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[docs] no hard coding cuda as bnb has multi-backend support (#35867)

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* change cuda to DEVICE

* Update docs/source/en/llm_tutorial.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

---------

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
This commit is contained in:
Fanli Lin 2025-02-06 00:20:02 +08:00 committed by MekkCyber
parent 1e929f357f
commit 741865eebf
1 changed files with 11 additions and 8 deletions
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19
docs/source/en/llm_tutorial.md
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@ -40,6 +40,7 @@ Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install transformers bitsandbytes>=0.39.0 -q
```
Bitsandbytes supports multiple backends in addition to CUDA-based GPUs. Refer to the multi-backend installation [guide](https://huggingface.co/docs/bitsandbytes/main/en/installation#multi-backend) to learn more.
## Generate text
@ -101,9 +102,11 @@ Next, you need to preprocess your text input with a [tokenizer](tokenizer_summar
```py
>>> from transformers import AutoTokenizer
>>> from accelerate.test_utils.testing import get_backend
>>> DEVICE, _, _ = get_backend() # automatically detects the underlying device type (CUDA, CPU, XPU, MPS, etc.)
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", padding_side="left")
>>> model_inputs = tokenizer(["A list of colors: red, blue"], return_tensors="pt").to("cuda")
>>> model_inputs = tokenizer(["A list of colors: red, blue"], return_tensors="pt").to(DEVICE)
```
The `model_inputs` variable holds the tokenized text input, as well as the attention mask. While [`~generation.GenerationMixin.generate`] does its best effort to infer the attention mask when it is not passed, we recommend passing it whenever possible for optimal results.
@ -122,7 +125,7 @@ Finally, you don't need to do it one sequence at a time! You can batch your inpu
>>> tokenizer.pad_token = tokenizer.eos_token # Most LLMs don't have a pad token by default
>>> model_inputs = tokenizer(
... ["A list of colors: red, blue", "Portugal is"], return_tensors="pt", padding=True
... ).to("cuda")
... ).to(DEVICE)
>>> generated_ids = model.generate(**model_inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
['A list of colors: red, blue, green, yellow, orange, purple, pink,',
@ -152,7 +155,7 @@ If not specified in the [`~generation.GenerationConfig`] file, `generate` return
```py
>>> model_inputs = tokenizer(["A sequence of numbers: 1, 2"], return_tensors="pt").to("cuda")
>>> model_inputs = tokenizer(["A sequence of numbers: 1, 2"], return_tensors="pt").to(DEVICE)
>>> # By default, the output will contain up to 20 tokens
>>> generated_ids = model.generate(**model_inputs)
@ -174,7 +177,7 @@ By default, and unless specified in the [`~generation.GenerationConfig`] file, `
>>> from transformers import set_seed
>>> set_seed(42)
>>> model_inputs = tokenizer(["I am a cat."], return_tensors="pt").to("cuda")
>>> model_inputs = tokenizer(["I am a cat."], return_tensors="pt").to(DEVICE)
>>> # LLM + greedy decoding = repetitive, boring output
>>> generated_ids = model.generate(**model_inputs)
@ -196,7 +199,7 @@ LLMs are [decoder-only](https://huggingface.co/learn/nlp-course/chapter1/6?fw=pt
>>> # which is shorter, has padding on the right side. Generation fails to capture the logic.
>>> model_inputs = tokenizer(
... ["1, 2, 3", "A, B, C, D, E"], padding=True, return_tensors="pt"
... ).to("cuda")
... ).to(DEVICE)
>>> generated_ids = model.generate(**model_inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
'1, 2, 33333333333'
@ -206,7 +209,7 @@ LLMs are [decoder-only](https://huggingface.co/learn/nlp-course/chapter1/6?fw=pt
>>> tokenizer.pad_token = tokenizer.eos_token # Most LLMs don't have a pad token by default
>>> model_inputs = tokenizer(
... ["1, 2, 3", "A, B, C, D, E"], padding=True, return_tensors="pt"
... ).to("cuda")
... ).to(DEVICE)
>>> generated_ids = model.generate(**model_inputs)
>>> tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
'1, 2, 3, 4, 5, 6,'
@ -223,7 +226,7 @@ Some models and tasks expect a certain input prompt format to work properly. Whe
... )
>>> set_seed(0)
>>> prompt = """How many helicopters can a human eat in one sitting? Reply as a thug."""
>>> model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
>>> model_inputs = tokenizer([prompt], return_tensors="pt").to(DEVICE)
>>> input_length = model_inputs.input_ids.shape[1]
>>> generated_ids = model.generate(**model_inputs, max_new_tokens=20)
>>> print(tokenizer.batch_decode(generated_ids[:, input_length:], skip_special_tokens=True)[0])
@ -239,7 +242,7 @@ Some models and tasks expect a certain input prompt format to work properly. Whe
... },
... {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
... ]
>>> model_inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
>>> model_inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(DEVICE)
>>> input_length = model_inputs.shape[1]
>>> generated_ids = model.generate(model_inputs, do_sample=True, max_new_tokens=20)
>>> print(tokenizer.batch_decode(generated_ids[:, input_length:], skip_special_tokens=True)[0])