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I am running Ubuntu 20.04.2 with all updates:
$ uname -a Linux bengt-desktop 5.11.0-37-generic #41~20.04.2-Ubuntu SMP Fri Sep 24 09:06:38 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
I am running a Vega 64 on a Threadripper 1950X with ROCm 4.3.1:
$ rocminfo ROCk module is loaded ===================== 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 ========== HSA Agents ========== ******* Agent 1 ******* Name: AMD Ryzen Threadripper 1950X 16-Core Processor Uuid: CPU-XX Marketing Name: AMD Ryzen Threadripper 1950X 16-Core Processor 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: L1: 32768(0x8000) KB Chip ID: 0(0x0) Cacheline Size: 64(0x40) Max Clock Freq. (MHz): 3900 BDFID: 0 Internal Node ID: 0 Compute Unit: 32 SIMDs per CU: 0 Shader Engines: 0 Shader Arrs. per Eng.: 0 WatchPts on Addr. Ranges:1 Features: None Pool Info: Pool 1 Segment: GLOBAL; FLAGS: FINE GRAINED Size: 65711880(0x3eaaf08) KB Allocatable: TRUE Alloc Granule: 4KB Alloc Alignment: 4KB Accessible by all: TRUE Pool 2 Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED Size: 65711880(0x3eaaf08) KB Allocatable: TRUE Alloc Granule: 4KB Alloc Alignment: 4KB Accessible by all: TRUE Pool 3 Segment: GLOBAL; FLAGS: COARSE GRAINED Size: 65711880(0x3eaaf08) KB Allocatable: TRUE Alloc Granule: 4KB Alloc Alignment: 4KB Accessible by all: TRUE ISA Info: ******* Agent 2 ******* Name: gfx900 Uuid: GPU-02151de3936c4944 Marketing Name: Vega 10 XL/XT [Radeon RX Vega 56/64] Vendor Name: AMD Feature: KERNEL_DISPATCH Profile: BASE_PROFILE Float Round Mode: NEAR Max Queue Number: 128(0x80) Queue Min Size: 4096(0x1000) Queue Max Size: 131072(0x20000) Queue Type: MULTI Node: 1 Device Type: GPU Cache Info: L1: 16(0x10) KB L2: 4096(0x1000) KB Chip ID: 26751(0x687f) Cacheline Size: 64(0x40) Max Clock Freq. (MHz): 1630 BDFID: 17664 Internal Node ID: 1 Compute Unit: 64 SIMDs per CU: 4 Shader Engines: 4 Shader Arrs. per Eng.: 1 WatchPts on Addr. Ranges:4 Features: KERNEL_DISPATCH Fast F16 Operation: FALSE Wavefront Size: 64(0x40) Workgroup Max Size: 1024(0x400) Workgroup Max Size per Dimension: x 1024(0x400) y 1024(0x400) z 1024(0x400) Max Waves Per CU: 40(0x28) Max Work-item Per CU: 2560(0xa00) Grid Max Size: 4294967295(0xffffffff) Grid Max Size per Dimension: x 4294967295(0xffffffff) y 4294967295(0xffffffff) z 4294967295(0xffffffff) Max fbarriers/Workgrp: 32 Pool Info: Pool 1 Segment: GLOBAL; FLAGS: COARSE GRAINED Size: 8372224(0x7fc000) KB Allocatable: TRUE Alloc Granule: 4KB Alloc Alignment: 4KB Accessible by all: FALSE Pool 2 Segment: GROUP Size: 64(0x40) KB Allocatable: FALSE Alloc Granule: 0KB Alloc Alignment: 0KB Accessible by all: FALSE ISA Info: ISA 1 Name: amdgcn-amd-amdhsa--gfx900:xnack- 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 ***
I set up a virtual environment something like this:
$ python3.8 -m venv venv $ venv/bin/python -m pip install --upgrade torch torchvision==0.10.1 -f https://download.pytorch.org/whl/rocm4.2/torch_stable.html $ venv/bin/python -m pip install --upgrade pandas psutil
This left me with an environment like so:
$ venv/bin/python -m pip freeze --all numpy==1.21.2 pandas==1.3.3 Pillow==8.3.2 pip==20.0.2 pkg-resources==0.0.0 psutil==5.8.0 python-dateutil==2.8.2 pytz==2021.3 setuptools==44.0.0 six==1.16.0 torch==1.9.1+rocm4.2 torchvision==0.10.1+rocm4.2 typing-extensions==3.10.0.2
