6162e650b0
[BE] remove torch deploy - conditionals ( #158288 )
...
This PR is part of the work to deprecate torch::deploy in OSS. Effectively it does 3 things to get started.
1. Remove test_deploy_interaction as we no longer need to worry about this
2. Remove all torch._running_with_deploy checks and use the False path always (surfaced 1)
3. Remove `USE_DEPLOY` and switch to the default path always
Note: MyPy does fail on a bunch of things here as a bunch of older files are touched. It may be better to fix these things on a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158288
Approved by: https://github.com/albanD
2025-07-29 17:40:49 +00:00
f8fafdc7a6
Revert "[BE] remove torch deploy - conditionals ( #158288 )"
...
This reverts commit ab26d4fbeb5bc4b4e6ef1c37fbec9fab6e5a9edd.
Reverted https://github.com/pytorch/pytorch/pull/158288 on behalf of https://github.com/ZainRizvi due to Reverting as per offline discussion to fix internal breaks. @PaliC will reland this as a codev diff. Instructions here: https://fburl.com/fixing-ghfirst-reverts ([comment](https://github.com/pytorch/pytorch/pull/158288#issuecomment-3119037960 ))
2025-07-25 16:09:39 +00:00
ab26d4fbeb
[BE] remove torch deploy - conditionals ( #158288 )
...
This PR is part of the work to deprecate torch::deploy in OSS. Effectively it does 3 things to get started.
1. Remove test_deploy_interaction as we no longer need to worry about this
2. Remove all torch._running_with_deploy checks and use the False path always (surfaced 1)
3. Remove `USE_DEPLOY` and switch to the default path always
Note: MyPy does fail on a bunch of things here as a bunch of older files are touched. It may be better to fix these things on a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158288
Approved by: https://github.com/albanD
2025-07-23 20:27:28 +00:00
ee5a434f8c
Revert "[BE] remove torch deploy - conditionals ( #158288 )"
...
This reverts commit 1a4268b8113d5160d71225bab980f03c2318a0a4.
Reverted https://github.com/pytorch/pytorch/pull/158288 on behalf of https://github.com/ZainRizvi due to Sorry but this is breaking internally, see D78496147 for details. To validate your fixes internally, you can follow the instructions here: https://fburl.com/fixing-ghfirst-reverts ([comment](https://github.com/pytorch/pytorch/pull/158288#issuecomment-3099826158 ))
2025-07-21 23:17:39 +00:00
1a4268b811
[BE] remove torch deploy - conditionals ( #158288 )
...
This PR is part of the work to deprecate torch::deploy in OSS. Effectively it does 3 things to get started.
1. Remove test_deploy_interaction as we no longer need to worry about this
2. Remove all torch._running_with_deploy checks and use the False path always (surfaced 1)
3. Remove `USE_DEPLOY` and switch to the default path always
Note: MyPy does fail on a bunch of things here as a bunch of older files are touched. It may be better to fix these things on a separate PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158288
Approved by: https://github.com/albanD
2025-07-17 05:56:07 +00:00
c515385b0a
Add Intel GPU info collection to the collect env script ( #157351 )
...
https://github.com/pytorch/pytorch/pull/137846 was mistakenly closed. Reopen a PR to land the PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157351
Approved by: https://github.com/guangyey , https://github.com/malfet
2025-07-09 03:01:41 +00:00
d40aaa42ee
[BE][16/16] fix typos in torch/ (torch/utils/) ( #156606 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156606
Approved by: https://github.com/albanD
ghstack dependencies: #156318 , #156320 , #156602 , #156604
2025-07-02 22:55:29 +00:00
45c5a23237
Revert "Add Intel GPU info collection to the collect env script ( #137846 )"
...
This reverts commit 5264f8cd8d08272003298cdefe6bd60b1b8c80b4.
Reverted https://github.com/pytorch/pytorch/pull/137846 on behalf of https://github.com/malfet due to Just testing if it will fix PR time benchmarks signal ([comment](https://github.com/pytorch/pytorch/pull/137846#issuecomment-2963232606 ))
2025-06-11 15:18:47 +00:00
5264f8cd8d
Add Intel GPU info collection to the collect env script ( #137846 )
...
As title, add Intel GPU info collection to the collect env script
Output examples:
1. CPU on Windows
```
C:\Users\user\miniforge3\envs\py310\lib\site-packages\torch\_subclasses\functional_tensor.py:279: UserWarning: Failed to initialize NumPy: No module named 'numpy' (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\pytorch\torch\csrc\utils\tensor_numpy.cpp:81.)
cpu = _conversion_method_template(device=torch.device("cpu"))
Collecting environment information...
PyTorch version: 2.8.0.dev20250528+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: Microsoft Windows 11 Enterprise (10.0.22631 64-bit)
GCC version: Could not collect
Clang version: Could not collect
CMake version: Could not collect
Libc version: N/A
Python version: 3.10.17 | packaged by conda-forge | (main, Apr 10 2025, 22:06:35) [MSC v.1943 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.22631-SP0
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Name: 12th Gen Intel(R) Core(TM) i7-1270P
Manufacturer: GenuineIntel
Family: 198
Architecture: 9
ProcessorType: 3
DeviceID: CPU0
CurrentClockSpeed: 1711
MaxClockSpeed: 2200
L2CacheSize: 9216
L2CacheSpeed: None
Revision: None
Versions of relevant libraries:
[pip3] torch==2.8.0.dev20250528+cpu
[conda] torch 2.8.0.dev20250528+cpu pypi_0 pypi
```
2. XPU on Windows
```
Collecting environment information...
PyTorch version: 2.8.0a0+gitef6306e
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: Microsoft Windows 10 Pro (10.0.19045 64-bit)
GCC version: (GCC) 13.1.0
Clang version: Could not collect
CMake version: version 3.29.3
Libc version: N/A
Python version: 3.10.17 | packaged by conda-forge | (main, Apr 10 2025, 22:06:35) [MSC v.1943 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.19045-SP0
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: True
XPU used to build PyTorch: 20250101
Intel GPU driver version:
* 32.0.101.6795 (20250520000000.******+***)
Intel GPU models onboard:
* Intel(R) Arc(TM) A770 Graphics
Intel GPU models detected:
* [0] _XpuDeviceProperties(name='Intel(R) Arc(TM) A770 Graphics', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.33184', total_memory=15915MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=128, sub_group_sizes=[8 16 32], has_fp16=1, has_fp64=0, has_atomic64=1)
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
----------------------
Name: Intel(R) Xeon(R) Platinum 8260 CPU @ 2.40GHz
Manufacturer: GenuineIntel
Family: 179
Architecture: 9
ProcessorType: 3
DeviceID: CPU0
CurrentClockSpeed: 2401
MaxClockSpeed: 2401
L2CacheSize: 24576
L2CacheSpeed: None
Revision: 21767
----------------------
Name: Intel(R) Xeon(R) Platinum 8260 CPU @ 2.40GHz
Manufacturer: GenuineIntel
Family: 179
Architecture: 9
ProcessorType: 3
DeviceID: CPU1
CurrentClockSpeed: 2200
MaxClockSpeed: 2401
L2CacheSize: 24576
L2CacheSpeed: None
Revision: 21767
Versions of relevant libraries:
[pip3] intel_extension_for_pytorch==2.8.10+gitb3ea3a1
[pip3] numpy==2.1.2
[pip3] optree==0.13.1
[pip3] pytorch-triton-xpu==3.3.1+gitb0e26b73
[pip3] torch==2.8.0a0+gitef6306e
[conda] intel-extension-for-pytorch 2.8.10+gitb3ea3a1 pypi_0 pypi
[conda] mkl 2025.1.0 pypi_0 pypi
[conda] mkl-dpcpp 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-blas 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-datafitting 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-dft 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-lapack 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-rng 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-sparse 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-stats 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-vm 2025.1.0 pypi_0 pypi
[conda] pytorch-triton-xpu 3.3.1+gitb0e26b73 pypi_0 pypi
[conda] torch 2.8.0a0+gitef6306e pypi_0 pypi
```
3. CPU on Linux
```
/opt/python/cp312-cp312/lib/python3.12/site-packages/torch/_subclasses/functional_tensor.py:279: UserWarning: Failed to initialize NumPy: No module named 'numpy' (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:81.)
