4cb9a4728e
* Small fixes * Add initial api doc index * Change index.md -> README.md * Fix on API links
79 lines
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3.5 KiB
Markdown
79 lines
No EOL
3.5 KiB
Markdown
# IPEX-LLM PyTorch API
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## Optimize Model
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You can run any PyTorch model with `optimize_model` through only one-line code change to benefit from IPEX-LLM optimization, regardless of the library or API you are using.
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### `ipex_llm.optimize_model`_`(model, low_bit='sym_int4', optimize_llm=True, modules_to_not_convert=None, cpu_embedding=False, lightweight_bmm=False, **kwargs)`_
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A method to optimize any pytorch model.
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- **Parameters**:
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- **model**: The original PyTorch model (nn.module)
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- **low_bit**: str value, options are `'sym_int4'`, `'asym_int4'`, `'sym_int5'`, `'asym_int5'`, `'sym_int8'`, `'nf3'`, `'nf4'`, `'fp4'`, `'fp8'`, `'fp8_e4m3'`, `'fp8_e5m2'`, `'fp16'` or `'bf16'`, `'sym_int4'` means symmetric int 4, `'asym_int4'` means asymmetric int 4, `'nf4'` means 4-bit NormalFloat, etc. Relevant low bit optimizations will be applied to the model.
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- **optimize_llm**: Whether to further optimize llm model. Default to be `True`.
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- **modules_to_not_convert**: list of str value, modules (`nn.Module`) that are skipped when conducting model optimizations. Default to be `None`.
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- **cpu_embedding**: Whether to replace the Embedding layer, may need to set it to `True` when running IPEX-LLM on GPU. Default to be `False`.
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- **lightweight_bmm**: Whether to replace the `torch.bmm` ops, may need to set it to `True` when running IPEX-LLM on GPU on Windows. Default to be `False`.
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- **Returns**: The optimized model.
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- **Example**:
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```python
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# Take OpenAI Whisper model as an example
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from ipex_llm import optimize_model
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model = whisper.load_model('tiny') # Load whisper model under pytorch framework
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model = optimize_model(model) # With only one line code change
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# Use the optimized model without other API change
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result = model.transcribe(audio, verbose=True, language="English")
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# (Optional) you can also save the optimized model by calling 'save_low_bit'
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model.save_low_bit(saved_dir)
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```
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## Load Optimized Model
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To avoid high resource consumption during the loading processes of the original model, we provide save/load API to support the saving of model after low-bit optimization and the loading of the saved low-bit model. Saving and loading operations are platform-independent, regardless of their operating systems.
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### `ipex_llm.optimize.load_low_bit`_`(model, model_path)`_
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Load the optimized pytorch model.
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- **Parameters**:
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- **model**: The PyTorch model instance.
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- **model_path**: The path of saved optimized model.
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- **Returns**: The optimized model.
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- **Example**:
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```python
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# Example 1:
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# Take ChatGLM2-6B model as an example
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# Make sure you have saved the optimized model by calling 'save_low_bit'
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from ipex_llm.optimize import low_memory_init, load_low_bit
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with low_memory_init(): # Fast and low cost by loading model on meta device
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model = AutoModel.from_pretrained(saved_dir,
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torch_dtype="auto",
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trust_remote_code=True)
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model = load_low_bit(model, saved_dir) # Load the optimized model
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```
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```python
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# Example 2:
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# If the model doesn't fit 'low_memory_init' method,
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# alternatively, you can obtain the model instance through traditional loading method.
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# Take OpenAI Whisper model as an example
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# Make sure you have saved the optimized model by calling 'save_low_bit'
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from ipex_llm.optimize import load_low_bit
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model = whisper.load_model('tiny') # A model instance through traditional loading method
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model = load_low_bit(model, saved_dir) # Load the optimized model
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``` |