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[Installation]: I tried compile GFX1100 on WSL2 but it does not seems work #780

Description

@sorasoras

Your current environment

The output of `python env.py`

python env.py

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.1.2 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last): File "/home/sora/aphrodite-engine/env.py", line 17, in
import torch
File "/usr/local/lib/python3.10/dist-packages/torch/init.py", line 1382, in
from .functional import * # noqa: F403
File "/usr/local/lib/python3.10/dist-packages/torch/functional.py", line 7, in
import torch.nn.functional as F
File "/usr/local/lib/python3.10/dist-packages/torch/nn/init.py", line 1, in
from .modules import * # noqa: F403
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/init.py", line 35, in
from .transformer import TransformerEncoder, TransformerDecoder,
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/transformer.py", line 20, in
device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'),
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/transformer.py:20: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at /pytorch/torch/csrc/utils/tensor_n umpy.cpp:84.)
device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'),
Collecting environment information...
/usr/local/lib/python3.10/dist-packages/torch/cuda/init.py:611: UserWarning: Can't initialize NVML
warnings.warn("Can't initialize NVML")
PyTorch version: 2.1.2+rocm6.1.3
Is debug build: False
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: 6.1.40093-bd86f1708

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.30.4
Libc version: glibc-2.35

Python version: 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.5.119
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: AMD Radeon RX 7900 XTXNoGCNArchNameOnOldPyTorch
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: 6.1.40093
MIOpen runtime version: 3.1.0
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): 32
On-line CPU(s) list: 0-31
Vendor ID: AuthenticAMD
Model name: AMD Ryzen 9 7950X3D 16-Core Processor
CPU family: 25
Model: 97
Thread(s) per core: 2
Core(s) per socket: 16
Socket(s): 1
Stepping: 2
BogoMIPS: 8399.84
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 re p_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misal ignsse 3dnowprefetch osvw topoext perfctr_core ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx 512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_bf16 clzero xsaveerptr arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm
Virtualization: AMD-V
Hypervisor vendor: Microsoft
Virtualization type: full
L1d cache: 512 KiB (16 instances)
L1i cache: 512 KiB (16 instances)
L2 cache: 16 MiB (16 instances)
L3 cache: 96 MiB (1 instance)
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: Mitigation; safe RET
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; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] numpy==2.1.2
[pip3] pytorch-triton-rocm==2.1.0+rocm6.1.3.4d510c3a44
[pip3] torch==2.1.2+rocm6.1.3
[pip3] torchvision==0.16.1+rocm6.1.3
[conda] Could not collect
ROCM Version: 6.1.40093-bd86f1708
Neuron SDK Version: N/A
Aphrodite Version: N/A
Aphrodite Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
Could not collect
root@SORANET:/home/sora/aphrodite-engine# sudo python env.py

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.1.2 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last): File "/home/sora/aphrodite-engine/env.py", line 17, in
import torch
File "/usr/local/lib/python3.10/dist-packages/torch/init.py", line 1382, in
from .functional import * # noqa: F403
File "/usr/local/lib/python3.10/dist-packages/torch/functional.py", line 7, in
import torch.nn.functional as F
File "/usr/local/lib/python3.10/dist-packages/torch/nn/init.py", line 1, in
from .modules import * # noqa: F403
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/init.py", line 35, in
from .transformer import TransformerEncoder, TransformerDecoder,
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/transformer.py", line 20, in
device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'),
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/transformer.py:20: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:84.)
device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'),
Collecting environment information...
/usr/local/lib/python3.10/dist-packages/torch/cuda/init.py:611: UserWarning: Can't initialize NVML
warnings.warn("Can't initialize NVML")
PyTorch version: 2.1.2+rocm6.1.3
Is debug build: False
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: 6.1.40093-bd86f1708

OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.30.4
Libc version: glibc-2.35

Python version: 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.153.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.5.119
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: AMD Radeon RX 7900 XTXNoGCNArchNameOnOldPyTorch
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: 6.1.40093
MIOpen runtime version: 3.1.0
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): 32
On-line CPU(s) list: 0-31
Vendor ID: AuthenticAMD
Model name: AMD Ryzen 9 7950X3D 16-Core Processor
CPU family: 25
Model: 97
Thread(s) per core: 2
Core(s) per socket: 16
Socket(s): 1
Stepping: 2
BogoMIPS: 8399.84
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 tsc_reliable nonstop_tsc cpuid extd_apicid pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_bf16 clzero xsaveerptr arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm
Virtualization: AMD-V
Hypervisor vendor: Microsoft
Virtualization type: full
L1d cache: 512 KiB (16 instances)
L1i cache: 512 KiB (16 instances)
L2 cache: 16 MiB (16 instances)
L3 cache: 96 MiB (1 instance)
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: Mitigation; safe RET
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; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] numpy==2.1.2
[pip3] pytorch-triton-rocm==2.1.0+rocm6.1.3.4d510c3a44
[pip3] torch==2.1.2+rocm6.1.3
[pip3] torchvision==0.16.1+rocm6.1.3
[conda] Could not collect
ROCM Version: 6.1.40093-bd86f1708
Neuron SDK Version: N/A
Aphrodite Version: N/A
Aphrodite Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
Could not collect

How did you install Aphrodite?

pip install aphrodite-engine

sudo apt update
wget https://repo.radeon.com/amdgpu-install/6.1.3/ubuntu/jammy/amdgpu-install_6.1.60103-1_all.deb
sudo apt install ./amdgpu-install_6.1.60103-1_all.deb

sudo amdgpu-install --list-usecase

If --usecase option is not present, the default selection is
"dkms,graphics,opencl,hip"
Available use cases:
dkms (to only install the kernel mode driver)