cpu = _conversion_method_template(device=torch.device("cpu"))
Collecting environment information...
PyTorch version: 2.8.0.dev20250528+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: AlmaLinux 8.10 (Cerulean Leopard) (x86_64)
GCC version: (GCC) 14.2.1 20250110 (Red Hat 14.2.1-7)
Clang version: Could not collect
CMake version: version 4.0.0
Libc version: glibc-2.28 Python version: 3.12.10 (main, Apr 19 2025, 05:03:56) [GCC 14.2.1 20250110 (Red Hat 14.2.1-7)] (64-bit runtime) Python platform: Linux-6.8.0-40-generic-x86_64-with-glibc2.28
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: False
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
Byte Order: Little Endian
CPU(s): 88
On-line CPU(s) list: 0-87
Thread(s) per core: 2
Core(s) per socket: 22
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) Gold 6238M CPU @ 2.10GHz
Stepping: 7
CPU MHz: 1000.000
CPU max MHz: 3700.0000
CPU min MHz: 1000.0000
BogoMIPS: 4200.00
Virtualization: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 1024K
L3 cache: 30976K
NUMA node0 CPU(s): 0-21,44-65
NUMA node1 CPU(s): 22-43,66-87
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 pni pclmulqdq dtes64 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 cdp_l3 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts vnmi pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Versions of relevant libraries:
[pip3] torch==2.8.0.dev20250528+cpu
[conda] Could not collect
```
5. XPU on Linux
```
Collecting environment information...
PyTorch version: 2.8.0.dev20250516+xpu
Is debug build: False
CUDA used to build PyTorch: None
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 3.31.6
Libc version: glibc-2.35
Python version: 3.10.17 | packaged by conda-forge | (main, Apr 10 2025, 22:19:12) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-5.15.50-051550-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: True
XPU used to build PyTorch: 20250101
Intel GPU driver version:
* intel_opencl: 24.39.31294.21-1032~22.04
* level_zero: 1.17.44.0-1022~22.04
Intel GPU models onboard:
* Intel(R) Data Center GPU Max 1550
* Intel(R) Data Center GPU Max 1550
* Intel(R) Data Center GPU Max 1550
* Intel(R) Data Center GPU Max 1550
Intel GPU models detected:
* [0] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [1] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [2] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [3] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [4] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [5] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [6] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [7] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
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): 224
On-line CPU(s) list: 0-223
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8480+
CPU family: 6
Model: 143
Thread(s) per core: 2
Core(s) per socket: 56
Socket(s): 2
Stepping: 6
CPU max MHz: 3800.0000
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 avx512_fp16 flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 5.3 MiB (112 instances)
L1i cache: 3.5 MiB (112 instances)
L2 cache: 224 MiB (112 instances)
L3 cache: 210 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-55,112-167
NUMA node1 CPU(s): 56-111,168-223
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: 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
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] numpy==2.2.5
[pip3] pytorch-triton-xpu==3.3.0+git0bcc8265
[pip3] torch==2.8.0.dev20250516+xpu
[conda] mkl 2025.1.0 pypi_0 pypi
[conda] numpy 2.2.5 pypi_0 pypi
[conda] onemkl-sycl-blas 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-dft 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-lapack 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-rng 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-sparse 2025.1.0 pypi_0 pypi
[conda] pytorch-triton-xpu 3.3.0+git0bcc8265 pypi_0 pypi
[conda] torch 2.8.0.dev20250516+xpu pypi_0 pypi
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137846
Approved by: https://github.com/guangyey , https://github.com/malfet
Co-authored-by: Yu, Guangye <106960996+guangyey@users.noreply.github.com >
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com >
2025-06-11 01:22:06 +00:00
6fb6293159
Revert "Add Intel GPU info collection to the collect env script ( #137846 )"
...
This reverts commit c6b4f98625bb6b22bb9a60112a6d58e684a97e1b.
Reverted https://github.com/pytorch/pytorch/pull/137846 on behalf of https://github.com/etaf due to This is breaking tests on xpu, detail log: https://hud.pytorch.org/pr/pytorch/pytorch/154962#43700962849 ([comment](https://github.com/pytorch/pytorch/pull/137846#issuecomment-2954517883 ))
2025-06-09 03:13:27 +00:00
c6b4f98625
Add Intel GPU info collection to the collect env script ( #137846 )
...
As title, add Intel GPU info collection to the collect env script
Output examples:
1. CPU on Windows
```
C:\Users\user\miniforge3\envs\py310\lib\site-packages\torch\_subclasses\functional_tensor.py:279: UserWarning: Failed to initialize NumPy: No module named 'numpy' (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\pytorch\torch\csrc\utils\tensor_numpy.cpp:81.)
cpu = _conversion_method_template(device=torch.device("cpu"))
Collecting environment information...
PyTorch version: 2.8.0.dev20250528+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: Microsoft Windows 11 Enterprise (10.0.22631 64-bit)
GCC version: Could not collect
Clang version: Could not collect
CMake version: Could not collect
Libc version: N/A
Python version: 3.10.17 | packaged by conda-forge | (main, Apr 10 2025, 22:06:35) [MSC v.1943 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.22631-SP0
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Name: 12th Gen Intel(R) Core(TM) i7-1270P
Manufacturer: GenuineIntel
Family: 198
Architecture: 9
ProcessorType: 3
DeviceID: CPU0
CurrentClockSpeed: 1711
MaxClockSpeed: 2200
L2CacheSize: 9216
L2CacheSpeed: None
Revision: None
Versions of relevant libraries:
[pip3] torch==2.8.0.dev20250528+cpu
[conda] torch 2.8.0.dev20250528+cpu pypi_0 pypi
```
2. XPU on Windows
```
Collecting environment information...
