From f7c1b27656904b0d050cf820ca8dfdc4aa12c051 Mon Sep 17 00:00:00 2001
From: Mahimai Raja J <mahimairaja@users.noreply.huggingface.co>
Date: Tue, 12 Sep 2023 15:23:58 +0000
Subject: [PATCH] Added cuda config on Sample Code

I tried to replicate the `sample code` in the free versions of `kaggle`, `Colab` and `SageMaker Studio Lab`.
When running on GPU:
``` bash
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
```

And While running on CPU:
```
RuntimeError: "LayerNormKernelImpl" not implemented for 'Half'
```

| Changes I made:

```diff
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto")

+ device = torch.device("cuda:0")
+ model.cuda()

inputs = tokenizer('''```python
def print_prime(n):
   """
   Print all primes between 1 and n
-   """''', return_tensors="pt", return_attention_mask=False)
+   """''', return_tensors="pt", return_attention_mask=False).to('cuda')

outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```


I could resolve the runtime issue on `GPU` by adding cuda settiings. I beleive this would help the co-developers to try out phi-1_5. Thanks!!!
---
 README.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 843cd9d..7640174 100644
--- a/README.md
+++ b/README.md
@@ -104,11 +104,15 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 
 model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto")
 tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto")
+
+device = torch.device("cuda:0")
+model.cuda()
+
 inputs = tokenizer('''```python
 def print_prime(n):
    """
    Print all primes between 1 and n
-   """''', return_tensors="pt", return_attention_mask=False)
+   """''', return_tensors="pt", return_attention_mask=False).to('cuda')
 
 outputs = model.generate(**inputs, max_length=200)
 text = tokenizer.batch_decode(outputs)[0]