From 4c8a6c6092532d8df3f45831d2bfa2715a06507f Mon Sep 17 00:00:00 2001
From: yuwenzho <yuwen.zhou@intel.com>
Date: Thu, 26 Sep 2024 20:26:16 +0800
Subject: [PATCH] Fix torch.compile issue of dispatch key set mismatch (#299)

### Issue:
torch.compile recompiles after warmup because `tensor 'L['input_ids']'
dispatch key set mismatch. expected DispatchKeySet(HPU, BackendSelect),
actual DispatchKeySet(HPU, BackendSelect, ADInplaceOrView). `

### Detail:
Run script with `TORCH_LOGS="guards"` and get different dispatch key set
info:
- warmup:
```
TENSOR_MATCH: check_tensor(L['input_ids'], Tensor, DispatchKeySet(HPU, BackendSelect), torch.int64, device=0, requires_grad=False, size=[2, 1], stride=[1, 1])  # masked_input = input_  # ome/zyuwen/workspace/vllm/habana_main_g3_v2/vllm/model_executor/layers/vocab_parallel_embedding.py:358 in forward
```
- after warmup:
```
TENSOR_MATCH: check_tensor(L['input_ids'], Tensor, DispatchKeySet(HPU, BackendSelect, ADInplaceOrView), torch.int64, device=0, requires_grad=False, size=[2, 1], stride=[1, 1])  # masked_input = input_  # ome/zyuwen/workspace/vllm/habana_main_g3_v2/vllm/model_executor/layers/vocab_parallel_embedding.py:358 in forward
```
### Solution:
The difference in dispatch key set is caused by the
'torch.inference_mode()' decoration, and here is a simple example:
```python
import torch
import habana_frameworks.torch as htorch

@torch.inference_mode()
def func():
    x = torch.rand(3, 3).to("hpu")
    print(torch._C._dispatch_key_set(x))
func()
# output: DispatchKeySet(HPU, AutocastHPU)
```
```python
import torch
import habana_frameworks.torch as htorch

def func():
    x = torch.rand(3, 3).to("hpu")
    print(torch._C._dispatch_key_set(x))
func()
# output: DispatchKeySet(HPU, ADInplaceOrView, AutogradHPU, AutocastHPU)
```

In vllm-fork, the warmup phase is decorated with
`torch.inference_mode()` in
[habana_model_runner.py#L1487-L1488](https://github.com/HabanaAI/vllm-fork/blob/b62fba85ac03326e9f466d8d37e91ae1b14a6511/vllm/worker/habana_model_runner.py#L1487-L1488),
but the after-warmup phase is not.

So in this PR I add the decorator to `prepare_input_tensors` function to
keep the dispatch key set the same.



---

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</details>

Signed-off-by: yuwenzho <yuwen.zhou@intel.com>
---
 vllm/worker/habana_model_runner.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/vllm/worker/habana_model_runner.py b/vllm/worker/habana_model_runner.py
index c43acdf04923b..f3bda39ec4822 100644
--- a/vllm/worker/habana_model_runner.py
+++ b/vllm/worker/habana_model_runner.py
@@ -1790,6 +1790,7 @@ def make_model_input_from_broadcasted_tensor_dict(
                 attn_backend=self.attn_backend,
             ))
 
+    @torch.inference_mode()
     def prepare_model_input(
         self,
         seq_group_metadata_list: List[SequenceGroupMetadata],