PyTorch version: 2.8.0a0+gitef6306e
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: Microsoft Windows 10 Pro (10.0.19045 64-bit)
GCC version: (GCC) 13.1.0
Clang version: Could not collect
CMake version: version 3.29.3
Libc version: N/A
Python version: 3.10.17 | packaged by conda-forge | (main, Apr 10 2025, 22:06:35) [MSC v.1943 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.19045-SP0
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: True
XPU used to build PyTorch: 20250101
Intel GPU driver version:
* 32.0.101.6795 (20250520000000.******+***)
Intel GPU models onboard:
* Intel(R) Arc(TM) A770 Graphics
Intel GPU models detected:
* [0] _XpuDeviceProperties(name='Intel(R) Arc(TM) A770 Graphics', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.33184', total_memory=15915MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=128, sub_group_sizes=[8 16 32], has_fp16=1, has_fp64=0, has_atomic64=1)
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
----------------------
Name: Intel(R) Xeon(R) Platinum 8260 CPU @ 2.40GHz
Manufacturer: GenuineIntel
Family: 179
Architecture: 9
ProcessorType: 3
DeviceID: CPU0
CurrentClockSpeed: 2401
MaxClockSpeed: 2401
L2CacheSize: 24576
L2CacheSpeed: None
Revision: 21767
----------------------
Name: Intel(R) Xeon(R) Platinum 8260 CPU @ 2.40GHz
Manufacturer: GenuineIntel
Family: 179
Architecture: 9
ProcessorType: 3
DeviceID: CPU1
CurrentClockSpeed: 2200
MaxClockSpeed: 2401
L2CacheSize: 24576
L2CacheSpeed: None
Revision: 21767
Versions of relevant libraries:
[pip3] intel_extension_for_pytorch==2.8.10+gitb3ea3a1
[pip3] numpy==2.1.2
[pip3] optree==0.13.1
[pip3] pytorch-triton-xpu==3.3.1+gitb0e26b73
[pip3] torch==2.8.0a0+gitef6306e
[conda] intel-extension-for-pytorch 2.8.10+gitb3ea3a1 pypi_0 pypi
[conda] mkl 2025.1.0 pypi_0 pypi
[conda] mkl-dpcpp 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-blas 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-datafitting 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-dft 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-lapack 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-rng 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-sparse 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-stats 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-vm 2025.1.0 pypi_0 pypi
[conda] pytorch-triton-xpu 3.3.1+gitb0e26b73 pypi_0 pypi
[conda] torch 2.8.0a0+gitef6306e pypi_0 pypi
```
3. CPU on Linux
```
/opt/python/cp312-cp312/lib/python3.12/site-packages/torch/_subclasses/functional_tensor.py:279: UserWarning: Failed to initialize NumPy: No module named 'numpy' (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:81.)
cpu = _conversion_method_template(device=torch.device("cpu"))
Collecting environment information...
PyTorch version: 2.8.0.dev20250528+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: AlmaLinux 8.10 (Cerulean Leopard) (x86_64)
GCC version: (GCC) 14.2.1 20250110 (Red Hat 14.2.1-7)
Clang version: Could not collect
CMake version: version 4.0.0
Libc version: glibc-2.28 Python version: 3.12.10 (main, Apr 19 2025, 05:03:56) [GCC 14.2.1 20250110 (Red Hat 14.2.1-7)] (64-bit runtime) Python platform: Linux-6.8.0-40-generic-x86_64-with-glibc2.28
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: False
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
Byte Order: Little Endian
CPU(s): 88
On-line CPU(s) list: 0-87
Thread(s) per core: 2
Core(s) per socket: 22
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) Gold 6238M CPU @ 2.10GHz
Stepping: 7
CPU MHz: 1000.000
CPU max MHz: 3700.0000
CPU min MHz: 1000.0000
BogoMIPS: 4200.00
Virtualization: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 1024K
L3 cache: 30976K
NUMA node0 CPU(s): 0-21,44-65
NUMA node1 CPU(s): 22-43,66-87
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 pni pclmulqdq dtes64 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 cdp_l3 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts vnmi pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Versions of relevant libraries:
[pip3] torch==2.8.0.dev20250528+cpu
[conda] Could not collect
```
5. XPU on Linux
```
Collecting environment information...
PyTorch version: 2.8.0.dev20250516+xpu
Is debug build: False
CUDA used to build PyTorch: None
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 3.31.6
Libc version: glibc-2.35
Python version: 3.10.17 | packaged by conda-forge | (main, Apr 10 2025, 22:19:12) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-5.15.50-051550-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: True
XPU used to build PyTorch: 20250101
Intel GPU driver version:
* intel_opencl: 24.39.31294.21-1032~22.04
* level_zero: 1.17.44.0-1022~22.04
Intel GPU models onboard:
* Intel(R) Data Center GPU Max 1550
* Intel(R) Data Center GPU Max 1550
* Intel(R) Data Center GPU Max 1550
* Intel(R) Data Center GPU Max 1550
Intel GPU models detected:
* [0] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [1] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [2] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [3] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [4] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [5] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [6] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [7] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
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): 224
On-line CPU(s) list: 0-223
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8480+
CPU family: 6
Model: 143
Thread(s) per core: 2
Core(s) per socket: 56
Socket(s): 2
Stepping: 6
CPU max MHz: 3800.0000
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 avx512_fp16 flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 5.3 MiB (112 instances)
L1i cache: 3.5 MiB (112 instances)
L2 cache: 224 MiB (112 instances)
L3 cache: 210 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-55,112-167
NUMA node1 CPU(s): 56-111,168-223
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: 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
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] numpy==2.2.5
[pip3] pytorch-triton-xpu==3.3.0+git0bcc8265
[pip3] torch==2.8.0.dev20250516+xpu
[conda] mkl 2025.1.0 pypi_0 pypi
[conda] numpy 2.2.5 pypi_0 pypi
[conda] onemkl-sycl-blas 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-dft 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-lapack 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-rng 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-sparse 2025.1.0 pypi_0 pypi
[conda] pytorch-triton-xpu 3.3.0+git0bcc8265 pypi_0 pypi
[conda] torch 2.8.0.dev20250516+xpu pypi_0 pypi
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137846
Approved by: https://github.com/guangyey , https://github.com/malfet
Co-authored-by: Yu, Guangye <106960996+guangyey@users.noreply.github.com >
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com >
2025-06-06 05:53:24 +00:00
0db3e0cf29
Revert "Add Intel GPU info collection to the collect env script ( #137846 )"
...
This reverts commit e1180c7228ba8c8b16cabf78706d4a67ca189a6b.
Reverted https://github.com/pytorch/pytorch/pull/137846 on behalf of https://github.com/malfet due to Breaks doc test, but should be easily fixable ([comment](https://github.com/pytorch/pytorch/pull/137846#issuecomment-2947935940 ))
2025-06-06 03:08:48 +00:00
e1180c7228
Add Intel GPU info collection to the collect env script ( #137846 )
...
As title, add Intel GPU info collection to the collect env script
Output examples:
1. CPU on Windows
```
C:\Users\user\miniforge3\envs\py310\lib\site-packages\torch\_subclasses\functional_tensor.py:279: UserWarning: Failed to initialize NumPy: No module named 'numpy' (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\pytorch\torch\csrc\utils\tensor_numpy.cpp:81.)
cpu = _conversion_method_template(device=torch.device("cpu"))
Collecting environment information...
