Llava 1.5 poor output quality in iOS app

[jakmro]

🐛 Describe the bug

Description

I tried running Llava on iOS following this tutorial. However, the model performs poorly inside the app (see results below). When I run the model using the C++ runner with the same image, the results are accurate.

I used a Linux machine to export the model because I encountered this issue on my Mac.

Results

iOS app result

C++ runner result

What's on the image?I 00:00:26.959272 executorch:text_prefiller.cpp:53] Prefill token result numel(): 32064

The image features a large, happy-looking dog sitting in a lush green field. The dog appears to be enjoying the outdoors, possibly on a sunny day. The field is filled with green grass, providing a perfect setting for the dog to relax and take in the surroundings.</s>

Minimal Example

1. Follow this tutorial, to run the model inside the iOS simulator.
2. Build and run the model using c++ runner.
3. I've also used a different image, if you want to use the same one make sure to go inside ./.ci/scripts/test_llava.sh and change this line of code:

curl -o basketball.jpg https://upload.wikimedia.org/wikipedia/commons/7/73/Chicago_Bulls_and_New_Jersey_Nets%2C_March_28%2C_1991.jpg

to this one:

curl -o basketball.jpg https://upload.wikimedia.org/wikipedia/commons/1/18/Dog_Breeds.jpg

Versions

MacBook Pro 14

Collecting environment information...
PyTorch version: 2.7.0.dev20250131
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: macOS 15.1.1 (arm64)
GCC version: Could not collect
Clang version: 16.0.0 (clang-1600.0.26.6)
CMake version: version 3.31.6
Libc version: N/A

Python version: 3.10.0 (default, Mar 3 2022, 03:54:28) [Clang 12.0.0 ] (64-bit runtime)
Python platform: macOS-15.1.1-arm64-arm-64bit
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Apple M1 Pro

Versions of relevant libraries:
[pip3] executorch==0.6.0a0+e2201c5
[pip3] numpy==2.2.3
[pip3] torch==2.7.0.dev20250131
[pip3] torchao==0.10.0+git7d879462
[pip3] torchaudio==2.6.0.dev20250131
[pip3] torchsr==1.0.4
[pip3] torchvision==0.22.0.dev20250131
[conda] executorch 0.6.0a0+e2201c5 pypi_0 pypi
[conda] numpy 2.2.3 pypi_0 pypi
[conda] torch 2.7.0.dev20250131 pypi_0 pypi
[conda] torchao 0.10.0+git7d879462 pypi_0 pypi
[conda] torchaudio 2.6.0.dev20250131 pypi_0 pypi
[conda] torchsr 1.0.4 pypi_0 pypi
[conda] torchvision 0.22.0.dev20250131 pypi_0 pypi

Linux Machine

Collecting environment information...
PyTorch version: 2.7.0.dev20250131+cpu
Is debug build: False
CUDA used to build PyTorch: Could not collect
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 3.31.6
Libc version: glibc-2.35

Python version: 3.10.0 (default, Mar 3 2022, 09:58:08) [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-6.8.0-40-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090
Nvidia driver version: 535.183.01
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): 24
On-line CPU(s) list: 0-23
Vendor ID: AuthenticAMD
Model name: AMD Ryzen 9 5900X 12-Core Processor
CPU family: 25
Model: 33
Thread(s) per core: 2
Core(s) per socket: 12
Socket(s): 1
Stepping: 2
Frequency boost: enabled
CPU max MHz: 4950,1948
CPU min MHz: 2200,0000
BogoMIPS: 7385.70
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 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy 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 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 user_shstk clzero irperf xsaveerptr rdpru wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm debug_swap
L1d cache: 384 KiB (12 instances)
L1i cache: 384 KiB (12 instances)
L2 cache: 6 MiB (12 instances)
L3 cache: 64 MiB (2 instances)
NUMA node(s): 1
NUMA node0 CPU(s): 0-23
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: Not affected
Vulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode
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; IBRS_FW; 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] executorch==0.6.0a0+e2201c5
[pip3] numpy==2.2.3
[pip3] torch==2.7.0.dev20250131+cpu
[pip3] torchao==0.10.0+git7d879462
[pip3] torchaudio==2.6.0.dev20250131+cpu
[pip3] torchsr==1.0.4
[pip3] torchvision==0.22.0.dev20250131+cpu
[conda] executorch 0.6.0a0+e2201c5 pypi_0 pypi
[conda] numpy 2.2.3 pypi_0 pypi
[conda] torch 2.7.0.dev20250131+cpu pypi_0 pypi
[conda] torchao 0.10.0+git7d879462 pypi_0 pypi
[conda] torchaudio 2.6.0.dev20250131+cpu pypi_0 pypi
[conda] torchsr 1.0.4 pypi_0 pypi
[conda] torchvision 0.22.0.dev20250131+cpu pypi_0 pypi

[mergennachin]

How performant is it (e.g., token/s) when running the C++ runner on your computer?

