Repository: pytorch
Issue: torch.compile doesn't convert all input scalar types to symbolic values
Labels: triaged,oncall: pt2,module: dynamic shapes

### 🐛 Describe the bug

torch.compile seems to only promote integer scalar inputs to SymInt when their value changes during execution. However, other types like float are not converted to SymFloat.

Example: With float input
```
import torch
import torch._dynamo as dynamo
from torch._functorch.aot_autograd import aot_module_simplified

dynamo.reset()

def dummy_compiler(gm, sample_inputs):
    def my_compiler(gm, sample_inputs):
        print(f"{gm.print_readable()}")
        return gm.forward

    # Invoke AOTAutograd
    return aot_module_simplified(
        gm,
        sample_inputs,
        fw_compiler=my_compiler
    )

def div_fn(t, v):
   r = t / v
   return r

compiled_fn = torch.compile(div_fn, backend=dummy_compiler)

t = torch.randn([2, 3])
r = compiled_fn(t, 2.1)

t = torch.randn([3, 3])
r = compiled_fn(t, 3.2)
```
FX graph after first call -
```
def forward(self, arg0_1: f32[2, 3]):
        # File: <ipython-input-8-83a07899468e>:20, code: r = t / v
        div: f32[2, 3] = torch.ops.aten.div.Tensor(arg0_1, 2.1);  arg0_1 = None
        return (div,)
```
FX graph after first call -
```
def forward(self, arg0_1: Sym(s0), arg1_1: f32[s0, 3]):
        # File: <ipython-input-8-83a07899468e>:20, code: r = t / v
        div: f32[s0, 3] = torch.ops.aten.div.Tensor(arg1_1, 3.2);  arg1_1 = None
        return (div,)
```
Example: With int input
```
import torch
import torch._dynamo as dynamo
from torch._functorch.aot_autograd import aot_module_simplified

dynamo.reset()

def dummy_compiler(gm, sample_inputs):
    def my_compiler(gm, sample_inputs):
        print(f"{gm.print_readable()}")
        return gm.forward

    # Invoke AOTAutograd
    return aot_module_simplified(
        gm,
        sample_inputs,
        fw_compiler=my_compiler
    )

def div_fn(t, v):
   r = t / v
   return r

compiled_fn = torch.compile(div_fn, backend=dummy_compiler)

t = torch.randn([2, 3])
r = compiled_fn(t, 2)

t = torch.randn([3, 3])
r = compiled_fn(t, 3)
```

The FX graph produced after first call -
```
def forward(self, arg0_1: f32[2, 3]):
        # File: <ipython-input-7-b09640a76181>:20, code: r = t / v
        div: f32[2, 3] = torch.ops.aten.div.Tensor(arg0_1, 2);  arg0_1 = None
        return (div,)
```
The integer is converted to SymInt in the second call -

```
def forward(self, arg0_1: Sym(s0), arg1_1: f32[s0, 3], arg2_1: Sym(s1)):
        # File: <ipython-input-7-b09640a76181>:20, code: r = t / v
        div: f32[s0, 3] = torch.ops.aten.div.Tensor(arg1_1, arg2_1);  arg1_1 = arg2_1 = None
        return (div,)
``` 
What is the reason for this difference in behavior? This could lead to inadvertent recompilations when a variable input is changed from int to float in a script.

### Versions

PyTorch version: 2.2.0+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.27.9
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-6.1.58+-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
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:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             2
On-line CPU(s) list:                0,1
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) CPU @ 2.20GHz
CPU family:                         6
Model:                              79
Thread(s) per core:                 2
Core(s) per socket:                 1
Socket(s):                          1
Stepping:                           0
BogoMIPS:                           4399.99
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 rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          32 KiB (1 instance)
L1i cache:                          32 KiB (1 instance)
L2 cache:                           256 KiB (1 instance)
L3 cache:                           55 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0,1
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Mitigation; PTE Inversion
Vulnerability Mds:                  Vulnerable; SMT Host state unknown
Vulnerability Meltdown:             Vulnerable
Vulnerability Mmio stale data:      Vulnerable
Vulnerability Retbleed:             Vulnerable
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Vulnerable
Vulnerability Spectre v1:           Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:           Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Vulnerable

Versions of relevant libraries:
[pip3] numpy==1.26.3
[pip3] torch==2.2.0+cu118
[pip3] torchaudio==2.2.0+cu118
[pip3] torchdata==0.7.0
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.16.0
[pip3] torchvision==0.17.0+cu118
[pip3] triton==2.2.0
[conda] Could not collect

cc @ezyang @msaroufim @bdhirsh @anijain2305 @zou3519