi325_1
-  grade: Blocking
+  # grade: Blocking
   source_file_dependencies:
     - vllm/
     - tests/v1
@@ -392,20 +391,6 @@ steps:
   commands:
     - pytest -v -s v1/attention
 
-- label: Batch Invariance Tests (H100) # 10min
-  mirror_hardwares: [amdexperimental]
-  agent_pool: mi325_1
-  timeout_in_minutes: 25
-  gpu: h100
-  source_file_dependencies:
-    - vllm/
-    - tests/v1/determinism/
-  commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pip install pytest-timeout pytest-forked
-    - pytest -v -s v1/determinism/test_batch_invariance.py
-    - pytest -v -s v1/determinism/test_rms_norm_batch_invariant.py
-
 - label: V1 Test attention (B200) # 10min
   timeout_in_minutes: 30
   gpu: b200
@@ -416,9 +401,9 @@ steps:
     - VLLM_DISABLE_FLASHINFER_PREFILL=1 pytest -v -s v1/attention # TODO: FI prefill is bugged and causes incorrectness, fix this
 
 - label: V1 Test others (CPU) # 5 mins
-  mirror_hardwares: [amdexperimental, amdproduction, amdtentative]
+  mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi325_1
-  grade: Blocking
+  # grade: Blocking
   source_file_dependencies:
     - vllm/
     - tests/v1
@@ -510,7 +495,7 @@ steps:
 
 - label: PyTorch Compilation Unit Tests # 15min
   timeout_in_minutes: 30
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
   torch_nightly: true
@@ -527,7 +512,7 @@ steps:
 
 - label: PyTorch Fullgraph Smoke Test # 15min
   timeout_in_minutes: 30
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
   torch_nightly: true
@@ -583,7 +568,7 @@ steps:
 
 - label: Kernels Attention Test %N # 23min
   timeout_in_minutes: 35
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_8
   # grade: Blocking
   source_file_dependencies:
@@ -610,7 +595,7 @@ steps:
 
 - label: Kernels MoE Test %N # 40min
   timeout_in_minutes: 60
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_8
   # grade: Blocking
   source_file_dependencies:
@@ -637,26 +622,6 @@ steps:
   commands:
     - pytest -v -s kernels/mamba
 
-- label: Kernels DeepGEMM Test (H100) # Nvidia-centric
-# Not replicating for CUTLAS & CuTe
-  timeout_in_minutes: 45
-  gpu: h100
-  num_gpus: 1
-  source_file_dependencies:
-  - tools/install_deepgemm.sh
-  - vllm/utils/deep_gemm.py
-  - vllm/model_executor/layers/fused_moe
-  - vllm/model_executor/layers/quantization
-  - tests/kernels/quantization/test_block_fp8.py
-  - tests/kernels/moe/test_deepgemm.py
-  - tests/kernels/moe/test_batched_deepgemm.py
-  - tests/kernels/attention/test_deepgemm_attention.py
-  commands:
-    - pytest -v -s kernels/quantization/test_block_fp8.py -k deep_gemm
-    - pytest -v -s kernels/moe/test_deepgemm.py
-    - pytest -v -s kernels/moe/test_batched_deepgemm.py
-    - pytest -v -s kernels/attention/test_deepgemm_attention.py
-
 - label: Model Executor Test # 23min
   timeout_in_minutes: 35
   torch_nightly: true
@@ -1090,7 +1055,6 @@ steps:
     - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
     - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
     - pytest -v -s tests/kernels/moe/test_flashinfer.py
-    - pytest -v -s tests/kernels/moe/test_cutedsl_moe.py
 
 - label: Blackwell Fusion and Compile Tests # 30 min
   timeout_in_minutes: 40
@@ -1100,19 +1064,11 @@ steps:
   - csrc/quantization/fp4/
   - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
   - vllm/v1/attention/backends/flashinfer.py
-  - vllm/v1/worker/
-  - vllm/v1/cudagraph_dispatcher.py
   - vllm/compilation/
   # can affect pattern matching
   - vllm/model_executor/layers/layernorm.py
   - vllm/model_executor/layers/activation.py
   - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  - vllm/model_executor/layers/fused_moe/layer.py
-  - tests/compile/test_fusion_attn.py
-  - tests/compile/test_silu_mul_quant_fusion.py
-  - tests/compile/distributed/test_fusion_all_reduce.py
-  - tests/compile/distributed/test_fusions_e2e.py
-  - tests/compile/fullgraph/test_full_graph.py
   commands:
     - nvidia-smi
     - pytest -v -s tests/compile/test_fusion_attn.py
@@ -1123,7 +1079,7 @@ steps:
     # Wrap with quotes to escape yaml
     - "pytest -v -s tests/compile/distributed/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm -k 'True and not +quant_fp8 and not +rms_norm'"
     # test_fp8_kv_scale_compile requires FlashAttention (not supported on default L4/L40)
-    - pytest -v -s tests/compile/fullgraph/test_full_graph.py::test_fp8_kv_scale_compile
+    - pytest -v -s tests/compile/distributed/test_full_graph.py::test_fp8_kv_scale_compile
 
