Llama 3.2 MM Multiturn Browser: Second message errors out #1224
Labels
Browser
Issue Related to UI components and behavior
bug
Something isn't working
Llama 3.2- Multimodal
Issues related to Multimodal of Llama3.2
🐛 Describe the bug
Kick off a server (tested on CPU)
python3 torchchat.py server llama3.2-11B
In a separate terminal open the browser:
streamlit run torchchat/usages/browser.py
First send a message with an image.
When that completes
Error when sending the second message:
Versions
Collecting environment information...
PyTorch version: 2.5.0.dev20240901+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: CentOS Stream 9 (x86_64)
GCC version: (GCC) 11.5.0 20240719 (Red Hat 11.5.0-2)
Clang version: Could not collect
CMake version: version 3.30.3
Libc version: glibc-2.34
Python version: 3.10.0 (default, Mar 3 2022, 09:58:08) [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-6.4.3-0_fbk12_hardened_2624_g7d95a0297d81-x86_64-with-glibc2.34
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA PG509-210
Nvidia driver version: 550.90.07
cuDNN version: Probably one of the following:
/usr/lib64/libcudnn.so.8.9.4
/usr/lib64/libcudnn_adv_infer.so.8.9.4
/usr/lib64/libcudnn_adv_train.so.8.9.4
/usr/lib64/libcudnn_cnn_infer.so.8.9.4
/usr/lib64/libcudnn_cnn_train.so.8.9.4
/usr/lib64/libcudnn_ops_infer.so.8.9.4
/usr/lib64/libcudnn_ops_train.so.8.9.4
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): 22
On-line CPU(s) list: 0-21
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8339HC CPU @ 1.80GHz
CPU family: 6
Model: 85
Thread(s) per core: 1
Core(s) per socket: 22
Socket(s): 1
Stepping: 11
BogoMIPS: 3591.57
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology cpuid tsc_known_freq pni pclmulqdq vmx ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_bf16 arat vnmi umip pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Virtualization: VT-x
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 704 KiB (22 instances)
L1i cache: 704 KiB (22 instances)
L2 cache: 88 MiB (22 instances)
L3 cache: 16 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-21
Vulnerability Gather data sampling: Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Vulnerable
Vulnerability Retbleed: Vulnerable
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2: Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Vulnerable
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; TSX disabled
Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] pytorch-triton==3.0.0+dedb7bdf33
[pip3] torch==2.5.0.dev20240901+cu121
[pip3] torchao==0.5.0
[pip3] torchtune==0.0.0
[pip3] torchvision==0.20.0.dev20240901+cu121
[conda] numpy 1.26.4 pypi_0 pypi
[conda] pytorch-triton 3.0.0+dedb7bdf33 pypi_0 pypi
[conda] torch 2.5.0.dev20240901+cu121 pypi_0 pypi
[conda] torchao 0.5.0 pypi_0 pypi
[conda] torchtune 0.0.0 pypi_0 pypi
[conda] torchvision 0.20.0.dev20240901+cu121 pypi_0 pypi
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