Add vLLM v0.18.1 source tree with KV transfer abort fix

third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:

  vllm/v1/core/sched/scheduler.py:
    Replace fatal assert with graceful skip when KV transfer callback
    arrives for an already-aborted request during PD disaggregated serving.

Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
2026-05-22 00:30:38 +08:00
parent b6591950bc
commit 445e491123
4285 changed files with 1111303 additions and 1 deletions

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# For vllm script, with -t option (tensor parallel size).
# bash ./run-lm-eval-gsm-vllm-baseline.sh -m deepseek-ai/DeepSeek-V2-Lite-Chat -b "auto" -l 1000 -f 5 -t 2
model_name: "deepseek-ai/DeepSeek-V2-Lite-Chat"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.671
- name: "exact_match,flexible-extract"
value: 0.664
limit: 1000
num_fewshot: 5
trust_remote_code: True

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# For hf script, without -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m nm-testing/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform -b auto -l 1000 -f 5
model_name: "nm-testing/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.905
- name: "exact_match,flexible-extract"
value: 0.905
limit: 1000
num_fewshot: 5

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# For hf script, without -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m meta-llama/Meta-Llama-3-70B-Instruct -b 32 -l 250 -f 5
model_name: "meta-llama/Meta-Llama-3-70B-Instruct"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.892
- name: "exact_match,flexible-extract"
value: 0.892
limit: 250
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-W8A8-FP8-Channelwise-compressed-tensors -b auto -l 1000 -f 5 -t 1
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8A8-FP8-Channelwise-compressed-tensors"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.752
- name: "exact_match,flexible-extract"
value: 0.754
limit: 1000
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-FBGEMM-nonuniform -b auto -l 1000 -f 5 -t 1
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-FBGEMM-nonuniform"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.753
- name: "exact_match,flexible-extract"
value: 0.753
limit: 1000
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test -b 32 -l 1000 -f 5 -t 1
model_name: "nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.755
- name: "exact_match,flexible-extract"
value: 0.755
limit: 1000
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Meta-Llama-3-8B-Instruct-FP8 -b 32 -l 250 -f 5 -t 1
model_name: "neuralmagic/Meta-Llama-3-8B-Instruct-FP8"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.753
- name: "exact_match,flexible-extract"
value: 0.753
limit: 1000
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Asym-Per-Token-Test -b "auto" -l 250 -f 5 -t 1
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Asym-Per-Token-Test"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.764
- name: "exact_match,flexible-extract"
value: 0.764
limit: 250
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Per-Token-Test -b "auto" -l 250 -f 5 -t 1
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Per-Token-Test"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.728
- name: "exact_match,flexible-extract"
value: 0.728
limit: 250
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-nonuniform-test -b auto -l 1000 -f 5 -t 1
model_name: "nm-testing/Meta-Llama-3-8B-Instruct-nonuniform-test"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.758
- name: "exact_match,flexible-extract"
value: 0.759
limit: 1000
num_fewshot: 5

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# For hf script, without -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m meta-llama/Meta-Llama-3-8B-Instruct -b 32 -l 250 -f 5
model_name: "meta-llama/Meta-Llama-3-8B-Instruct"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.756
- name: "exact_match,flexible-extract"
value: 0.752
limit: 250
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m HandH1998/QQQ-Llama-3-8b-g128 -b 32 -l 1000 -f 5 -t 1
model_name: "HandH1998/QQQ-Llama-3-8b-g128"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.419
- name: "exact_match,flexible-extract"
value: 0.416
limit: 1000
num_fewshot: 5

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# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Llama-3.2-1B-Instruct-FP8 -b "auto" -l 1319 -f 5 -t 1
model_name: "RedHatAI/Llama-3.2-1B-Instruct-FP8"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.335
- name: "exact_match,flexible-extract"
value: 0.323
limit: 1319
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8 -b "auto" -l 1000 -f 5 -t 1
model_name: "neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.356
- name: "exact_match,flexible-extract"
value: 0.358
limit: 1000
num_fewshot: 5

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# For hf script, without -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -l 100 -t 8
model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
backend: "vllm-vlm"
tasks:
- name: "chartqa"
metrics:
- name: "relaxed_accuracy,none"
# TODO(zhewenl): model card is 0.90, but the actual score is 0.80.
value: 0.80
limit: 100
num_fewshot: 0

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# For hf script, without -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-mmlupro-vllm-baseline.sh -m meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 -l 250 -t 8 -f 5
model_name: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
tasks:
- name: "mmlu_pro"
metrics:
- name: "exact_match,custom-extract"
value: 0.80
limit: 250 # will run on 250 * 14 subjects = 3500 samples
num_fewshot: 5
rtol: 0.05

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# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m mgoin/Minitron-4B-Base-FP8 -b auto -l 1000 -f 5 -t 1
model_name: "mgoin/Minitron-4B-Base-FP8"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.231
- name: "exact_match,flexible-extract"
value: 0.22
limit: 1000
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size).
# bash ./run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Mixtral-8x22B-Instruct-v0.1-FP8-dynamic -b "auto" -l 250 -f 5 -t 8
model_name: "neuralmagic/Mixtral-8x22B-Instruct-v0.1-FP8-dynamic"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.86
- name: "exact_match,flexible-extract"
value: 0.86
limit: 250
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size).
# bash ./run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8 -b "auto" -l 250 -f 5 -t 4
model_name: "neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.624
- name: "exact_match,flexible-extract"
value: 0.624
limit: 250
num_fewshot: 5

