How to deploy Vision Transformer with ANE to Achieve Faster Uncached Load Speed

## ❓Question
I wanted to deploy some ViT models on an iPhone. I referred to https://machinelearning.apple.com/research/vision-transformers for deployment and wrote a simple demo based on the code from https://github.com/apple/ml-vision-transformers-ane. However, I found that the uncached load time on the phone is very long. According to the blog, the input is already aligned to 64 bytes, but the speed is still very slow. Is there any way to speed it up? This is my test case:
```python
import torch
import coremltools as ct
import math
from torch import nn

class SelfAttn(torch.nn.Module):
def __init__(self, window_size, num_heads, dim, dim_out):
super().__init__()
self.window_size = window_size
self.num_heads = num_heads
self.dim = dim
self.dim_out = dim_out
self.q_proj = nn.Conv2d(
in_channels=dim,
out_channels=dim_out,
kernel_size=1,
)
self.k_proj = nn.Conv2d(
in_channels=dim,
out_channels=dim_out,
kernel_size=1,
)
self.v_proj = nn.Conv2d(
in_channels=dim,
out_channels=dim_out,
kernel_size=1,
)

def forward(self, x):
B, HW, C = x.shape
image_shape = (B, C, self.window_size, self.window_size)
x_2d = x.permute((0, 2, 1)).reshape(image_shape) # BCHW
x_flat = torch.unsqueeze(x.permute((0, 2, 1)), 2) # BC1L
q, k, v_2d = self.q_proj(x_flat), self.k_proj(x_flat), self.v_proj(x_2d)

mh_q = torch.split(q, self.dim_out // self.num_heads, dim=1) # BC1L
mh_v = torch.split(
v_2d.reshape(B, -1, x_flat.shape[2], x_flat.shape[3]), self.dim_out // self.num_heads, dim=1
)
mh_k = torch.split(
torch.permute(k, (0, 3, 2, 1)), self.dim_out // self.num_heads, dim=3
)
scale_factor = 1 / math.sqrt(mh_q[0].size(1))
attn_weights = [
torch.einsum("bchq, bkhc->bkhq", qi, ki) * scale_factor
for qi, ki in zip(mh_q, mh_k)
]
attn_weights = [
torch.softmax(aw, dim=1) for aw in attn_weights
] # softmax applied on channel "C"
mh_x = [torch.einsum("bkhq,bchk->bchq", wi, vi) for wi, vi in zip(attn_weights, mh_v)]
x = torch.cat(mh_x, dim=1)
return x

window_size = 8
path_batch = 1024
emb_dim = 96
emb_dim_out = 96
x = torch.rand(path_batch, window_size * window_size, emb_dim)
qkv_layer = SelfAttn(window_size, 1, emb_dim, emb_dim_out)
jit = torch.jit.trace(qkv_layer, (x))

mlmod_fixed_shape = ct.convert(
jit,
inputs=[
ct.TensorType("x", x.shape),
],
convert_to="mlprogram",
)
mlmodel_path = "test_ane.mlpackage"
mlmod_fixed_shape.save(mlmodel_path)
```
This is my profiler results:
The uncached load took nearly 36 seconds, and it was just a single matrix multiplication.

Start with the provided Python reproduction that converts the SelfAttn model into test_ane.mlpackage, then review the linked Vision Transformer ANE example and the profiler result. Compare the uncached load behavior on an iPhone and determine whether the reported 36-second matrix multiplication is expected or indicates a conversion or deployment issue; a useful result should identify the cause or a supported acceleration path.

Written by the indexing model from the issue text.