Repository: pytorch
Issue: ExpandableMemorySegments not working on H100s/A100s
Labels: module: cuda,triaged,module: CUDACachingAllocator

### 🐛 Describe the bug

On H100s and A100s instances setting  PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' is not taking affect with the latest nightllies.

```
[W CUDAAllocatorConfig.h:28] Warning: expandable_segments not supported on this platform (function operator())
```

Warning is coming from: https://github.com/pytorch/pytorch/blob/main/c10/cuda/CUDAAllocatorConfig.h#L28



### Versions

H100 machine: 
```
<frozen runpy>:128: RuntimeWarning: 'torch.utils.collect_env' found in sys.modules after import of package 'torch.utils', but prior to execution of 'torch.utils.collect_env'; this may result in unpredictable behaviour
Collecting environment information...
PyTorch version: 2.4.0.dev20240317+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: CentOS Stream 9 (x86_64)
GCC version: (GCC) 11.4.1 20231218 (Red Hat 11.4.1-3)
Clang version: Could not collect
CMake version: version 3.26.5
Libc version: glibc-2.34

Python version: 3.11.8 (main, Feb 26 2024, 21:39:34) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.19.0-0_fbk12_zion_11583_g0bef9520ca2b-x86_64-with-glibc2.34
Is CUDA available: True
CUDA runtime version: 12.0.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA H100
GPU 1: NVIDIA H100
GPU 2: NVIDIA H100
GPU 3: NVIDIA H100

Nvidia driver version: 525.105.17
cuDNN version: Probably one of the following:
/usr/lib64/libcudnn.so.8.8.0
/usr/lib64/libcudnn_adv_infer.so.8.8.0
/usr/lib64/libcudnn_adv_train.so.8.8.0
/usr/lib64/libcudnn_cnn_infer.so.8.8.0
/usr/lib64/libcudnn_cnn_train.so.8.8.0
/usr/lib64/libcudnn_ops_infer.so.8.8.0
/usr/lib64/libcudnn_ops_train.so.8.8.0
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):                          184
On-line CPU(s) list:             0-183
Vendor ID:                       AuthenticAMD
Model name:                      AMD EPYC 9654 96-Core Processor
CPU family:                      25
Model:                           17
Thread(s) per core:              1
Core(s) per socket:              184
Socket(s):                       1
Stepping:                        1
BogoMIPS:                        4792.78
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 rep_good nopl cpuid extd_apicid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw perfctr_core invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase tsc_adjust 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 wbnoinvd arat npt lbrv nrip_save tsc_scale vmcb_clean pausefilter pfthreshold v_vmsave_vmload vgif avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm arch_capabilities
Virtualization:                  AMD-V
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       11.5 MiB (184 instances)
L1i cache:                       11.5 MiB (184 instances)
L2 cache:                        92 MiB (184 instances)
L3 cache:                        2.9 GiB (184 instances)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-183
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 store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Vulnerable, IBPB: conditional, IBRS_FW, STIBP: disabled, RSB filling
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] pytorch-triton==3.0.0+989adb9a29
[pip3] torch==2.4.0.dev20240317+cu121
[pip3] torchao-nightly==2024.3.17
[pip3] torchaudio==2.2.0.dev20240317+cu121
[pip3] torchtune==0.0.1
[pip3] torchvision==0.18.0.dev20240317+cu121
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] pytorch-triton            3.0.0+989adb9a29          pypi_0    pypi
[conda] torch                     2.4.0.dev20240317+cu121          pypi_0    pypi
[conda] torchao-nightly           2024.3.17                pypi_0    pypi
[conda] torchaudio                2.2.0.dev20240317+cu121          pypi_0    pypi
[conda] torchtune                 0.0.1                    pypi_0    pypi
[conda] torchvision               0.18.0.dev20240317+cu121          pypi_0    pypi
```


A100 machine: 
```
python -m torch.utils.collect_env
<frozen runpy>:128: RuntimeWarning: 'torch.utils.collect_env' found in sys.modules after import of package 'torch.utils', but prior to execution of 'torch.utils.collect_env'; this may result in unpredictable behaviour
Collecting environment information...
PyTorch version: 2.4.0.dev20240317+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.27.7
Libc version: glibc-2.31

Python version: 3.11.8 (main, Feb 26 2024, 21:39:34) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-1048-aws-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA A100-SXM4-40GB
GPU 1: NVIDIA A100-SXM4-40GB
GPU 2: NVIDIA A100-SXM4-40GB
GPU 3: NVIDIA A100-SXM4-40GB
GPU 4: NVIDIA A100-SXM4-40GB
GPU 5: NVIDIA A100-SXM4-40GB
GPU 6: NVIDIA A100-SXM4-40GB
GPU 7: NVIDIA A100-SXM4-40GB

Nvidia driver version: 535.104.12
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Byte Order:                         Little Endian
Address sizes:                      46 bits physical, 48 bits virtual
CPU(s):                             96
On-line CPU(s) list:                0-95
Thread(s) per core:                 2
Core(s) per socket:                 24
Socket(s):                          2
NUMA node(s):                       2
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              85
Model name:                         Intel(R) Xeon(R) Platinum 8275CL CPU @ 3.00GHz
Stepping:                           7
CPU MHz:                            3599.998
BogoMIPS:                           6000.00
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          1.5 MiB
L1i cache:                          1.5 MiB
L2 cache:                           48 MiB
L3 cache:                           71.5 MiB
NUMA node0 CPU(s):                  0-23,48-71
NUMA node1 CPU(s):                  24-47,72-95
Vulnerability Gather data sampling: Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit:        KVM: Mitigation: VMX unsupported
Vulnerability L1tf:                 Mitigation; PTE Inversion
Vulnerability Mds:                  Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Meltdown:             Mitigation; PTI
Vulnerability Mmio stale data:      Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed:             Vulnerable
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Vulnerable
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves ida arat pku ospke

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] pytorch-triton==3.0.0+989adb9a29
[pip3] torch==2.4.0.dev20240317+cu118
[pip3] torchao-nightly==2024.3.17
[pip3] torchtune==0.0.1
[pip3] torchvision==0.18.0.dev20240317+cu118
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] pytorch-triton            3.0.0+989adb9a29          pypi_0    pypi
[conda] torch                     2.4.0.dev20240317+cu118          pypi_0    pypi
[conda] torchao-nightly           2024.3.17                pypi_0    pypi
[conda] torchtune                 0.0.1                    pypi_0    pypi
[conda] torchvision               0.18.0.dev20240317+cu118          pypi_0    pypi
```

cc @ptrblck