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# For hf script, without -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m neuralmagic/Mixtral-8x7B-Instruct-v0.1 -b 32 -l 250 -f 5
model_name: "mistralai/Mixtral-8x7B-Instruct-v0.1"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.616
- name: "exact_match,flexible-extract"
value: 0.632
limit: 250
num_fewshot: 5

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model_name: "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.695
- name: "exact_match,flexible-extract"
value: 0.447
limit: 1319
num_fewshot: 5
max_model_len: 262144
enforce_eager: false
apply_chat_template: true
fewshot_as_multiturn: true
trust_remote_code: true

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model_name: "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.7142
- name: "exact_match,flexible-extract"
value: 0.4579
env_vars:
VLLM_USE_FLASHINFER_MOE_FP8: "1"
VLLM_FLASHINFER_MOE_BACKEND: "throughput"
limit: 1319
num_fewshot: 5
max_model_len: 262144
kv_cache_dtype: fp8
enforce_eager: false
apply_chat_template: true
fewshot_as_multiturn: true
trust_remote_code: true

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# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen1.5-MoE-A2.7B-Chat-quantized.w4a16 -b auto -l 1319 -f 5 -t 1
model_name: "nm-testing/Qwen1.5-MoE-A2.7B-Chat-quantized.w4a16"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.30
- name: "exact_match,flexible-extract"
value: 0.465
limit: 1319
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen2-1.5B-Instruct-FP8W8 -b auto -l 1000 -f 5 -t 1
model_name: "nm-testing/Qwen2-1.5B-Instruct-FP8W8"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.578
- name: "exact_match,flexible-extract"
value: 0.585
limit: 1000
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Qwen2-1.5B-Instruct-quantized.w8a8 -b "auto" -l 1000 -f 5 -t 1
model_name: "neuralmagic/Qwen2-1.5B-Instruct-quantized.w8a8"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.593
- name: "exact_match,flexible-extract"
value: 0.588
limit: 1000
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size).
# bash ./run-lm-eval-gsm-vllm-baseline.sh -m Qwen/Qwen2-57B-A14B-Instruct -b "auto" -l 250 -f 5 -t 4
model_name: "Qwen/Qwen2-57B-A14B-Instruct"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.792
- name: "exact_match,flexible-extract"
value: 0.824
limit: 250
num_fewshot: 5

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# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m Qwen/Qwen2.5-1.5B-Instruct -b auto -l 1319 -f 5 -t 1
model_name: "Qwen/Qwen2.5-1.5B-Instruct"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.54
- name: "exact_match,flexible-extract"
value: 0.59
limit: 1319
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size)
# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic -l 1319 -t 1
model_name: "RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.47
- name: "exact_match,flexible-extract"
value: 0.64
limit: 1319
num_fewshot: 5

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# For vllm script, with -t option (tensor parallel size).
# bash .buildkite/lm-eval-harness/run-lm-eval-chartqa-vllm-vlm-baseline.sh -m Qwen/Qwen2.5-VL-7B-Instruct -l 2500 -t 1
model_name: "Qwen/Qwen2.5-VL-7B-Instruct"
backend: "vllm-vlm"
tasks:
- name: "chartqa"
metrics:
- name: "relaxed_accuracy,none"
value: 0.855
limit: 2500
num_fewshot: 0

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model_name: "Qwen/Qwen3-235B-A22B-Instruct-2507-FP8"
tasks:
- name: "mmlu_pro"
metrics:
- name: "exact_match,custom-extract"
value: 0.82
limit: 250 # will run on 250 * 14 subjects = 3500 samples
num_fewshot: 5
enforce_eager: false # we use false to speed up the eval process
kv_cache_dtype: fp8 # we use fp8 to speed up the eval process
max_model_len: 40960
apply_chat_template: true
fewshot_as_multiturn: true
gen_kwargs: "temperature=0,top_p=1,top_k=0,max_gen_toks=5632,until=<|ENDANSWER|>"

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# For vllm script, with -t option (tensor parallel size).
# bash ./run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM -b "auto" -t 2
model_name: "nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM"
tasks:
- name: "gsm8k"
metrics:
- name: "exact_match,strict-match"
value: 0.6353
- name: "exact_match,flexible-extract"
value: 0.637
limit: null
num_fewshot: null

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@@ -0,0 +1,2 @@
Qwen3-235B-A22B-Instruct-2507-FP8.yaml
NVIDIA-Nemotron-3-Nano-30B-A3B-FP8.yaml

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@@ -0,0 +1 @@
Meta-Llama-4-Maverick-17B-128E-Instruct-FP8.yaml

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@@ -0,0 +1,6 @@
Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform.yaml
Meta-Llama-3-70B-Instruct.yaml
Mixtral-8x7B-Instruct-v0.1.yaml
Qwen2-57B-A14-Instruct.yaml
DeepSeek-V2-Lite-Chat.yaml
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.yaml

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@@ -0,0 +1 @@
Meta-Llama-4-Maverick-17B-128E-Instruct-FP8-MM.yaml

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@@ -0,0 +1 @@
Qwen2.5-VL-7B-Instruct.yaml

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@@ -0,0 +1,5 @@
Qwen2.5-1.5B-Instruct.yaml
Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml
Qwen1.5-MoE-W4A16-compressed-tensors.yaml

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@@ -0,0 +1,6 @@
Qwen2.5-1.5B-Instruct.yaml
Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml
Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
Qwen2.5-VL-3B-Instruct-FP8-dynamic.yaml
Qwen1.5-MoE-W4A16-compressed-tensors.yaml