 - label: Blackwell Fusion E2E Tests # 30 min
   timeout_in_minutes: 40
@@ -1145,7 +1101,7 @@ steps:
   commands:
     - nvidia-smi
     # Run all e2e fusion tests
-    - pytest -v -s tests/compile/distributed/test_fusions_e2e.py
+    - pytest -v -s tests/compile/test_fusions_e2e.py
 
 - label: ROCm GPT-OSS Eval
   timeout_in_minutes: 60
@@ -1260,7 +1216,6 @@ steps:
   - tests/v1/worker/test_worker_memory_snapshot.py
   commands:
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
   - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py
   - pytest -v -s entrypoints/llm/test_collective_rpc.py
@@ -1296,7 +1251,7 @@ steps:
 
 - label: Plugin Tests (2 GPUs) # 40min
   timeout_in_minutes: 60
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_2
   # grade: Blocking
   working_dir: "/vllm-workspace/tests"
@@ -1372,7 +1327,7 @@ steps:
 
 - label: Weight Loading Multiple GPU Test  # 33min
   timeout_in_minutes: 45
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_2
   # grade: Blocking
   working_dir: "/vllm-workspace/tests"
@@ -1477,7 +1432,7 @@ steps:
     - pytest -v -s tests/compile/distributed/test_fusion_all_reduce.py
     #- pytest -v -s tests/compile/distributed/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm
     - "pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'"
-    - pytest -v -s tests/distributed/test_sequence_parallel.py
+    - pytest -v -s tests/compile/distributed/test_sequence_parallel.py
     - pytest -v -s tests/distributed/test_context_parallel.py
     - CUDA_VISIBLE_DEVICES=1,2 VLLM_ALL2ALL_BACKEND=deepep_high_throughput VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model Qwen/Qwen1.5-MoE-A2.7B --tp-size=1  --dp-size=2 --max-model-len 2048
     - pytest -v -s tests/v1/distributed/test_dbo.py
@@ -1509,7 +1464,7 @@ steps:
     - bash .buildkite/scripts/run-prime-rl-test.sh
 
 - label: DeepSeek V2-Lite Accuracy
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_4
   # grade: Blocking
   timeout_in_minutes: 60
@@ -1520,8 +1475,8 @@ steps:
   commands:
   - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh 0.25 200 8010
 
-- label: Qwen3-30B-A3B-FP8-block Accuracy (H100)
-  mirror_hardwares: [amdexperimental, amdproduction]
+- label: Qwen3-30B-A3B-FP8-block Accuracy
+  mirror_hardwares: [amdexperimental]
   agent_pool: mi325_4
   # grade: Blocking
   timeout_in_minutes: 60
@@ -1531,12 +1486,3 @@ steps:
   working_dir: "/vllm-workspace"
   commands:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020
-
-- label: Qwen3-30B-A3B-FP8-block Accuracy (B200)
-  timeout_in_minutes: 60
-  gpu: b200
-  optional: true
-  num_gpus: 2
-  working_dir: "/vllm-workspace"
-  commands:
-  - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1

setup.py

@@ -310,6 +310,11 @@ class cmake_build_ext(build_ext):
 class precompiled_build_ext(build_ext):
     """Disables extension building when using precompiled binaries."""
 
+    def finalize_options(self) -> None:
+        # use the project root as build_lib
+        super().finalize_options()
+        self.build_lib = str(ROOT_DIR)
+
     def run(self) -> None:
         assert _is_cuda(), "VLLM_USE_PRECOMPILED is only supported for CUDA builds"
 
@@ -640,6 +645,9 @@ if _is_cuda():
 if _build_custom_ops():
     ext_modules.append(CMakeExtension(name="vllm._C"))
 
+if envs.VLLM_USE_PRECOMPILED:
+    ext_modules = [ext for ext in ext_modules if ext.name != "vllm.triton_kernels"]
+
 package_data = {
     "vllm": [
         "py.typed",

tests/v1/e2e/test_spec_decode.py

@@ -191,8 +191,8 @@ def test_suffix_decoding_acceptance(
     # Expect the acceptance rate to improve.
     assert first_accept_rate < last_accept_rate
 
