Get cudaErrorIllegalAddress when running ctc decoder on H100

Ray-Leung commented 4 months ago

🐛 Describe the bug

I am getting this error when running the sample code form pytorch https://pytorch.org/audio/2.2.0/tutorials/asr_inference_with_cuda_ctc_decoder_tutorial.html?highlight=ctc But everything is okay when running on both 4090/3090 machine. What kind of issue do I run into on the H100 machine, but not on the 4090/3090?

Error Message

> File /__w/audio/audio/pytorch/audio/src/libtorchaudio/cuctc/src/ctc_prefix_decoder_kernel_v2.cu Line 880 cudaMemcpyAsync( select_seq_lens.data(), log_prob_struct->select_seq_lens, sizeof(int) * bxs, cudaMemcpyDeviceToHost, stream) returned cudaErrorIllegalAddress.

Source Code

import torch
import torchaudio

print(torch.__version__)
print(torchaudio.__version__)

import time
from pathlib import Path

# import IPython
import sentencepiece as spm
from torchaudio.models.decoder import cuda_ctc_decoder
from torchaudio.utils import download_asset

def download_asset_external(url, key):
    path = Path(torch.hub.get_dir()) / "torchaudio" / Path(key)
    if not path.exists():
        path.parent.mkdir(parents=True, exist_ok=True)
        torch.hub.download_url_to_file(url, path)
    return str(path)

device = torch.device("cuda", 0)
url_prefix = "https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-ctc-2022-12-01"
model_link = f"{url_prefix}/resolve/main/exp/cpu_jit.pt"
model_path = download_asset_external(model_link, "cuda_ctc_decoder/cpu_jit.pt")

speech_file = download_asset("tutorial-assets/ctc-decoding/1688-142285-0007.wav")
waveform, sample_rate = torchaudio.load(speech_file)
assert sample_rate == 16000
# IPython.display.Audio(speech_file)

bpe_link = f"{url_prefix}/resolve/main/data/lang_bpe_500/bpe.model"
bpe_path = download_asset_external(bpe_link, "cuda_ctc_decoder/bpe.model")

bpe_model = spm.SentencePieceProcessor()
bpe_model.load(bpe_path)
tokens = [bpe_model.id_to_piece(id) for id in range(bpe_model.get_piece_size())]
#print(tokens)

cuda_decoder = cuda_ctc_decoder(tokens, nbest=10, beam_size=10, blank_skip_threshold=0.95)

actual_transcript = "i really was very much afraid of showing him how much shocked i was at some parts of what he said"
actual_transcript = actual_transcript.split()

acoustic_model = torch.jit.load(model_path)
acoustic_model.to(device)
acoustic_model.eval()

waveform = waveform.to(device)

feat = torchaudio.compliance.kaldi.fbank(waveform, num_mel_bins=80, snip_edges=False)
feat = feat.unsqueeze(0)
feat_lens = torch.tensor(feat.size(1), device=device).unsqueeze(0)

encoder_out, encoder_out_lens = acoustic_model.encoder(feat, feat_lens)
nnet_output = acoustic_model.ctc_output(encoder_out)
log_prob = torch.nn.functional.log_softmax(nnet_output, -1)

print(f"The shape of log_prob: {log_prob.shape}, the shape of encoder_out_lens: {encoder_out_lens.shape}")

# Error occurs on cuda_decoder line
results = cuda_decoder(log_prob, encoder_out_lens.to(torch.int32)) 
beam_search_transcript = bpe_model.decode(results[0][0].tokens).lower()
beam_search_wer = torchaudio.functional.edit_distance(actual_transcript, beam_search_transcript.split()) / len(
    actual_transcript
)

print(f"Transcript: {beam_search_transcript}")
print(f"WER: {beam_search_wer}")

Versions

PyTorch version: 2.2.0+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.22.1 Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-5.15.0-92-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: 12.3.103 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA H100 PCIe GPU 1: NVIDIA H100 PCIe

Nvidia driver version: 545.23.08 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: 52 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 48 On-line CPU(s) list: 0-47 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Silver 4410Y CPU family: 6 Model: 143 Thread(s) per core: 2 Core(s) per socket: 12 Socket(s): 2 Stepping: 8 CPU max MHz: 3900.0000 CPU min MHz: 800.0000 BogoMIPS: 4000.00 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 tsc_known_freq pni pclmulqdq dtes64 monitor 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 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 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 rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities Virtualization: VT-x L1d cache: 1.1 MiB (24 instances) L1i cache: 768 KiB (24 instances) L2 cache: 48 MiB (24 instances) L3 cache: 60 MiB (2 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-11,24-35 NUMA node1 CPU(s): 12-23,36-47 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: 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 IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] torch==2.2.0+cu121 [pip3] torchaudio==2.2.0+cu121 [pip3] torchvision==0.17.0+cu121 [pip3] triton==2.2.0

cc @ptrblck

malfet commented 4 months ago

Moving to torchaudio

ab23z commented 1 month ago

Encounter the same issue.

pytorch / audio