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Update torchao.md: use auto-compilation (#35490)

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* Update torchao.md: use auto-compilation

* Update torchao.md: indicate updating transformers to the latest

---------

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
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Martin
2025-01-14 18:33:48 +08:00
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parent 4b8d1f7fca
commit 715fdd6459
1 changed files with 7 additions and 10 deletions
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docs/source/en/quantization/torchao.md
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@@ -16,7 +16,8 @@ rendered properly in your Markdown viewer.
Before you begin, make sure the following libraries are installed with their latest version: Before you begin, make sure the following libraries are installed with their latest version:
```bash ```bash
pip install --upgrade torch torchao # Updating 🤗 Transformers to the latest version, as the example script below uses the new auto compilation
pip install --upgrade torch torchao transformers
``` ```
By default, the weights are loaded in full precision (torch.float32) regardless of the actual data type the weights are stored in such as torch.float16. Set `torch_dtype="auto"` to load the weights in the data type defined in a model's `config.json` file to automatically load the most memory-optimal data type. By default, the weights are loaded in full precision (torch.float32) regardless of the actual data type the weights are stored in such as torch.float16. Set `torch_dtype="auto"` to load the weights in the data type defined in a model's `config.json` file to automatically load the most memory-optimal data type.
@@ -35,12 +36,8 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
input_text = "What are we having for dinner?" input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
# compile the quantized model to get speedup # auto-compile the quantized model with `cache_implementation="static"` to get speedup
import torchao output = quantized_model.generate(**input_ids, max_new_tokens=10, cache_implementation="static")
torchao.quantization.utils.recommended_inductor_config_setter()
quantized_model = torch.compile(quantized_model, mode="max-autotune")
output = quantized_model.generate(**input_ids, max_new_tokens=10)
print(tokenizer.decode(output[0], skip_special_tokens=True)) print(tokenizer.decode(output[0], skip_special_tokens=True))
# benchmark the performance # benchmark the performance
@@ -59,11 +56,11 @@ def benchmark_fn(f, *args, **kwargs):
return f"{(t0.blocked_autorange().mean):.3f}" return f"{(t0.blocked_autorange().mean):.3f}"
MAX_NEW_TOKENS = 1000 MAX_NEW_TOKENS = 1000
print("int4wo-128 model:", benchmark_fn(quantized_model.generate, **input_ids, max_new_tokens=MAX_NEW_TOKENS)) print("int4wo-128 model:", benchmark_fn(quantized_model.generate, **input_ids, max_new_tokens=MAX_NEW_TOKENS, cache_implementation="static"))
bf16_model = AutoModelForCausalLM.from_pretrained(model_name, device_map="cuda", torch_dtype=torch.bfloat16) bf16_model = AutoModelForCausalLM.from_pretrained(model_name, device_map="cuda", torch_dtype=torch.bfloat16)
bf16_model = torch.compile(bf16_model, mode="max-autotune") output = bf16_model.generate(**input_ids, max_new_tokens=10, cache_implementation="static") # auto-compile
print("bf16 model:", benchmark_fn(bf16_model.generate, **input_ids, max_new_tokens=MAX_NEW_TOKENS)) print("bf16 model:", benchmark_fn(bf16_model.generate, **input_ids, max_new_tokens=MAX_NEW_TOKENS, cache_implementation="static"))
``` ```