Now, the benchmark gives me these errors:
$ venv/bin/python benchmark_models.py -g 1 benchmark start : 2021/10/12 21:01:33 Number of GPUs on current device : 1 CUDA Version : None Cudnn Version : 2011000 Device Name : Vega 10 XL/XT [Radeon RX Vega 56/64] uname_result(system='Linux', node='bengt-desktop', release='5.11.0-37-generic', version='#41~20.04.2-Ubuntu SMP Fri Sep 24 09:06:38 UTC 2021', machine='x86_64', processor='x86_64') scpufreq(current=2320.7297500000004, min=2200.0, max=3900.0) cpu_count: 32 memory_available: 55991275520 Benchmarking Training float precision type mnasnet0_5 MIOpen(HIP): Warning [SQLiteBase] Unable to read system database file:/opt/rocm/miopen/share/miopen/db/gfx900_64.kdb Performance may degrade MIOpen(HIP): Error [SetIsaName] 'amd_comgr_action_info_set_isa_name(handle, isa.c_str())' amdgcn-amd-amdhsa--gfx900:sramecc-:xnack-: INVALID_ARGUMENT (2) MIOpen(HIP): Error [BuildOcl] comgr status = INVALID_ARGUMENT (2) MIOpen(HIP): Warning [BuildOcl] amdgcn-amd-amdhsa--gfx900:sramecc-:xnack- MIOpen Error: /MIOpen/src/hipoc/hipoc_program.cpp:286: Code object build failed. Source: MIOpenIm2d2Col.cl Traceback (most recent call last): File "benchmark_models.py", line 183, in <module> train_result = train(precision) File "benchmark_models.py", line 93, in train prediction = model(img.to("cuda")) File "/home/bengt/Downloads/Projekte/github.com/ryujaehun/pytorch-gpu-benchmark/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(*input, **kwargs) File "/home/bengt/Downloads/Projekte/github.com/ryujaehun/pytorch-gpu-benchmark/venv/lib/python3.8/site-packages/torchvision/models/mnasnet.py", line 148, in forward x = self.layers(x) File "/home/bengt/Downloads/Projekte/github.com/ryujaehun/pytorch-gpu-benchmark/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(*input, **kwargs) File "/home/bengt/Downloads/Projekte/github.com/ryujaehun/pytorch-gpu-benchmark/venv/lib/python3.8/site-packages/torch/nn/modules/container.py", line 139, in forward input = module(input) File "/home/bengt/Downloads/Projekte/github.com/ryujaehun/pytorch-gpu-benchmark/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(*input, **kwargs) File "/home/bengt/Downloads/Projekte/github.com/ryujaehun/pytorch-gpu-benchmark/venv/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 443, in forward return self._conv_forward(input, self.weight, self.bias) File "/home/bengt/Downloads/Projekte/github.com/ryujaehun/pytorch-gpu-benchmark/venv/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 439, in _conv_forward return F.conv2d(input, weight, bias, self.stride, RuntimeError: miopenStatusUnknownError
Any idea what to do about that?
The text was updated successfully, but these errors were encountered:
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I am running Ubuntu 20.04.2 with all updates:
I am running a Vega 64 on a Threadripper 1950X with ROCm 4.3.1:
I set up a virtual environment something like this:
This left me with an environment like so:
Now, the benchmark gives me these errors:
Any idea what to do about that?
The text was updated successfully, but these errors were encountered: