[Bug]: Broken outputs for large contexts if `max_model_len` is fixed.

Your current environment

The output of `python collect_env.py`

```text 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 22.04.5 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.26.4 Libc version: glibc-2.35 Python version: 3.10.12 (main, Jun 20 2023, 11:10:17) [GCC 11.3.0] (64-bit runtime) Python platform: Linux-5.15.0-69-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.1.66 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4090 GPU 1: NVIDIA GeForce RTX 4090 GPU 2: NVIDIA GeForce RTX 4090 GPU 3: NVIDIA GeForce RTX 4090 GPU 4: NVIDIA GeForce RTX 4090 GPU 5: NVIDIA GeForce RTX 4090 GPU 6: NVIDIA GeForce RTX 4090 GPU 7: NVIDIA GeForce RTX 4090 Nvidia driver version: 535.113.01 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: 48 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 128 On-line CPU(s) list: 0-127 Vendor ID: AuthenticAMD Model name: AMD EPYC 7T83 64-Core Processor CPU family: 25 Model: 1 Thread(s) per core: 1 Core(s) per socket: 64 Socket(s): 2 Stepping: 1 Frequency boost: disabled CPU max MHz: 3529.0520 CPU min MHz: 1500.0000 BogoMIPS: 4900.28 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 pcid 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 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves 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 v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm Virtualization: AMD-V L1d cache: 4 MiB (128 instances) L1i cache: 4 MiB (128 instances) L2 cache: 64 MiB (128 instances) L3 cache: 512 MiB (16 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-63 NUMA node1 CPU(s): 64-127 Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: 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; Retpolines, IBPB conditional, IBRS_FW, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] mypy==1.12.0 [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==12.560.30 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] nvidia-nvjitlink-cu12==12.6.77 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] pyzmq==26.2.0 [pip3] torch==2.4.0 [pip3] torchvision==0.19.0 [pip3] transformers==4.45.2 [pip3] triton==3.0.0 [conda] Could not collect ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: N/A (dev) vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: [4mGPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity GPU NUMA ID[0m GPU0 X NODE NODE NODE SYS SYS SYS SYS 0-63 0 N/A GPU1 NODE X NODE NODE SYS SYS SYS SYS 0-63 0 N/A GPU2 NODE NODE X NODE SYS SYS SYS SYS 0-63 0 N/A GPU3 NODE NODE NODE X SYS SYS SYS SYS 0-63 0 N/A GPU4 SYS SYS SYS SYS X NODE NODE NODE 64-127 1 N/A GPU5 SYS SYS SYS SYS NODE X NODE NODE 64-127 1 N/A GPU6 SYS SYS SYS SYS NODE NODE X NODE 64-127 1 N/A GPU7 SYS SYS SYS SYS NODE NODE NODE X 64-127 1 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 ```

Model Input Dumps

Model input is attached in model_input.txt

Model output: // TODO(" Ground_truth: navigationTarget<PhotoState, PhotoEffect, PhotoAction, PhotoViewModel>(

🐛 Describe the bug

I generted code using deepseek-ai/deepseek-coder-1.3b-base model with long context. I fix rather large max_model_len = 16600 and input sequence length is 16000, max_tokens = 100. In this case the generation is broken, does not correspond to ground truth at all (mainly generates "TODO"). The problem can be fixed by adding and setting other parameter max_seq_len_to_capture equal to max_model_len. There is no this issue if the context length is smaller - less than 8000 tokens.

I use the following arguments:

from vllm import LLM

    generation_engine = LLM(
        hf_model_path=deepseek-ai/deepseek-coder-1.3b-base,
        max_model_len=16600,
    )
   sampling_params = SamplingParams(
        temperature: 0.0
        max_tokens: 100
        min_tokens: 5
        stop: ["\n"])

I use version 0.6.3. GPU - single RTX 4090 (24Gb)

Is it a bug or I am using it wrong?

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vllm-project / vllm