[Bug]: enabling *prompt* logprobs support with multi-step scheduling (as distinct from decoded token logprobs) crashes vLLM #8198
Description
Your current environment
The output of `python collect_env.py`
Collecting environment information...
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 12.3.0-1ubuntu1~22.04) 12.3.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.29.5
Libc version: glibc-2.35
Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.8.0-40-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.5.82
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA RTX A4000
GPU 1: NVIDIA RTX A4000
GPU 2: NVIDIA RTX A4000
GPU 3: NVIDIA RTX A4000
Nvidia driver version: 555.42.06
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 43 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 48
On-line CPU(s) list: 0-47
Vendor ID: AuthenticAMD
Model name: AMD Ryzen Threadripper 3960X 24-Core Processor
CPU family: 23
Model: 49
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 1
Stepping: 0
Frequency boost: enabled
CPU max MHz: 4568.1641
CPU min MHz: 2200.0000
BogoMIPS: 7599.93
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sev sev_es
Virtualization: AMD-V
L1d cache: 768 KiB (24 instances)
L1i cache: 768 KiB (24 instances)
L2 cache: 12 MiB (24 instances)
L3 cache: 128 MiB (8 instances)
NUMA node(s): 1
NUMA node0 CPU(s): 0-47
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Mitigation; untrained return thunk; SMT enabled with STIBP protection
Vulnerability Spec rstack overflow: Mitigation; Safe RET
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] mypy==1.11.1
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-ml-py==12.555.43
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.5.40
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] onnx==1.14.1
[pip3] onnxruntime==1.18.1
[pip3] pyzmq==26.0.3
[pip3] sentence-transformers==3.0.1
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.43.4
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.0.0
[conda] No relevant packages
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.0@6f81d9d941997dfad3b1dde3e77984dedffd1022
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X SYS SYS SYS 0-47 0 N/A
GPU1 SYS X SYS SYS 0-47 0 N/A
GPU2 SYS SYS X PHB 0-47 0 N/A
GPU3 SYS SYS PHB X 0-47 0 N/A
Legend:
X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
PIX = Connection traversing at most a single PCIe bridge
NV# = Connection traversing a bonded set of # NVLinks
🐛 Describe the bug
#7652 added support for decode token logprobs and prompt logprobs with multi-step scheduling. However attempting to configure a non-None prompt logprobs setting causing vLLM to crash.
This is the test code (not present on the main branch, see PR branch c6f703d or link: https://github.com/neuralmagic/vllm/blob/c6f703d9ea084042cd5002e08a253b8e1f161cd7/tests/multi_step/test_correctness_llm.py#L113):
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("tp_size", [1])
@pytest.mark.parametrize("max_tokens", [5])
@pytest.mark.parametrize("enforce_eager", [True])
@pytest.mark.parametrize("num_scheduler_steps", NUM_SCHEDULER_STEPS)
@pytest.mark.parametrize("num_prompts", NUM_PROMPTS)
@pytest.mark.parametrize("num_logprobs,num_prompt_logprobs", [(5, 5)])
def test_multi_step_llm_w_prompt_logprobs(
vllm_runner,
example_prompts,
model: str,
dtype: str,
tp_size: int,
max_tokens: int,
enforce_eager: int,
num_scheduler_steps: int,
num_prompts: int,
num_logprobs: Optional[int],
num_prompt_logprobs: Optional[int],
) -> None:
"""Test vLLM engine with multi-step scheduling via sync LLM Engine.
Set up a vLLM engine instance w/ single-step scheduling as a ground-truth
reference.
Prompt them with the same example prompts.
Validate:
* Generated logprobs are all very close
Args:
hf_runner: HF transformers model runner fixture
vllm_runner: vLLM model runner fixture
example_prompts: test fixture providing example prompts
model: model under test (same for single- and multi-step engines)
dtype: tensor datatype for engine to utilize
tp_size: degree of tensor-parallelism
max_tokens: the maximum number of tokens to generate
enforce_eager
num_scheduler_steps: for multi-step scheduling, GPU-side steps per
GPU -> CPU output transfer
num_prompts: number of example prompts under test
num_logprobs: corresponds to the `logprobs` argument to the OpenAI
completions endpoint; `None` -> no logprobs
"""
prompts = example_prompts
if len(prompts) < num_prompts:
prompts = prompts * ((num_prompts // len(prompts)) + 1)
prompts = prompts[:num_prompts]
assert len(prompts) == num_prompts
with vllm_runner(
model,
dtype=dtype,
enforce_eager=enforce_eager,
gpu_memory_utilization=0.7,
tensor_parallel_size=tp_size,
use_v2_block_manager=True,
num_scheduler_steps=num_scheduler_steps,
) as vllm_model:
vllm_outputs = vllm_model.generate_greedy_logprobs(
prompts,
max_tokens,
num_logprobs,
num_prompt_logprobs=num_prompt_logprobs)
with vllm_runner(
model,
dtype=dtype,
enforce_eager=enforce_eager,
gpu_memory_utilization=0.7,
tensor_parallel_size=tp_size,
) as vllm_model:
single_step_vllm_outputs = vllm_model.generate_greedy_logprobs(
prompts,
max_tokens,
num_logprobs,
num_prompt_logprobs=num_prompt_logprobs)
check_logprobs_close(
outputs_0_lst=single_step_vllm_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)
Run the test:
pytest -s -v tests/multi_step/test_correctness_llm.py::test_multi_step_llm_w_prompt_logprobs[5-5-10-8-True-5-1-half-JackFram/llama-160m]
The resultant exception is:
def _pythonize_sampler_output(
model_input: StatefulModelInput,
output: SamplerOutput,
pinned_sampled_token_buffer: torch.Tensor,
sampled_token_ids: torch.Tensor,
logprobs_tensor: Optional[torch.Tensor],
cache: Optional[PythonizationCache],
) -> None:
""" This function is only called when the output tensors are ready.
See :class:`ModelOutput`.
Modifies `output.outputs` and `pinned_sampled_token_buffer` in-place,
adding a Pythonized output data structure
(:class:`CompletionSequenceGroupOutput`) for each :class:`SequenceGroup`.
Args:
model_input
output: sampler output
pinned_sampled_token_token_buffer: CPU-side pinned memory
(receives copy of
GPU-side token buffer.)
sampled_token_ids: GPU-side token buffer
logprobs_tensor: GPU-side tensor containing
logprobs computed during sampling
"""
assert model_input.frozen_model_input is not None
frozen_model_input = model_input.frozen_model_input
assert frozen_model_input.sampling_metadata is not None
# samples generation should have been skipped
assert not output.outputs
pinned_buffer = pinned_sampled_token_buffer[:model_input.num_queries]
# CPU GPU sync
> pinned_buffer = pinned_buffer.copy_(sampled_token_ids, non_blocking=False)
E RuntimeError: The size of tensor a (10) must match the size of tensor b (240) at non-singleton dimension 0
vllm/worker/multi_step_model_runner.py:632: RuntimeError
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