Cc @shoumikhin , @larryliu0820

[jakmro]

I updated the issue title to avoid confusion with performance in terms of speed. My original concern was the quality of the model's output.

Here are the complete logs from the C++ runner.

(et_xnnpack) jakubmroz@jm executorch % cmake-out/examples/models/llava/llava_main \
    --model_path=llava.pte                 \
    --tokenizer_path=tokenizer.bin         \
    --image_path=image2.pt                  \
    --prompt="What's on the image?" \
    --seq_len=768                          \
    --temperature=0
I 00:00:00.003718 executorch:cpuinfo_utils.cpp:62] Reading file /sys/devices/soc0/image_version
I 00:00:00.003837 executorch:cpuinfo_utils.cpp:78] Failed to open midr file /sys/devices/soc0/image_version
I 00:00:00.003841 executorch:cpuinfo_utils.cpp:158] Number of efficient cores 4
I 00:00:00.003842 executorch:main.cpp:78] Resetting threadpool with num threads = 4
I 00:00:00.004730 executorch:multimodal_runner.h:49] Creating Multimodal LLM runner: model_path=llava.pte, tokenizer_path=tokenizer.bin
I 00:00:00.025559 executorch:main.cpp:112] image size(0): 3, size(1): 252, size(2): 336
I 00:00:08.649390 executorch:llava_runner.cpp:144] RSS after loading model: 0.000000 MiB (0 if unsupported)
I 00:00:11.367456 executorch:text_prefiller.cpp:53] Prefill token result numel(): 32064
I 00:00:24.638151 executorch:llava_runner.cpp:168] RSS after prompt and image prefill: 0.000000 MiB (0 if unsupported)
What's on the image?I 00:00:26.959272 executorch:text_prefiller.cpp:53] Prefill token result numel(): 32064

The image features a large, happy-looking dog sitting in a lush green field. The dog appears to be enjoying the outdoors, possibly on a sunny day. The field is filled with green grass, providing a perfect setting for the dog to relax and take in the surroundings.</s>
I 00:03:06.608756 executorch:text_token_generator.h:118] 
Reached to the end of generation
PyTorchObserver {"prompt_tokens":618,"generated_tokens":64,"model_load_start_ms":1741788656861,"model_load_end_ms":1741788665484,"inference_start_ms":1741788665484,"inference_end_ms":1741788843446,"prompt_eval_end_ms":1741788683794,"first_token_ms":1741788683794,"aggregate_sampling_time_ms":3,"SCALING_FACTOR_UNITS_PER_SECOND":1000}
I 00:03:06.608818 executorch:stats.h:110] 	Prompt Tokens: 618    Generated Tokens: 64
I 00:03:06.608819 executorch:stats.h:116] 	Model Load Time:		8.623000 (seconds)
I 00:03:06.608827 executorch:stats.h:126] 	Total inference time:		177.962000 (seconds)		 Rate: 	0.359627 (tokens/second)
I 00:03:06.608829 executorch:stats.h:134] 		Prompt evaluation:	18.310000 (seconds)		 Rate: 	33.752048 (tokens/second)
I 00:03:06.608831 executorch:stats.h:145] 		Generated 64 tokens:	159.652000 (seconds)		 Rate: 	0.400872 (tokens/second)
I 00:03:06.608833 executorch:stats.h:153] 	Time to first generated token:	18.310000 (seconds)
I 00:03:06.608834 executorch:stats.h:160] 	Sampling time over 682 tokens:	0.003000 (seconds)
I 00:03:06.608886 executorch:llava_runner.cpp:180] RSS after finishing text generation: 0.000000 MiB (0 if unsupported)

[mergennachin]

I see what you mean

• with C++ runner, it is saying "The image features a large, happy-looking dog sitting in a lush green field" which is correct
• with iOS, it is saying "the image, there's a view of the sky..." which is incorrect

So that means, there's something wrong with the iOS integration somewhere

Cc @shoumikhin - this might be on your plate.