PyTorch version: 2.8.0.dev20250528+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: Microsoft Windows 11 Enterprise (10.0.22631 64-bit)
GCC version: Could not collect
Clang version: Could not collect
CMake version: Could not collect
Libc version: N/A
Python version: 3.10.17 | packaged by conda-forge | (main, Apr 10 2025, 22:06:35) [MSC v.1943 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.22631-SP0
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Name: 12th Gen Intel(R) Core(TM) i7-1270P
Manufacturer: GenuineIntel
Family: 198
Architecture: 9
ProcessorType: 3
DeviceID: CPU0
CurrentClockSpeed: 1711
MaxClockSpeed: 2200
L2CacheSize: 9216
L2CacheSpeed: None
Revision: None
Versions of relevant libraries:
[pip3] torch==2.8.0.dev20250528+cpu
[conda] torch 2.8.0.dev20250528+cpu pypi_0 pypi
```
2. XPU on Windows
```
Collecting environment information...
PyTorch version: 2.8.0a0+gitef6306e
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: Microsoft Windows 10 Pro (10.0.19045 64-bit)
GCC version: (GCC) 13.1.0
Clang version: Could not collect
CMake version: version 3.29.3
Libc version: N/A
Python version: 3.10.17 | packaged by conda-forge | (main, Apr 10 2025, 22:06:35) [MSC v.1943 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.19045-SP0
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: True
XPU used to build PyTorch: 20250101
Intel GPU driver version:
* 32.0.101.6795 (20250520000000.******+***)
Intel GPU models onboard:
* Intel(R) Arc(TM) A770 Graphics
Intel GPU models detected:
* [0] _XpuDeviceProperties(name='Intel(R) Arc(TM) A770 Graphics', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.33184', total_memory=15915MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=128, sub_group_sizes=[8 16 32], has_fp16=1, has_fp64=0, has_atomic64=1)
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
----------------------
Name: Intel(R) Xeon(R) Platinum 8260 CPU @ 2.40GHz
Manufacturer: GenuineIntel
Family: 179
Architecture: 9
ProcessorType: 3
DeviceID: CPU0
CurrentClockSpeed: 2401
MaxClockSpeed: 2401
L2CacheSize: 24576
L2CacheSpeed: None
Revision: 21767
----------------------
Name: Intel(R) Xeon(R) Platinum 8260 CPU @ 2.40GHz
Manufacturer: GenuineIntel
Family: 179
Architecture: 9
ProcessorType: 3
DeviceID: CPU1
CurrentClockSpeed: 2200
MaxClockSpeed: 2401
L2CacheSize: 24576
L2CacheSpeed: None
Revision: 21767
Versions of relevant libraries:
[pip3] intel_extension_for_pytorch==2.8.10+gitb3ea3a1
[pip3] numpy==2.1.2
[pip3] optree==0.13.1
[pip3] pytorch-triton-xpu==3.3.1+gitb0e26b73
[pip3] torch==2.8.0a0+gitef6306e
[conda] intel-extension-for-pytorch 2.8.10+gitb3ea3a1 pypi_0 pypi
[conda] mkl 2025.1.0 pypi_0 pypi
[conda] mkl-dpcpp 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-blas 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-datafitting 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-dft 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-lapack 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-rng 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-sparse 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-stats 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-vm 2025.1.0 pypi_0 pypi
[conda] pytorch-triton-xpu 3.3.1+gitb0e26b73 pypi_0 pypi
[conda] torch 2.8.0a0+gitef6306e pypi_0 pypi
```
3. CPU on Linux
```
/opt/python/cp312-cp312/lib/python3.12/site-packages/torch/_subclasses/functional_tensor.py:279: UserWarning: Failed to initialize NumPy: No module named 'numpy' (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:81.)
cpu = _conversion_method_template(device=torch.device("cpu"))
Collecting environment information...
PyTorch version: 2.8.0.dev20250528+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: AlmaLinux 8.10 (Cerulean Leopard) (x86_64)
GCC version: (GCC) 14.2.1 20250110 (Red Hat 14.2.1-7)
Clang version: Could not collect
CMake version: version 4.0.0
Libc version: glibc-2.28 Python version: 3.12.10 (main, Apr 19 2025, 05:03:56) [GCC 14.2.1 20250110 (Red Hat 14.2.1-7)] (64-bit runtime) Python platform: Linux-6.8.0-40-generic-x86_64-with-glibc2.28
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: False
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
Byte Order: Little Endian
CPU(s): 88
On-line CPU(s) list: 0-87
Thread(s) per core: 2
Core(s) per socket: 22
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) Gold 6238M CPU @ 2.10GHz
Stepping: 7
CPU MHz: 1000.000
CPU max MHz: 3700.0000
CPU min MHz: 1000.0000
BogoMIPS: 4200.00
Virtualization: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 1024K
L3 cache: 30976K
NUMA node0 CPU(s): 0-21,44-65
NUMA node1 CPU(s): 22-43,66-87
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 pni pclmulqdq dtes64 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 cdp_l3 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts vnmi pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Versions of relevant libraries:
[pip3] torch==2.8.0.dev20250528+cpu
[conda] Could not collect
```
5. XPU on Linux
```
Collecting environment information...
PyTorch version: 2.8.0.dev20250516+xpu
Is debug build: False
CUDA used to build PyTorch: None
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 3.31.6
Libc version: glibc-2.35
Python version: 3.10.17 | packaged by conda-forge | (main, Apr 10 2025, 22:19:12) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-5.15.50-051550-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: True
XPU used to build PyTorch: 20250101
Intel GPU driver version:
* intel_opencl: 24.39.31294.21-1032~22.04
* level_zero: 1.17.44.0-1022~22.04
Intel GPU models onboard:
* Intel(R) Data Center GPU Max 1550
* Intel(R) Data Center GPU Max 1550
* Intel(R) Data Center GPU Max 1550
* Intel(R) Data Center GPU Max 1550
Intel GPU models detected:
* [0] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [1] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [2] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [3] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [4] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [5] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [6] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
* [7] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', driver_version='1.6.31294+21', total_memory=65536MB, max_compute_units=512, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
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): 224
On-line CPU(s) list: 0-223
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8480+
CPU family: 6
Model: 143
Thread(s) per core: 2
Core(s) per socket: 56
Socket(s): 2
Stepping: 6
CPU max MHz: 3800.0000
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 avx512_fp16 flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 5.3 MiB (112 instances)
L1i cache: 3.5 MiB (112 instances)
L2 cache: 224 MiB (112 instances)
L3 cache: 210 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-55,112-167
NUMA node1 CPU(s): 56-111,168-223
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: 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
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] numpy==2.2.5
[pip3] pytorch-triton-xpu==3.3.0+git0bcc8265
[pip3] torch==2.8.0.dev20250516+xpu
[conda] mkl 2025.1.0 pypi_0 pypi
[conda] numpy 2.2.5 pypi_0 pypi
[conda] onemkl-sycl-blas 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-dft 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-lapack 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-rng 2025.1.0 pypi_0 pypi
[conda] onemkl-sycl-sparse 2025.1.0 pypi_0 pypi
[conda] pytorch-triton-xpu 3.3.0+git0bcc8265 pypi_0 pypi
[conda] torch 2.8.0.dev20250516+xpu pypi_0 pypi
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137846
Approved by: https://github.com/guangyey , https://github.com/malfet
Co-authored-by: Yu, Guangye <106960996+guangyey@users.noreply.github.com >
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com >
2025-06-05 22:35:04 +00:00
1bd6bc7190
[BE]: Enable ruff YTT linter for Python version checks ( #153547 )
...
