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The LinearAttention output of tilelang are inconsistent with the FLA baseline #975

@cailun01

Description

@cailun01

Hi, TileLang team!

I run examples/linear_attention/example_linear_attn_fwd.py and examples/linear_attention/example_linear_attn_bwd.py and got:

Failed some tests!❌
Triton latency: 3.718 ms
TileLang latency: 2.905 ms
Speedup: 1.280x
Failed some tests!❌
Triton latency: 11.189 ms
TileLang latency: 8.127 ms
Speedup: 1.377x

torch.allclose(o, o_ref) in example_linear_attn_fwd.py and torch.allclose(dq, q.grad), torch.allclose(dk, k.grad), torch.allclose(dv, v.grad) in example_linear_attn_bwd.py return False.

Could you please share some troubleshooting ideas?

Testbed setup

I built tilelang from source by method 3

tilelang version: 8fe3540
flash-linear-attention version: 195b74db140bada1ebf5a7bac8b11af0faf4ca59

PyTorch version: 2.8.0a0+5228986c39.nv25.06
Is debug build: False
CUDA used to build PyTorch: 12.9
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.2 LTS (aarch64)
GCC version: (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version: Could not collect
CMake version: version 4.0.3
Libc version: glibc-2.39

Python version: 3.12.3 (main, Feb  4 2025, 14:48:35) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-6.6.47-002.ant8.aarch64-aarch64-with-glibc2.39
Is CUDA available: True
CUDA runtime version: 12.9.86
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GB200
GPU 1: NVIDIA GB200
GPU 2: NVIDIA GB200
GPU 3: NVIDIA GB200

Nvidia driver version: 580.65.06
cuDNN version: Probably one of the following:
/usr/lib/aarch64-linux-gnu/libcudnn.so.9.10.2
/usr/lib/aarch64-linux-gnu/libcudnn_adv.so.9.10.2
/usr/lib/aarch64-linux-gnu/libcudnn_cnn.so.9.10.2
/usr/lib/aarch64-linux-gnu/libcudnn_engines_precompiled.so.9.10.2
/usr/lib/aarch64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.10.2
/usr/lib/aarch64-linux-gnu/libcudnn_graph.so.9.10.2
/usr/lib/aarch64-linux-gnu/libcudnn_heuristic.so.9.10.2
/usr/lib/aarch64-linux-gnu/libcudnn_ops.so.9.10.2
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                         aarch64
CPU op-mode(s):                       64-bit
Byte Order:                           Little Endian
CPU(s):                               144
On-line CPU(s) list:                  0-143
Vendor ID:                            ARM
Model name:                           Neoverse-V2
Model:                                0
Thread(s) per core:                   1
Core(s) per cluster:                  72
Socket(s):                            -
Cluster(s):                           2
Stepping:                             r0p0
Frequency boost:                      disabled
CPU(s) scaling MHz:                   95%
CPU max MHz:                          3411.0000
CPU min MHz:                          81.0000
BogoMIPS:                             2000.00
Flags:                                fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm sb dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh
L1d cache:                            9 MiB (144 instances)
L1i cache:                            9 MiB (144 instances)
L2 cache:                             144 MiB (144 instances)
L3 cache:                             228 MiB (2 instances)
NUMA node(s):                         34
NUMA node0 CPU(s):                    0-71
NUMA node1 CPU(s):                    72-143
NUMA node2 CPU(s):                    
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NUMA node33 CPU(s):                   
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:   Not affected
Vulnerability Spec store bypass:      Not affected
Vulnerability Spectre v1:             Mitigation; __user pointer sanitization
Vulnerability Spectre v2:             Not affected
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] flake8==7.3.0
[pip3] mypy_extensions==1.1.0
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.11.0.98
[pip3] nvidia-cudnn-frontend==1.12.0
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-nccl-cu12==2.27.6
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] nvtx==0.2.11
[pip3] onnx==1.17.0
[pip3] onnx-ir==0.1.4
[pip3] onnxscript==0.3.1
[pip3] optree==0.16.0
[pip3] pynvjitlink==0.3.0
[pip3] pytorch-triton==3.3.0+git96316ce52.nvinternal
[pip3] torch==2.8.0a0+5228986c39.nv25.6
[pip3] torch_tensorrt==2.8.0a0
[pip3] torchao==0.11.0+git
[pip3] torchprofile==0.0.4
[pip3] torchvision==0.22.0a0+95f10a4e

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