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[docs] fix not-working example code in perf_infer_gpu_one.md #36087

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merged 2 commits into from
Feb 7, 2025
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[docs] fix not-working example code in perf_infer_gpu_one.md #36087

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6b745df
bug fix
faaany Feb 7, 2025
3041b27
update memory limit
faaany Feb 7, 2025
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25 changes: 11 additions & 14 deletions docs/source/en/perf_infer_gpu_one.md
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Original file line number Diff line number Diff line change
Expand Up @@ -357,7 +357,7 @@ tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype=torch.float16).to("cuda")

input_text = "Hello my dog is cute and"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

+ with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
outputs = model.generate(**inputs)
Expand Down Expand Up @@ -431,14 +431,14 @@ To load a model in 4-bit for inference, use the `load_in_4bit` parameter. The `d
```py
from transformers import AutoModelForCausalLM

model_name = "bigscience/bloom-2b5"
model_name = "bigscience/bloom-1b7"
model_4bit = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto", load_in_4bit=True)
```

To load a model in 4-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. For example, to distribute 600MB of memory to the first GPU and 1GB of memory to the second GPU:
To load a model in 4-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. For example, to distribute 2GB of memory to the first GPU and 5GB of memory to the second GPU:

```py
max_memory_mapping = {0: "600MB", 1: "1GB"}
max_memory_mapping = {0: "2GB", 1: "5GB"}
model_name = "bigscience/bloom-3b"
model_4bit = AutoModelForCausalLM.from_pretrained(
model_name, torch_dtype="auto", device_map="auto", load_in_4bit=True, max_memory=max_memory_mapping
Expand All @@ -458,7 +458,7 @@ To load a model in 8-bit for inference, use the `load_in_8bit` parameter. The `d
```py
from transformers import AutoModelForCausalLM, BitsAndBytesConfig

model_name = "bigscience/bloom-2b5"
model_name = "bigscience/bloom-1b7"
model_8bit = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", quantization_config=BitsAndBytesConfig(load_in_8bit=True))
```

Expand All @@ -467,20 +467,20 @@ If you're loading a model in 8-bit for text generation, you should use the [`~tr
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_name = "bigscience/bloom-2b5"
model_name = "bigscience/bloom-1b7"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model_8bit = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", quantization_config=BitsAndBytesConfig(load_in_8bit=True))

prompt = "Hello, my llama is cute"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
generated_ids = model.generate(**inputs)
inputs = tokenizer(prompt, return_tensors="pt").to(model_8bit.device)
generated_ids = model_8bit.generate(**inputs)
outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
```

To load a model in 8-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. For example, to distribute 1GB of memory to the first GPU and 2GB of memory to the second GPU:
To load a model in 8-bit for inference with multiple GPUs, you can control how much GPU RAM you want to allocate to each GPU. For example, to distribute 2GB of memory to the first GPU and 5GB of memory to the second GPU:

```py
max_memory_mapping = {0: "1GB", 1: "2GB"}
max_memory_mapping = {0: "2GB", 1: "5GB"}
model_name = "bigscience/bloom-3b"
model_8bit = AutoModelForCausalLM.from_pretrained(
model_name, torch_dtype="auto", device_map="auto", load_in_8bit=True, max_memory=max_memory_mapping
Expand Down Expand Up @@ -545,11 +545,8 @@ quantization_config = BitsAndBytesConfig(
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype="auto", quantization_config=quantization_config)

# enable BetterTransformer
model = model.to_bettertransformer()

input_text = "Hello my dog is cute and"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

# enable FlashAttention
with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
Expand Down