Adds ruff YTT checks to help future proof version checks and follow best practices here. Also makes it easier for static linters like mypy to detect python version branching.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153547
Approved by: https://github.com/albanD
2025-05-14 21:09:16 +00:00
e2f9759bd0
Fix broken URLs ( #152237 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152237
Approved by: https://github.com/huydhn , https://github.com/malfet
2025-04-27 09:56:42 +00:00
675f69f40f
collect_env: gracefully handle no pip ( #151607 )
...
If pip is not installed:
### Before
```console
> python3 torch/utils/collect_env.py
Collecting environment information...
Traceback (most recent call last):
File "/Users/Adam/pytorch/torch/utils/collect_env.py", line 694, in <module>
main()
~~~~^^
File "/Users/Adam/pytorch/torch/utils/collect_env.py", line 677, in main
output = get_pretty_env_info()
File "/Users/Adam/pytorch/torch/utils/collect_env.py", line 672, in get_pretty_env_info
return pretty_str(get_env_info())
~~~~~~~~~~~~^^
File "/Users/Adam/pytorch/torch/utils/collect_env.py", line 497, in get_env_info
pip_version, pip_list_output = get_pip_packages(run_lambda)
~~~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/Users/Adam/pytorch/torch/utils/collect_env.py", line 450, in get_pip_packages
for line in out.splitlines()
^^^^^^^^^^^^^^
AttributeError: 'NoneType' object has no attribute 'splitlines'
```
### After
```console
> python3 torch/utils/collect_env.py
Collecting environment information...
PyTorch version: N/A
Is debug build: N/A
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: N/A
OS: macOS 15.4 (arm64)
GCC version: Could not collect
Clang version: 20.1.0
CMake version: version 3.31.6
Libc version: N/A
Python version: 3.13.2 (main, Apr 8 2025, 15:27:33) [Clang 17.0.0 (clang-1700.0.13.3)] (64-bit runtime)
Python platform: macOS-15.4-arm64-arm-64bit-Mach-O
Is CUDA available: N/A
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: N/A
CPU:
Apple M2 Pro
Versions of relevant libraries:
[pip3] Could not collect
[conda] Could not collect
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/151607
Approved by: https://github.com/malfet
2025-04-18 12:28:58 +00:00
6cbf97ede8
[ROCm] enable HIPMallocAsyncAllocator ( #149145 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149145
Approved by: https://github.com/izaitsevfb
Co-authored-by: Jeff Daily <jeff.daily@amd.com >
2025-03-19 23:42:35 +00:00
e1d143cb7b
Revert "[ROCm] enable HIPMallocAsyncAllocator ( #149145 )"
...
This reverts commit ee1a2b7810126258ce64d1e22b59fae81a3f7bcb.
Reverted https://github.com/pytorch/pytorch/pull/149145 on behalf of https://github.com/izaitsevfb due to breaking internal builds ([comment](https://github.com/pytorch/pytorch/pull/149145#issuecomment-2738115728 ))
2025-03-19 21:12:13 +00:00
ee1a2b7810
[ROCm] enable HIPMallocAsyncAllocator ( #149145 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149145
Approved by: https://github.com/jeffdaily
Co-authored-by: Jeff Daily <jeff.daily@amd.com >
2025-03-19 03:59:55 +00:00
9d37b501db
Revert "[ROCm] enable HIPMallocAsyncAllocator ( #149145 )"
...
This reverts commit 2e02c07a5d1c432547542f90de2885be9ffd13cf.
Reverted https://github.com/pytorch/pytorch/pull/149145 on behalf of https://github.com/ZainRizvi due to Sorry but this is breaking internally. @albanD, might you be able to help get this PR landed? See D71214814 for more details on the failure. To validate the fixes internally, you can follow the instructions here: https://fburl.com/fixing-ghfirst-reverts ([comment](https://github.com/pytorch/pytorch/pull/149145#issuecomment-2730104736 ))
2025-03-17 16:17:02 +00:00
2e02c07a5d
[ROCm] enable HIPMallocAsyncAllocator ( #149145 )
...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149145
Approved by: https://github.com/jeffdaily
2025-03-14 18:21:27 +00:00
0aa34e9591
Revert "Collect packages with importlib in collect_env ( #144616 )"
...
This reverts commit 3541d2a2aaacc4f15ea865c815ce8882577a439c.
Reverted https://github.com/pytorch/pytorch/pull/144616 on behalf of https://github.com/malfet due to Somehow this change causes test_bottleneck_cuda to fail ([comment](https://github.com/pytorch/pytorch/pull/144616#issuecomment-2586095595 ))
2025-01-13 03:11:04 +00:00
3541d2a2aa
Collect packages with importlib in collect_env ( #144616 )
...
If pytorch is installed systemwide (via os package manager) or by alternative package manager like `uv`, pip is not available, causing error in `collect_env`.
However it is still possible to collect exactly the same list using `importlib` API, which is always available.
Fixes #144615
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144616
Approved by: https://github.com/malfet
2025-01-12 23:21:08 +00:00
67cf126cf8
Disable PIP version check in collect_env ( #142308 )
...
Disables version check which might require users to reach out to PyPI, reference: https://pip.pypa.io/en/latest/cli/pip/#cmdoption-disable-pip-version-check
Switches pip to be used directly as a python module (`python3 -mpip`) instead of relying on `pip3` or `pip`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142308
Approved by: https://github.com/seemethere
2024-12-10 19:16:36 +00:00
14e6624473
Update wmic command used in collect_env.py to its counterpart in powershell due to its deprecation ( #138297 )
...
As title.
`wmic` is deprecated in Windows.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138297
Approved by: https://github.com/malfet
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com >
2024-10-18 07:03:17 +00:00
0b168ceb6d
Collect Nvidia libraries with collect_env.py ( #138076 )
...
Collect Nvidia libraries to diagnose issues like #133548 .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/138076
Approved by: https://github.com/malfet
2024-10-18 05:05:00 +00:00
8db9dfa2d7
Flip default value for mypy disallow_untyped_defs [9/11] ( #127846 )
...
See #127836 for details.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127846
Approved by: https://github.com/ezyang
ghstack dependencies: #127842 , #127843 , #127844 , #127845
2024-06-08 18:50:06 +00:00
117ab34891
Documenting the torch.utils.collect_env.get_pretty_env_info function ( #128123 )
...
Fixes #127888
This PR adds docstring to the `torch.utils.collect_env.get_pretty_env_info` function
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128123
Approved by: https://github.com/ezyang , https://github.com/malfet
2024-06-07 00:43:18 +00:00
3acbfd602e
Document torch.utils.collect_env.get_env_info function ( #128021 )
...
Fixes #127911
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128021
Approved by: https://github.com/malfet
2024-06-05 17:44:47 +00:00
3fe437b24b
[BE]: Update flake8 to v6.1.0 and fix lints ( #116591 )
...
Updates flake8 to v6.1.0 and fixes a few lints using sed and some ruff tooling.
- Replace `assert(0)` with `raise AssertionError()`
- Remove extraneous parenthesis i.e.
