Repository: pytorch
Issue: Higher peak memory with torch.compile
Labels: triaged,oncall: pt2,module: aotdispatch,module: inductor

### 🐛 Describe the bug

If I fuse the backward with compiled forward+loss, there's a higher peak memory than if I separate the backward from compiled forward+loss. It looks like the logits aren't being cleared.

Fused forward+loss+backward:
```
@torch.compile
def fused_forward_and_loss_and_backward(input_ids, labels):
    logits = model.forward(input_ids)
    loss = F.cross_entropy(logits.view(-1, logits.shape[-1]).float(), labels.view(-1))
    # del logits # doesn't change peak memory
    loss.backward()
    return loss
...
# usage:
loss = fused_forward_and_loss_and_backward(batch_input_ids, gas_labels)
print("peak memory usage", torch.cuda.max_memory_allocated())
```
Results:
```
peak memory usage 22139266048 (first step)
peak memory usage 23943063552 (second step)
peak memory usage 23943063552 (third step)
```


Fused forward+loss, separated backward:

```
@torch.compile
def fused_forward_and_loss(input_ids, labels):
    logits = model.forward(input_ids)
    loss = F.cross_entropy(logits.view(-1, logits.shape[-1]).float(), labels.view(-1))
    # loss.backward() # no backward here!
    return loss
...
# usage:
loss = fused_forward_and_loss(batch_input_ids, gas_labels)
loss.backward()
print("peak memory usage", torch.cuda.max_memory_allocated())
```

Results:
```
peak memory usage 19991782400
peak memory usage 21795579392
peak memory usage 21795579392
```

Memory traces:
https://drive.google.com/file/d/18UywAfWmBDNJMzbCy44KVUy6qCug3cxX/view?usp=sharing
https://drive.google.com/file/d/1A0Cu9fAbJS1dbzBJvRmm-0U1ygsmtT9W/view?usp=sharing

### Error logs

_No response_

### Minified repro

_No response_

### Versions

Collecting environment information...
PyTorch version: 2.2.0
Is debug build: False
CUDA used to build PyTorch: 12.2
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 3.28.3
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.19.17-coreweave-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA A40
Nvidia driver version: 535.161.07
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6
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:                   48 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          96
On-line CPU(s) list:             0-95
Vendor ID:                       AuthenticAMD
Model name:                      AMD EPYC 7413 24-Core Processor
CPU family:                      25
Model:                           1
Thread(s) per core:              2
Core(s) per socket:              24
Socket(s):                       2
Stepping:                        1
Frequency boost:                 enabled
CPU max MHz:                     3630.8101
CPU min MHz:                     1500.0000
BogoMIPS:                        5299.98
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin brs arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm
Virtualization:                  AMD-V
L1d cache:                       1.5 MiB (48 instances)
L1i cache:                       1.5 MiB (48 instances)
L2 cache:                        24 MiB (48 instances)
L3 cache:                        256 MiB (8 instances)
NUMA node(s):                    2
NUMA node0 CPU(s):               0-23,48-71
NUMA node1 CPU(s):               24-47,72-95
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:        Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.2.0
[pip3] torchaudio==2.2.0
[pip3] torchvision==0.17.0
[pip3] triton==2.2.0
[conda] Could not collect

cc @ezyang @msaroufim @bdhirsh @anijain2305 @zou3519 @chauhang @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @aakhundov @ColinPeppler @amjames @desertfire