[Bug]: Paligemma does not work with tensor parallelism

[siddvenk]

Your current environment

Collecting environment information...
PyTorch version: 2.3.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
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 3.30.1
Libc version: glibc-2.35

Python version: 3.10.12 (main, Mar 22 2024, 16:50:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.5.0-1022-aws-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA L4
GPU 1: NVIDIA L4
GPU 2: NVIDIA L4
GPU 3: NVIDIA L4

Nvidia driver version: 535.183.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.1.0
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):                             48
On-line CPU(s) list:                0-47
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 7R13 Processor
CPU family:                         25
Model:                              1
Thread(s) per core:                 2
Core(s) per socket:                 24
Socket(s):                          1
Stepping:                           1
BogoMIPS:                           5300.00
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 tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          768 KiB (24 instances)
L1i cache:                          768 KiB (24 instances)
L2 cache:                           12 MiB (24 instances)
L3 cache:                           96 MiB (3 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 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] flashinfer==0.0.9+cu121torch2.3
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] onnx==1.16.1
[pip3] onnxruntime-gpu==1.18.0
[pip3] sentence-transformers==3.0.1
[pip3] torch==2.3.1+cu121
[pip3] torchvision==0.18.1+cu121
[pip3] transformers==4.43.2
[pip3] triton==2.3.1
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.3.post1
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 	SYS	0-47	0		N/A
GPU3	SYS	SYS	SYS	 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

Model: google/paligemma-3b-mix-448

Issue: There is an issue during the warmup phase when using tensor parallelism.

Reproducible example:

from vllm import LLM

llm = LLM(model="google/paligemma-3b-mix-448", tensor_parallel_size=2)

Exception:

INFO 07-29 19:25:16 model_runner.py:692] Loading model weights took 3.2067 GB
(VllmWorkerProcess pid=161) INFO 07-29 19:25:18 model_runner.py:692] Loading model weights took 3.2067 GB
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226] Exception in worker VllmWorkerProcess while processing method determine_num_available_blocks: shape mismatch: value tensor of shape [8192, 1024] cannot be broadcast to indexing result of shape [8192, 2048], Traceback (most recent call last):
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_worker_utils.py", line 223, in _run_worker_process
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]     output = executor(*args, **kwargs)
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]   File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]     return func(*args, **kwargs)
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 179, in determine_num_available_blocks
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]     self.model_runner.profile_run()
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]   File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]     return func(*args, **kwargs)
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 896, in profile_run
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]     self.execute_model(model_input, kv_caches, intermediate_tensors)
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]   File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]     return func(*args, **kwargs)
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 1314, in execute_model
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]     hidden_or_intermediate_states = model_executable(
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]     return self._call_impl(*args, **kwargs)
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]   File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]     return forward_call(*args, **kwargs)
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/paligemma.py", line 259, in forward
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]     inputs_embeds = merge_vision_embeddings(
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]   File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/utils.py", line 33, in merge_vision_embeddings
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226]     inputs_embeds[mask] = vision_embeddings.view(total_tokens, embed_dim)
(VllmWorkerProcess pid=161) ERROR 07-29 19:25:19 multiproc_worker_utils.py:226] RuntimeError: shape mismatch: value tensor of shape [8192, 1024] cannot be broadcast to indexing result of shape [8192, 2048]

This model works when not using tensor parallelism, but any level of tensor parallelism causes the issue above.

[lanking520]

@ywang96

[ywang96]

Hey @siddvenk Thanks for reporting this issue! I will look into this.

Just to confirm: The same model works with TP=1 but not TP=2, correct? (I can verify this later myself too was just wondering if I can get a quick answer)

[lanking520]

@ywang96 we tested on TP=4 and doesn't work. We didn't test on TP2

[ywang96]

@ywang96 we tested on TP=4 and doesn't work. We didn't test on TP2

Got it - there might have been some changes in distributed ops that I wasn't aware of - I will look into this and patch a fix. Sorry for the trouble!

[siddvenk]

@ywang96 I also tested with TP=2 and it doesn't work. It's the same error as with TP=4, just different numbers in the broadcast exception

[ywang96]

Hey @lanking520 @siddvenk! This bug was due to an oversight of us using a TP-sharded layer in the multimodal projector when we haven't supported sharding it, and should be fixed by #6930. It wasn't caught previously because we don't have a TP test for VLMs but we should definitely add one when we have more credits for CI. Sorry for the inconvenience!

[siddvenk]

Appreciate the quick investigation and fix here!