- `assert(a == b)` -> `assert a == b`
- `if(x > y or y < z):`->`if x > y or y < z:`
- And `return('...')` -> `return '...'`
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com >
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116591
Approved by: https://github.com/albanD , https://github.com/malfet
2024-01-03 06:04:44 +00:00
5e58be678c
Make collect env BC compatible ( #116532 )
...
To avoid errors like the one in https://github.com/pytorch/pytorch/issues/116531 when the user tries to run collect_env
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116532
Approved by: https://github.com/malfet
2023-12-30 01:13:37 +00:00
765d4599ee
Give users control over packages in torch.utils.collect_env ( #112993 )
...
I'm looking to repurpose some logic in `torch.utils.collect_env` for the `geowatch` package. I'm mostly able to just use this script as a library, which is great because it reduces code in my package. However, the issue is that the package patterns that are relevant to torch are hard-coded inside of `get_conda_packages` and `get_pip_packages`.
The changes I made are simple. I defined the default package patterns as two global sets, and I added an argument to each function that lets the user customize exactly what package patterns are relevant. If they are not specified the defaults are used.
I was considering extending the power of the patterns by utilizing `fnmatch`, `re` (or [xdev.pattern](https://github.com/Erotemic/xdev/blob/main/xdev/patterns.py ) which abstracts them both), but instead I opted to just use the existing `__contains__` test to keep things simple.
From torch's perspective this should make maintaining this file slightly easier because to update relevant packages, the developer now updates two neighboring top-level globals instead of two separated local variables. However, it does add an argument to two functions, and that argument isn't used in torch itself, so there is an argument for removing that, and then users *could* still have some control by modifying globals, but I think the way I did it balances the tradeoffs well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112993
Approved by: https://github.com/zou3519
2023-11-28 22:35:25 +00:00
5e10dd2c78
fix docstring issues in torch.utils ( #113335 )
...
Fixes #112634
Fixes all the issues listed except in `torch/utils/_pytree.py` as the file no longer exists.
### Error counts
|File | Count Before | Count now|
|---- | ---- | ---- |
|`torch/utils/collect_env.py` | 39 | 25|
|`torch/utils/cpp_extension.py` | 51 | 13|
|`torch/utils/flop_counter.py` | 25 | 8|
|`torch/utils/_foreach_utils.py.py` | 2 | 0|
|`torch/utils/_python_dispatch.py.py` | 26 | 25|
|`torch/utils/backend_registration.py` | 15 | 4|
|`torch/utils/checkpoint.py` | 29 | 21|
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113335
Approved by: https://github.com/ezyang
2023-11-13 19:37:25 +00:00
333d5821ee
[ROCm] Add gcnArchName to collect_env and torch.cuda.get_device_properties ( #107477 )
...
Printing just the device name is not helpful when investigating PyTorch issues filed for specific AMD GPUs, as the support/issue might depend on the gfx arch, which is part of the gcnArchName property.
`torch.cuda.get_device_properties(0).gcnArchName` will print the value of the `gcnArchName` property: eg.
```
>>> torch.cuda.get_device_properties(0).gcnArchName
'gfx906:sramecc+:xnack-'
```
```
root@6f064e3c19fb:/data/pytorch/test# python ../torch/utils/collect_env.py
...
GPU models and configuration: AMD Radeon Graphics(gfx906:sramecc+:xnack-)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107477
Approved by: https://github.com/albanD
2023-10-31 23:05:36 +00:00
d24e7be243
Include onnx and onnxscript information in collect_env.py ( #110560 )
...
`onnx` and `onnxscript` are used in torch.onnx.dynamo_export since 2.0. It would be helpful to collect version information in user issue reports.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110560
Approved by: https://github.com/albanD
2023-10-05 01:29:04 +00:00
0bf30c140a
[pytree] Use OpTree for PyTree manipulation ( #93139 )
...
Split from #92679 . Use C++-based PyTree implementation.
## Highlights
1. High performance (20x speedup than the pure-Python implementation, 10%-20% overall speedup for `torch.fx`)
2. Multi-input tree-map support
3. Custom tree node registry with namespace isolation
Refs:
- #65761
- #91323
- #92679
From https://github.com/pytorch/pytorch/issues/65761#issuecomment-1334746366 :
> ### 0. Out-of-box compatible with JAX's pytree, provides the same interfaces and functions (and more).
>
> ### 1. High-performance: `optree` has comparable fast tree operations (~0.9x for `dict`s and ~2.5x for `OrderedDict`s) than JAX's pytree and it is 20x faster than `torch.utils._pytree`.
>
> `optree` implements some common Python container types in C++ (e.g., `OrderedDict`) and achieves 2.5x performance than JAX's pytree. Check out section [Built-in PyTree Node Types](https://github.com/metaopt/optree#built-in-pytree-node-types ) and [Benchmark](https://github.com/metaopt/optree#benchmark ) for more details.
>
> | Module | Nodes | OpTree (μs) | JAX XLA (μs) | PyTorch (μs) | DM-Tree (μs) | Speedup (J / O) | Speedup (P / O) | Speedup (D / O) |
> | :-------- | ----: | ----------: | -----------: | -----------: | -----------: | --------------: | --------------: | --------------: |
> | TinyMLP | 53 | 26.40 | 68.19 | 586.87 | 34.14 | 2.58 | 22.23 | 1.29 |
> | AlexNet | 188 | 84.28 | 259.51 | 2182.07 | 125.12 | 3.08 | 25.89 | 1.48 |
> | ResNet18 | 698 | 288.57 | 807.27 | 7881.69 | 429.39 | 2.80 | 27.31 | 1.49 |
> | ResNet34 | 1242 | 580.75 | 1564.97 | 15082.84 | 819.02 | 2.69 | 25.97 | 1.41 |
> | ResNet50 | 1702 | 791.18 | 2081.17 | 20982.82 | 1104.62 | 2.63 | 26.52 | 1.40 |
> | ResNet101 | 3317 | 1603.93 | 3939.37 | 40382.14 | 2208.63 | 2.46 | 25.18 | 1.38 |
> | ResNet152 | 4932 | 2446.56 | 6267.98 | 56892.36 | 3139.17 | 2.56 | 23.25 | 1.28 |
> | ViT-H/14 | 3420 | 1681.48 | 4488.33 | 41703.16 | 2504.86 | 2.67 | 24.80 | 1.49 |
> | Swin-B | 2881 | 1565.41 | 4091.10 | 34241.99 | 1936.75 | 2.61 | 21.87 | 1.24 |
> | | | | | | **Average** | **2.68** | **24.78** | **1.38** |
>
> <div align="center">
> <img src="https://user-images.githubusercontent.com/16078332/200494435-fd5bb385-59f7-4811-b520-98bf5763ccf3.png " width="90%" />
> </div>
>
> ### 2. Namespace Isolation for the PyTree Type Registry
>
> In addition to the JAX's pytree registry for custom node type registration, `optree` adds `namespace` isolation to the registry. Users can register the same type multiple times for different flatten/unflatten behavior. It also provides module-level isolation for safety reasons. For example, you can add a unique prefix to your namespace to isolate your registry with other modules (e.g., `torch.xxx`, `torch.functorch.xxx`):
>
> ```python
> # Register a Python type into a namespace
> import torch
>
> optree.register_pytree_node(
