[Usage]: How to check the corresponding functionality of operators in Llama-2-7b-hf?

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Your current environment

Collecting environment information... PyTorch version: 2.5.1+cu124 Is debug build: False CUDA used to build PyTorch: 12.4 ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.26.0 Libc version: glibc-2.35

Python version: 3.10.16 (main, Dec 11 2024, 16:24:50) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-125-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090 Nvidia driver version: 550.120 cuDNN version: Could not collect 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: 46 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 96 On-line CPU(s) list: 0-95 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Gold 6248R CPU @ 3.00GHz CPU family: 6 Model: 85 Thread(s) per core: 2 Core(s) per socket: 24 Socket(s): 2 Stepping: 7 CPU max MHz: 4000.0000 CPU min MHz: 1200.0000 BogoMIPS: 6000.00 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 1.5 MiB (48 instances) L1i cache: 1.5 MiB (48 instances) L2 cache: 48 MiB (48 instances) L3 cache: 71.5 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-23,48-71 NUMA node1 CPU(s): 24-47,72-95 Vulnerability Gather data sampling: Mitigation; Microcode Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Mitigation; Enhanced IBRS Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Mitigation; TSX disabled

Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] nvidia-cublas-cu12==12.4.5.8 [pip3] nvidia-cuda-cupti-cu12==12.4.127 [pip3] nvidia-cuda-nvrtc-cu12==12.4.127 [pip3] nvidia-cuda-runtime-cu12==12.4.127 [pip3] nvidia-cudnn-cu12==9.1.0.70 [pip3] nvidia-cufft-cu12==11.2.1.3 [pip3] nvidia-curand-cu12==10.3.5.147 [pip3] nvidia-cusolver-cu12==11.6.1.9 [pip3] nvidia-cusparse-cu12==12.3.1.170 [pip3] nvidia-ml-py==12.560.30 [pip3] nvidia-nccl-cu12==2.21.5 [pip3] nvidia-nvjitlink-cu12==12.4.127 [pip3] nvidia-nvtx-cu12==12.4.127 [pip3] pyzmq==26.2.0 [pip3] torch==2.5.1 [pip3] torchvision==0.20.1 [pip3] transformers==4.48.0 [pip3] triton==3.1.0 [conda] numpy 1.26.4 pypi_0 pypi [conda] nvidia-cublas-cu12 12.4.5.8 pypi_0 pypi [conda] nvidia-cuda-cupti-cu12 12.4.127 pypi_0 pypi [conda] nvidia-cuda-nvrtc-cu12 12.4.127 pypi_0 pypi [conda] nvidia-cuda-runtime-cu12 12.4.127 pypi_0 pypi [conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi [conda] nvidia-cufft-cu12 11.2.1.3 pypi_0 pypi [conda] nvidia-curand-cu12 10.3.5.147 pypi_0 pypi [conda] nvidia-cusolver-cu12 11.6.1.9 pypi_0 pypi [conda] nvidia-cusparse-cu12 12.3.1.170 pypi_0 pypi [conda] nvidia-ml-py 12.560.30 pypi_0 pypi [conda] nvidia-nccl-cu12 2.21.5 pypi_0 pypi [conda] nvidia-nvjitlink-cu12 12.4.127 pypi_0 pypi [conda] nvidia-nvtx-cu12 12.4.127 pypi_0 pypi [conda] pyzmq 26.2.0 pypi_0 pypi [conda] torch 2.5.1 pypi_0 pypi [conda] torchvision 0.20.1 pypi_0 pypi [conda] transformers 4.48.0 pypi_0 pypi [conda] triton 3.1.0 pypi_0 pypi ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.6.6.post2.dev188+gd14e98d9.d20250113 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 NIC0 NIC1 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NODE NODE 24-47,72-95 1 N/A NIC0 NODE X PIX NIC1 NODE PIX X

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

NIC Legend:

NIC0: mlx5_0 NIC1: mlx5_1

LD_LIBRARY_PATH=/workspace/mnt/storage/anaconda3/envs/vllm/lib/python3.10/site-packages/cv2/../../lib64: CUDA_MODULE_LOADING=LAZY

How would you like to use vllm

My vllm version is 0.6.6. This is my test.py file, I have already set up the source environment. How can I debug to see which low-level operators are being used, or is there a profiler method to capture which operators are used? Thanks you for your reply.

from vllm import LLM, SamplingParams

prompts = [
    "Hello, my name is",
    "The future of AI is",
    "The life is", 
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(model="/workspace/mnt/storage/[email protected]/xiangxin_infer_/vllm/Llama-2-7b-hf")

outputs = llm.generate(prompts, sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

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How would you like to use vllm

How should I proceed to check the corresponding functionality of operators in Llama-2-7b-hf, such as cpu_op: _C::fused_add_rms_norm - 64 times cpu_op: _C::rms_norm - 1 times cpu_op: _C::rotary_embedding - 32 times cpu_op: _C::silu_and_mul - 32 times cpu_op: _C_cache_ops::reshape_and_cache_flash - 32 times cpu_op: aten::_local_scalar_dense - 17 times and other operators?

import os
import time

from vllm import LLM, SamplingParams

# enable torch profiler, can also be set on cmd line
os.environ["VLLM_TORCH_PROFILER_DIR"] = "./vllm_profile"

# Sample prompts.
prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

if __name__ == "__main__":

    # Create an LLM.
    llm = LLM(model="facebook/opt-125m", tensor_parallel_size=1)

    llm.start_profile()

    # Generate texts from the prompts. The output is a list of RequestOutput
    # objects that contain the prompt, generated text, and other information.
    outputs = llm.generate(prompts, sampling_params)

    llm.stop_profile()

    # Print the outputs.
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

    # Add a buffer to wait for profiler in the background process
    # (in case MP is on) to finish writing profiling output.
    time.sleep(10)

Before submitting a new issue...

• [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.