Peak Memory Consumption doesnt change after applying Multi-Stream Execution

HI, I want to see the change of Peak Memory Consumption after applying multi-stream execution. I try ASAP and WaveFront, but it seems that the Peak Memory Consumption doesn't change at all.

And this is my code.

@yaoyaoding Hello, would you please help me solve this problem? THX :)

```python
from raf._ffi.pass_ import ToGraphNormalForm, WavefrontStreamSchedule
import tvm
from raf._core.vm import VMCompiler
from raf._ffi.pass_ import EstimateMemory, InferType
from raf._core.device import Device

device="cuda"
model, _ = inception.get_model()

# Generate a dummy input.
in_out , _ = inception.get_input(batch_size=32, device="cuda")
m_in=in_out[0]
m_out=in_out[1]
mod = model._internal(m_in,m_out).mod
'''
with raf.ir.PassContext(opt_level=2, config={"raf.stream_schedule.policy": "wavefront"}):
mod = RAFSequential([ToGraphNormalForm(), WavefrontStreamSchedule()])(mod)
'''
mod = RAFSequential([ToGraphNormalForm(), ASAPStreamSchedule()])(mod)

compiler = VMCompiler()
with tvm.transform.PassContext(opt_level=3):
mod, _ = compiler.optimize(mod, device)
mod = InferType()(mod)
trace = [(name, mem.value) for name, mem in EstimateMemory(mod, Device(device), True)]
print(max(trace, key=lambda x: x[1])[1])

```

Start by tracing the interaction between WavefrontStreamSchedule, ASAPStreamSchedule, VMCompiler, and EstimateMemory in the supplied Python example. Run the reproduction and compare the reported peak values for each schedule. Done means explaining why the peak memory is unchanged and correcting the behavior if it is a bug.

Written by the indexing model from the issue text.