> torch.Tensor,
> # (tensor) -> (children, metadata)
> flatten_func=lambda tensor: (
> (tensor.cpu().numpy(),),
> dict(dtype=tensor.dtype, device=tensor.device, requires_grad=tensor.requires_grad),
> ),
> # (metadata, children) -> tensor
> unflatten_func=lambda metadata, children: torch.tensor(children[0], **metadata),
> namespace='torch.torch2numpy',
> )
> ```
>
> ```python
> >>> tree = {'weight': torch.ones(size=(1, 2)).cuda(), 'bias': torch.zeros(size=(2,))}
> >>> tree
> {'weight': tensor([[1., 1.]], device='cuda:0'), 'bias': tensor([0., 0.])}
>
> # Flatten without specifying the namespace
> >>> tree_flatten(tree) # `torch.Tensor`s are leaf nodes
> ([tensor([0., 0.]), tensor([[1., 1.]], device='cuda:0')], PyTreeSpec({'bias': *, 'weight': *}))
>
> # Flatten with the namespace
> >>> leaves, treespec = optree.tree_flatten(tree, namespace='torch.torch2numpy')
> >>> leaves, treespec
> (
> [array([0., 0.], dtype=float32), array([[1., 1.]], dtype=float32)],
> PyTreeSpec(
> {
> 'bias': CustomTreeNode(Tensor[{'dtype': torch.float32, 'device': device(type='cpu'), 'requires_grad': False}], [*]),
> 'weight': CustomTreeNode(Tensor[{'dtype': torch.float32, 'device': device(type='cuda', index=0), 'requires_grad': False}], [*])
> },
> namespace='torch.torch2numpy'
> )
> )
>
> # `entries` are not defined and use `range(len(children))`
> >>> optree.tree_paths(tree, namespace='torch.torch2numpy')
> [('bias', 0), ('weight', 0)]
>
> # Unflatten back to a copy of the original object
> >>> optree.tree_unflatten(treespec, leaves)
> {'bias': tensor([0., 0.]), 'weight': tensor([[1., 1.]], device='cuda:0')}
> ```
>
> Check out section [Registering a Container-like Custom Type as Non-leaf Nodes](https://github.com/metaopt/optree#notes-about-the-pytree-type-registry ) for more details.
>
> ### 3. Support both `None` as Non-leaf Node and `None` as Leaf
>
> In JAX's implementation, `None` is always an internal non-leaf node with an arity 0, which is like an empty tuple. This limits the usage of the JAX's pytree utilities for PyTorch. For example, the `nn.Module` uses `_parameters` and `_buffers` (`OrderedDict[str, Optional[Tensor]]`) to hold the tensors, while the value can be a tensor or `None`.
>
> `optree` supports both `None` as Non-leaf Node (JAX's default) and `None` as Leaf (PyTorch's default). Check out section [None is Non-leaf Node vs. None is Leaf](https://github.com/metaopt/optree#none-is-non-leaf-node-vs-none-is-leaf ) for more details.
>
> ### 4. Some other improvements and bug fixes
>
> 1. Adds in-place version of treemap (`tree_map_`), which reduces redundant unflatten operation for better performance.
> 2. Adds support for tree flatten and tree map with paths. (useful for `functorch` module extraction).
> 3. Improves the JAX's pytree sorting support for `dict`s.
> 4. Better string representation `repr(PyTreeSpec)`.
> 5. Fixes some bugs for JAX's pytree of hashing, pickle serialization, segmentation fault for infinite recursion, and tree-compose/tree-transpose.
From https://github.com/pytorch/pytorch/pull/92679#issuecomment-1398778481 :
> ```python
> # pytree_make_fx_bench.py
> import torch
> from torch.fx.experimental.proxy_tensor import make_fx
> import time
>
> def f(x):
> for _ in range(10000):
> x = x+x
> return x
>
> import time
> begin = time.time()
> out = make_fx(f, tracing_mode="real")(torch.randn(20))
> begin = time.time()
> print(f'tracing_mode="real" {time.time() - begin:.2f}')
> out = make_fx(f, tracing_mode="fake")(torch.randn(20))
> print(f'tracing_mode="fake" {time.time() - begin:.2f}')
>
> out = make_fx(f, tracing_mode="symbolic")(torch.randn(20))
> print(f'tracing_mode="symbolic" {time.time() - begin:.2f}')
> ```
>
> This seems to run around 10-20% faster with the optree implementation:
>
> ```
> # Optree
> python pytree_make_fx_bench.py
> tracing_mode="real" 0.00
> tracing_mode="fake" 6.32
> tracing_mode="symbolic" 27.13
> ```
>
> ```
> # torch.utils._pytree
> python pytree_make_fx_bench.py
> tracing_mode="real" 0.00
> tracing_mode="fake" 7.66
> tracing_mode="symbolic" 31.07
> ```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93139
Approved by: https://github.com/malfet
2023-09-18 21:24:56 +00:00
2f95a3d0fc
[BE]: Apply ruff PERF fixes to torch ( #104917 )
...
Applies automated ruff fixes in the PERF modules and enables all automatic ones. I also updated ruff which applied some additional fixes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104917
Approved by: https://github.com/ezyang , https://github.com/albanD
2023-07-11 20:45:21 +00:00
1ac663d9f1
collect_env: parse HIP version exception free (#101844 )
...
Should prevent broken collect_env reporting as shown in https://github.com/pytorch/vision/issues/7561#issue-1698000841
<!--
copilot:poem
-->
### <samp>🤖 Generated by Copilot at 5204e0f</samp>
> _`get_version_or_na`_
> _Helper function refactors_
> _Code like autumn leaves_
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101844
Approved by: https://github.com/kit1980 , https://github.com/ZainRizvi
2023-05-19 17:24:35 +00:00
bd78532020
[BE] Fix collect_env for python-path-with-space ( #98415 )
...
By invoking [`Popen`](https://docs.python.org/2.7/library/subprocess.html#popen-constructor ) with list of command line arguments, rather than strings that would be parsed by shell.
Test plan:
```shell
% conda create -n py311 python=3.11
% cd ~/miniconda3/envs
% cp -a py311 py\ 311
% ./py\ 311/bin/python -mtorch.utils.collect_env
```
Fixes https://github.com/pytorch/pytorch/issues/98385
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98415
Approved by: https://github.com/huydhn
2023-04-06 01:09:23 +00:00
5e6e984835
flake8 version reporting in collect_env ( #94573 )
...
Fixes #94571
# Testing
`[pip3] flake8==3.9.2` now appears under `Versions of relevant libraries:`
when running: `python torch/utils/collect_env.py`
### Output with this change
```
Collecting environment information...
PyTorch version: N/A
Is debug build: N/A
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: N/A
OS: macOS 13.1 (x86_64)
GCC version: Could not collect
Clang version: 14.0.0 (clang-1400.0.29.202)
CMake version: Could not collect
Libc version: N/A
Python version: 3.9.12 (main, Apr 5 2022, 01:53:17) [Clang 12.0.0 ] (64-bit runtime)
Python platform: macOS-10.16-x86_64-i386-64bit
Is CUDA available: N/A
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: N/A
CPU:
Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz
Versions of relevant libraries:
[pip3] flake8==3.9.2
[pip3] mypy==0.971
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.21.5
[pip3] numpydoc==1.2
[conda] blas 1.0 mkl
[conda] mkl 2021.4.0 hecd8cb5_637
[conda] mkl-service 2.4.0 py39h9ed2024_0
[conda] mkl_fft 1.3.1 py39h4ab4a9b_0
[conda] mkl_random 1.2.2 py39hb2f4e1b_0
[conda] numpy 1.21.5 py39h2e5f0a9_1
[conda] numpy-base 1.21.5 py39h3b1a694_1
[conda] numpydoc 1.2 pyhd3eb1b0_0
```
### Output before
```
Collecting environment information...
