Update base for Update on "[ET-VK] Allocate memory for weight and activation tensors lazily"

Summary:
* Allocate memory for weight tensors right before the prepacking shader is dispatched, rather than while building the graph
* Move allocation of shared objects (i.e. memory for intermediate tensors) to occur after prepacking

## Motivation

Prevent screen blackout (Llama 3.2 1B) / device crash (Llama 3.2 3B) when running Llama 3.2 models on Samsung Galaxy S24. This behaviour is related to high peak memory usage when loading the model.

## Full Context

During model loading, Vulkan delegate needs to store 3 copies of constant data in memory at various points:

* source data obtained from loading the model
* staging buffer
* GPU texture/buffer

The general rationale of this change is to allocate memory for each copy only when necessary to minimize the "overlap" when all 3 exist at once.

### Current Order of operations

Legend:
* `W` represents total weight nbytes
* `w` represents weight nbytes for one tensor
* `A` represents total activations nbytes
* `M` represents approximation of total memory footprint

First, model file is loaded

Then, when building compute graph, for each weight tensor:
1. Weight data is loaded from NamedDataMap (`M = W`)
2. GPU texture/buffer for weight is initialized + memory allocated (`M = 2W`)
3. After building the graph, `graph->prepare()` is called which currently allocates memory for the activation tensors as well (`M = 2W + A`)

Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually:
1. Staging buffer initialized (`M = 2W + A + w`)
2. Copy CPU weight data to staging + CPU Weight data is freed (`M = 2W + A`)
3. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = 2W + A - w`)

The peak usage in mainline will be `M = 2W + A + w`

### Revised order of operations

This change revises the order of operations:

1. Weight data is loaded from NamedDataMap (`M = W`)
2. GPU texture/buffer for weight is initialized, but **memory is not allocated** (`M = W`)

Then, during the prepacking stage for each weight tensor, each weight tensor is copied individually:
1. Staging buffer initialized (`M = W + w`)
2. **Memory allocated for GPU texture/buffer** (`M = W + 2w`)
3. Copy CPU weight data to staging + CPU Weight data is freed (`M = W + w`)
4. Compute shader dispatch to copy staging to GPU texture/buffer + free staging buffer (`M = W`)

**Then, after all prepacking operations complete, only then is Activation memory allocated** (`M = W + A`)

Under this scheme, peak memory is reduced to `M = W + A` (or alternatively `M = W + 2w` if `2w > A`) which is (or at least very close to) the theoretical minimum.

Test Plan:
## Logging Memory Usage

Using

```
uint64_t getVmRssInKB() {
  std::ifstream statusFile("/proc/self/status");
  std::string l, num;
  while (std::getline(statusFile, l)) {
    if (l.substr(0, 5) == "VmRSS") {
      size_t pos = l.find_first_of("0123456789");
      num = l.substr(pos);
      break;
    }
  }
  uint64_t vmRssInKB = std::stoi(num);
  return vmRssInKB;
}

uint64_t getVmaStatsInKB() {
  auto stats =
      vkcompute::api::context()->adapter_ptr()->vma().get_memory_statistics();
  uint64_t vmaBlockInKB = stats.total.statistics.blockBytes >> 10;
  return vmaBlockInKB;
}
```

to log memory footprint at various points of inference when running the llama_runner binary with Llama 3.2 1B, we can compare the memory footprint with and without these changes.

With changes: P1908051860 (Meta only)

```
Memory usage before model compilation: 1115760 KB (VmRSS), 0 KB (VMA)
Memory usage after graph building: 1924832 KB (VmRSS), 17920 KB (VMA)
Memory usage after graph preparation: 1935312 KB (VmRSS), 17920 KB (VMA)
Memory usage prepack start: 1935312 KB, VMA Block: 17920 KB
Memory usage after prepack operations: 1372376 KB (VmRSS), 2330528 KB (VMA)

Memory usage before execute: 1372804 KB (VmRSS), 2330528 KB (VMA)
Memory usage at end of execute: 1376916 KB (VmRSS), 2330528 KB (VMA)
```

WIthout changes: P1908054759 (Meta only)

```
Memory usage before model compilation: 1114784 KB (VmRSS), 0 KB (VMA)
Memory usage after graph building: 1924432 KB (VmRSS), 962464 KB (VMA)
Memory usage after graph preparation: 1922916 KB (VmRSS), 2326432 KB (VMA)
Memory usage prepack start: 1922916 KB, VMA Block: 2326432 KB
Memory usage after prepack operations: 1359180 KB (VmRSS), 2330528 KB (VMA)

Memory usage before execute: 1359492 KB (VmRSS), 2330528 KB (VMA)
Memory usage at end of execute: 1363636 KB (VmRSS), 2330528 KB (VMA)
```

It is evident how peak memory can be reduced with these changes, as VMA footprint gradually increases while loading the model while VmRss gradually decreases. Without these changes, VMA footprint will reach its peak after initializing the graph.

Visually, it can also be verified that Samsung Galaxy S24's screen no longer blacks out while loading the model.

Differential Revision: [D80460033](https://our.internmc.facebook.com/intern/diff/D80460033)

[ghstack-poisoned]

Author: ssjia