Repository: pytorch
Issue: torch.save and torch.load is slow. Slower than numpy. Slower even than pickle.
Labels: module: serialization,triaged

### 🐛 Describe the bug

torch.save and torch.load are slow for vectors.

Here's minimal but nice example to show what I mean:

```python
import torch
import numpy as np
import pickle
import time
import io

def pickle_tensors(tensors):
    total_pickle_time = 0
    total_unpickle_time = 0

    for tensor in tensors:
        # Measure time to pickle
        start = time.time()
        pickled_tensor = pickle.dumps(tensor)
        total_pickle_time += time.time() - start

        # Measure time to unpickle
        start = time.time()
        unpickled_tensor = pickle.loads(pickled_tensor)
        total_unpickle_time += time.time() - start

    return total_pickle_time, total_unpickle_time

def pickle_numpy(tensors):
    total_pickle_time = 0
    total_unpickle_time = 0

    for tensor in tensors:
        numpy_array = tensor.numpy()

        # Measure time to pickle
        start = time.time()
        pickled_array = pickle.dumps(numpy_array)
        total_pickle_time += time.time() - start

        # Measure time to unpickle
        start = time.time()
        unpickled_array = pickle.loads(pickled_array)
        tensor_from_numpy = torch.from_numpy(unpickled_array)
        total_unpickle_time += time.time() - start

    return total_pickle_time, total_unpickle_time


def torch_save_load(tensors):
    total_pickle_time = 0
    total_unpickle_time = 0

    for tensor in tensors:
        buffer = io.BytesIO()

        start = time.time()
        torch.save(tensor, buffer)
        total_pickle_time += time.time() - start

        buffer.seek(0)  # Rewind to the beginning of the buffer before loading

        start = time.time()
        loaded_tensor = torch.load(buffer)
        total_unpickle_time += time.time() - start

    return total_pickle_time, total_unpickle_time

# Generate 10,000 random FP16 tensors with shape 1x1024
tensors = [torch.randn(1, 1024, dtype=torch.float16) for _ in range(10000)]

# Perform the tests
pickle_time, unpickle_time = pickle_tensors(tensors)
numpy_pickle_time, numpy_unpickle_time = pickle_numpy(tensors)
torch_pickle_time, torch_unpickle_time = torch_save_load(tensors)



# print those but in a human readable format
print(f"Pickle tensors: {pickle_time:.2f}s")
print(f"Unpickle tensors: {unpickle_time:.2f}s")
print(f"Pickle numpy arrays: {numpy_pickle_time:.2f}s")
print(f"Unpickle numpy arrays: {numpy_unpickle_time:.2f}s")
print(f"Pickle torch tensors: {torch_pickle_time:.2f}s")
print(f"Unpickle torch tensors: {torch_unpickle_time:.2f}s")

# print the ratios
print(f"Pickle tensors is {pickle_time / numpy_pickle_time:.2f}x slower than pickle numpy arrays")
print(f"Unpickle tensors is {unpickle_time / numpy_unpickle_time:.2f}x slower than unpickle numpy arrays")
```

The code outputs:
```
Pickle tensors: 0.78s
Unpickle tensors: 0.88s
Pickle numpy arrays: 0.13s
Unpickle numpy arrays: 0.10s
Pickle torch tensors: 1.13s
Unpickle torch tensors: 1.29s
Pickle tensors is 5.96x slower than pickle numpy arrays
Unpickle tensors is 8.41x slower than unpickle numpy arrays
```

This is a big problem for bfloat16 vectors because numpy doesn't even support those, so there isn't really a great workaround.

### Versions

```
Collecting environment information...
PyTorch version: 2.1.2+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
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 3.26.3
Libc version: glibc-2.31

Python version: 3.10.13 (main, Aug 25 2023, 13:20:03) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-69-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3
GPU 4: NVIDIA H100 80GB HBM3
GPU 5: NVIDIA H100 80GB HBM3
GPU 6: NVIDIA H100 80GB HBM3
GPU 7: NVIDIA H100 80GB HBM3

Nvidia driver version: 545.23.08
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0
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
Byte Order:                      Little Endian
Address sizes:                   52 bits physical, 57 bits virtual
CPU(s):                          224
On-line CPU(s) list:             0-223
Thread(s) per core:              2
Core(s) per socket:              56
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           143
Model name:                      Intel(R) Xeon(R) Platinum 8480+
Stepping:                        8
CPU MHz:                         1999.999
BogoMIPS:                        4000.00
L1d cache:                       5.3 MiB
L1i cache:                       3.5 MiB
L2 cache:                        224 MiB
L3 cache:                        210 MiB
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 Retbleed:          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, PBRSB-eIBRS SW sequence
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 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 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 cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced 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 arat pln pts 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 amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.26.2
[pip3] onnx==1.14.0
[pip3] onnxruntime==1.15.1
[pip3] pytorch-ranger==0.1.1
[pip3] torch==2.1.2
[pip3] torch-optimizer==0.3.0
[pip3] torchaudio==2.1.2
[pip3] torchmetrics==1.3.0.post0
[pip3] torchvision==0.16.2
[pip3] triton==2.1.0
[pip3] triton-pre-mlir==2.0.0
[conda] Could not collect
```
```[tasklist]
### Tasks
```


cc @mruberry @mikaylagawarecki