PyTorch version: N/A
Is debug build: N/A
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: N/A
OS: macOS 13.1 (x86_64)
GCC version: Could not collect
Clang version: 14.0.0 (clang-1400.0.29.202)
CMake version: Could not collect
Libc version: N/A
Python version: 3.9.12 (main, Apr 5 2022, 01:53:17) [Clang 12.0.0 ] (64-bit runtime)
Python platform: macOS-10.16-x86_64-i386-64bit
Is CUDA available: N/A
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: N/A
CPU:
Intel(R) Core(TM) i9-9880H CPU @ 2.30GHz
Versions of relevant libraries:
[pip3] mypy==0.971
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.21.5
[pip3] numpydoc==1.2
[conda] blas 1.0 mkl
[conda] mkl 2021.4.0 hecd8cb5_637
[conda] mkl-service 2.4.0 py39h9ed2024_0
[conda] mkl_fft 1.3.1 py39h4ab4a9b_0
[conda] mkl_random 1.2.2 py39hb2f4e1b_0
[conda] numpy 1.21.5 py39h2e5f0a9_1
[conda] numpy-base 1.21.5 py39h3b1a694_1
[conda] numpydoc 1.2 pyhd3eb1b0_0
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94573
Approved by: https://github.com/malfet , https://github.com/kit1980
2023-03-28 03:24:41 +00:00
4454655a4c
Add triton to relevant packages ( #96663 )
...
Signed-off-by: Edward Z. Yang <ezyang@meta.com >
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96663
Approved by: https://github.com/janeyx99 , https://github.com/malfet , https://github.com/atalman
2023-03-13 19:02:07 +00:00
3ce1ebb6fb
Apply some safe comprehension optimizations ( #94323 )
...
Optimize unnecessary collection cast calls, unnecessary calls to list, tuple, and dict, and simplify calls to the sorted builtin. This should strictly improve speed and improve readability.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94323
Approved by: https://github.com/albanD
2023-02-07 23:53:46 +00:00
8fce9a09cd
[BE]: pyupgrade Python to 3.8 - imports and object inheritance only ( #94308 )
...
Apply parts of pyupgrade to torch (starting with the safest changes).
This PR only does two things: removes the need to inherit from object and removes unused future imports.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94308
Approved by: https://github.com/ezyang , https://github.com/albanD
2023-02-07 21:10:56 +00:00
79243516f6
collect CPU info with collect_env.py for new issues reporting ( #93899 )
...
Add CPU information collection feature to collect_env.py for new issues reporting. This helps us to triage issues on CPU.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/93899
Approved by: https://github.com/malfet
2023-02-03 04:58:53 +00:00
77d94ac5ab
Sets CUDA_MODULE_LOADING to LAZY when not set by the user ( #85692 )
...
This PR sets CUDA_MODULE_LOADING if it's not set by the user. By default, it sets it to "LAZY".
It was tested using the following commands:
```
python -c "import torch; tensor=torch.randn(20, 16, 50, 100).cuda(); free, total = torch.cuda.cudart().cudaMemGetInfo(0); print(total-free)"
```
which shows a memory usage of: 287,047,680 bytes
vs
```
CUDA_MODULE_LOADING="DEFAULT" python -c "import torch; tensor=torch.randn(20, 16, 50, 100).cuda(); free, total = torch.cuda.cudart().cudaMemGetInfo(0); print(total-free)"
```
which shows 666,632,192 bytes.
C++ implementation is needed for the libtorch users (otherwise it could have been a pure python functionality).
cc: @ptrblck @ngimel @malfet
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85692
Approved by: https://github.com/malfet
2022-10-13 14:03:01 +00:00
be25566d13
tools: Ensure compat for collect_env with python 3.5
...
Users were reporting errors of not being able to use collect_env with
older versions of python. This adds a test to ensure that we maintain
compat for this script with older versions of python
Signed-off-by: Eli Uriegas <eliuriegasfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78946
Approved by: https://github.com/janeyx99
2022-06-06 21:32:57 +00:00
635aaa3d9d
replace "grep" with Python processing in collect_env.py ( #77148 )
...
Fixes #77063 .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77148
Approved by: https://github.com/ezyang
2022-05-10 19:35:15 +00:00
c170d395de
utils: Only check for xnnpack if torch installed ( #74342 )
...
Summary:
Fixes a bug where collect_env.py was not able to be run without having
torch installed
Signed-off-by: Eli Uriegas <eliuriegas@fb.com >
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74342
Reviewed By: malfet, janeyx99
Differential Revision: D34943464
Pulled By: seemethere
fbshipit-source-id: dbaa0004b88cb643a9c6426c9ea7c5be3d3c9ef5
(cherry picked from commit 4f39ebb823f88df0c3902db15deaffc6ba481cb3)
2022-03-17 15:31:26 +00:00
b2054d3025
Prepare for an update to the XNNPACK submodule ( #72642 )
...
Summary:
- Target Sha1: ae108ef49aa5623b896fc93d4298c49d1750d9ba
- Make USE_XNNPACK a dependent option on cmake minimum version 3.12
- Print USE_XNNPACK under cmake options summary, and print the
availability from collet_env.py
- Skip XNNPACK based tests when XNNPACK is not available
- Add SkipIfNoXNNPACK wrapper to skip tests
- Update cmake version for xenial-py3.7-gcc5.4 image to 3.12.4
- This is required for the backwards compatibility test.
The PyTorch op schema is XNNPACK dependent. See,
aten/src/ATen/native/xnnpack/RegisterOpContextClass.cpp for
example. The nightly version is assumed to have USE_XNNPACK=ON,
so with this change we ensure that the test build can also
have XNNPACK.
- HACK: skipping test_xnnpack_integration tests on ROCM
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72642
Reviewed By: kimishpatel
Differential Revision: D34456794
Pulled By: digantdesai
fbshipit-source-id: 85dbfe0211de7846d8a84321b14fdb061cd6c037
(cherry picked from commit 6cf48e7b64d6979962d701b5d493998262cc8bfa)
2022-02-25 00:39:15 +00:00
3f06f29577
Improve pip package determination ( #63321 )
...
Summary:
Invoking `pip` or `pip3` yields list of packages invoked for `pip` alias on the path, rather than for the one currently being executed. Changed `get_pip_packages` to use `sys.executable + '-mpip'`
Also, add mypy to the list of packages of interest
Discovered while looking at https://github.com/pytorch/pytorch/issues/63279
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63321
Reviewed By: walterddr
Differential Revision: D30342099
Pulled By: malfet
fbshipit-source-id: fc8d17cf2ddcf18236cfde5c1b9edb4e72804ee0
2021-08-16 13:54:39 -07:00