  • Kernel mode driver (included in all usecases)
    graphics (for users of graphics applications)
  • Open source Mesa 3D graphics and multimedia libraries
    multimedia (for users of open source multimedia)
  • Open source Mesa 3D multimedia libraries
    multimediasdk (for developers of open source multimedia)
  • Open source Mesa 3D multimedia libraries
  • Development headers for multimedia libraries
    workstation (for users of legacy WS applications)
  • Open source multimedia libraries
  • Closed source (legacy) OpenGL
    rocm (for users and developers requiring full ROCm stack)
  • OpenCL (ROCr/KFD based) runtime
  • HIP runtimes
  • Machine learning framework
  • All ROCm libraries and applications
    wsl (for using ROCm in a WSL context)
  • ROCr WSL runtime library (Ubuntu 22.04 only)
    rocmdev (for developers requiring ROCm runtime and
    profiling/debugging tools)
  • HIP runtimes
  • OpenCL runtime
  • Profiler, Tracer and Debugger tools
    rocmdevtools (for developers requiring ROCm profiling/debugging tools)
  • Profiler, Tracer and Debugger tools
    amf (for users of AMF based multimedia)
  • AMF closed source multimedia library
    lrt (for users of applications requiring ROCm runtime)
  • ROCm Compiler and device libraries
  • ROCr runtime and thunk
    opencl (for users of applications requiring OpenCL on Vega or later
    products)
  • ROCr based OpenCL
  • ROCm Language runtime
    openclsdk (for application developers requiring ROCr based OpenCL)
  • ROCr based OpenCL
  • ROCm Language runtime
  • development and SDK files for ROCr based OpenCL
    hip (for users of HIP runtime on AMD products)
  • HIP runtimes
    hiplibsdk (for application developers requiring HIP on AMD products)
  • HIP runtimes
  • ROCm math libraries
  • HIP development libraries
    openmpsdk (for users of openmp/flang on AMD products)
  • OpenMP runtime and devel packages
    mllib (for users executing machine learning workloads)
  • MIOpen hip/tensile libraries
  • Clang OpenCL
  • MIOpen kernels
    mlsdk (for developers executing machine learning workloads)
  • MIOpen development libraries
  • Clang OpenCL development libraries
  • MIOpen kernels
    asan (for users of ASAN enabled ROCm packages)
  • ASAN enabled OpenCL (ROCr/KFD based) runtime
  • ASAN enabled HIP runtimes
  • ASAN enabled Machine learning framework
  • ASAN enabled ROCm libraries

rocminfo

HSA System Attributes

Runtime Version: 1.1
System Timestamp Freq.: 1000.000000MHz
Sig. Max Wait Duration: 18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count)
Machine Model: LARGE
System Endianness: LITTLE
Mwaitx: DISABLED
DMAbuf Support: NO

==========
HSA Agents


Agent 1


Name: CPU
Uuid: CPU-XX
Marketing Name: CPU
Vendor Name: CPU
Feature: None specified
Profile: FULL_PROFILE
Float Round Mode: NEAR
Max Queue Number: 0(0x0)
Queue Min Size: 0(0x0)
Queue Max Size: 0(0x0)
Queue Type: MULTI
Node: 0
Device Type: CPU
Cache Info:
Chip ID: 0(0x0)
Cacheline Size: 64(0x40)
Internal Node ID: 0
Compute Unit: 32
SIMDs per CU: 0
Shader Engines: 0
Shader Arrs. per Eng.: 0
Features: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED
Size: 49137460(0x2edc734) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
Pool 2
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 49137460(0x2edc734) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
ISA Info:


Agent 2


Name: gfx1100
Marketing Name: AMD Radeon RX 7900 XTX
Vendor Name: AMD
Feature: KERNEL_DISPATCH
Profile: BASE_PROFILE
Float Round Mode: NEAR
Max Queue Number: 16(0x10)
Queue Min Size: 4096(0x1000)
Queue Max Size: 131072(0x20000)
Queue Type: MULTI
Node: 1
Device Type: GPU
Cache Info:
L1: 32(0x20) KB
L2: 6144(0x1800) KB
L3: 98304(0x18000) KB
Chip ID: 29772(0x744c)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 2526
Internal Node ID: 1
Compute Unit: 96
SIMDs per CU: 2
Shader Engines: 6
Shader Arrs. per Eng.: 2
Coherent Host Access: FALSE
Features: KERNEL_DISPATCH
Fast F16 Operation: TRUE
Wavefront Size: 32(0x20)
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Max Waves Per CU: 32(0x20)
Max Work-item Per CU: 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
Max fbarriers/Workgrp: 32
Packet Processor uCode:: 2280
SDMA engine uCode:: 21
IOMMU Support:: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 25086124(0x17ec8ac) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:2048KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 2
Segment: GROUP
Size: 64(0x40) KB
Allocatable: FALSE
Alloc Granule: 0KB
Alloc Recommended Granule:0KB
Alloc Alignment: 0KB
Accessible by all: FALSE
ISA Info:
ISA 1
Name: amdgcn-amd-amdhsa--gfx1100
Machine Models: HSA_MACHINE_MODEL_LARGE
Profiles: HSA_PROFILE_BASE
Default Rounding Mode: NEAR
Default Rounding Mode: NEAR
Fast f16: TRUE
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
FBarrier Max Size: 32
*** Done ***

build log

rocm_gfx1100_wsl2.txt

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