-    # Heuristic: expect at least 82.5% acceptance rate at the end.
-    assert last_accept_rate > 0.825
+    # Heuristic: expect at least 85% acceptance rate at the end.
+    assert last_accept_rate > 0.85
 
     del spec_llm
     torch.cuda.empty_cache()

vllm/engine/arg_utils.py

@@ -571,6 +571,7 @@ class EngineArgs:
     kv_offloading_backend: KVOffloadingBackend | None = (
         CacheConfig.kv_offloading_backend
     )
+    tokens_only: bool = False
 
     def __post_init__(self):
         # support `EngineArgs(compilation_config={...})`
@@ -1570,6 +1571,10 @@ class EngineArgs:
             else ParallelConfig.data_parallel_rpc_port
         )
 
+        if self.tokens_only and not model_config.skip_tokenizer_init:
+            model_config.skip_tokenizer_init = True
+            logger.info("Skipping tokenizer initialization for tokens-only mode.")
+
         if self.async_scheduling and not self.disable_nccl_for_dp_synchronization:
             logger.info(
                 "Disabling NCCL for DP synchronization when using async scheduling."

vllm/v1/core/sched/async_scheduler.py

@@ -33,7 +33,7 @@ class AsyncScheduler(Scheduler):
                 # in this scheduling step.
                 request.num_output_placeholders += 1 + cur_num_spec_tokens
                 # Add placeholders for the new tokens in spec_token_ids.
-                # We will update the actual spec token ids in the worker process.
+                # Wwe will update the actual spec token ids in the worker process.
                 request.spec_token_ids = [-1] * self.num_spec_tokens
 
         scheduler_output.pending_structured_output_tokens = (

vllm/v1/core/sched/scheduler.py

@@ -236,22 +236,6 @@ class Scheduler(SchedulerInterface):
         while req_index < len(self.running) and token_budget > 0:
             request = self.running[req_index]
 
-            if (
-                request.num_output_placeholders > 0
-                # This is (num_computed_tokens + 1) - (num_output_placeholders - 1).
-                # Since output placeholders are also included in the computed tokens
-                # count, we subtract (num_output_placeholders - 1) to remove any draft
-                # tokens, so that we can be sure no further steps are needed even if
-                # they are all rejected.
-                and request.num_computed_tokens + 2 - request.num_output_placeholders
-                >= request.num_prompt_tokens + request.max_tokens
-            ):
-                # Async scheduling: Avoid scheduling an extra step when we are sure that
-                # the previous step has reached request.max_tokens. We don't schedule
-                # partial draft tokens since this prevents uniform decode optimizations.
-                req_index += 1
-                continue
-
             num_new_tokens = (
                 request.num_tokens_with_spec
                 + request.num_output_placeholders
@@ -261,10 +245,18 @@ class Scheduler(SchedulerInterface):
                 num_new_tokens = self.scheduler_config.long_prefill_token_threshold
             num_new_tokens = min(num_new_tokens, token_budget)
 
-            # Make sure the input position does not exceed the max model len.
-            # This is necessary when using spec decoding.
+            num_spec_placeholders = max(0, request.num_output_placeholders - 1)
+            max_total_tokens = min(
+                # Avoid scheduling tokens that we're sure won't will be needed based on
+                # request.max_tokens. For this calculation we assume placeholder
+                # speculated output tokens are rejected.
+                request.num_prompt_tokens + request.max_tokens + num_spec_placeholders,
+                # Make sure the input position does not exceed the max model len.
+                # This is necessary when using spec decoding.
+                self.max_model_len,
+            )
             num_new_tokens = min(
-                num_new_tokens, self.max_model_len - 1 - request.num_computed_tokens
+                num_new_tokens, max_total_tokens - 1 - request.num_computed_tokens
             )
 
             # Schedule encoder inputs.
@@ -807,15 +799,15 @@ class Scheduler(SchedulerInterface):
         for idx, req in enumerate(itertools.chain(running_reqs, resumed_reqs)):
             req_id = req.request_id
             req_ids.append(req_id)
+            num_tokens = num_scheduled_tokens[req_id] - len(
+                spec_decode_tokens.get(req_id, ())
+            )
             if self.use_pp:
                 # When using PP, the scheduler sends the sampled tokens back,
                 # because there's no direct communication between the first-
                 # stage worker and the last-stage worker. Otherwise, we don't
                 # need to send the sampled tokens back because the model runner
                 # will cache them.
-                num_tokens = num_scheduled_tokens[req_id] - len(
-                    spec_decode_tokens.get(req_id, ())
-                )
                 token_ids = req.all_token_ids[
                     req.num_computed_tokens : req.num_computed_tokens + num_tokens
                 ]