Add initial python api doc in mddoc (2/2) (#11388)

* add PyTorch-API.md

* small change

* small change

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Co-authored-by: ATMxsp01 <shou.xu@intel.com>

docs/mddocs/PythonAPI/PyTorch-API.md

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+# IPEX-LLM PyTorch API
+
+## Optimize Model
+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.
+
+### `ipex_llm.optimize_model`_`(model, low_bit='sym_int4', optimize_llm=True, modules_to_not_convert=None, cpu_embedding=False, lightweight_bmm=False, **kwargs)`_
+
+A method to optimize any pytorch model.
+    
+- **Parameters**:
+
+  - **model**: The original PyTorch model (nn.module) 
+  
+  - **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.
+  
+  - **optimize_llm**: Whether to further optimize llm model. 
+ 
+    Default to be `True`.
+  
+  - **modules_to_not_convert**: list of str value, modules (`nn.Module`) that are skipped when conducting model optimizations. 
+  
+    Default to be `None`.
+  
+  - **cpu_embedding**: Whether to replace the Embedding layer, may need to set it to `True` when running BigDL-LLM on GPU on Windows. 
+  
+    Default to be `False`.
+  
+  - **lightweight_bmm**: Whether to replace the `torch.bmm` ops, may need to set it to `True` when running BigDL-LLM on GPU on Windows. 
+  
+    Default to be `False`.
+  
+- **Returns**: The optimized model.
+
+- **Example**:
+    ```python
+    # Take OpenAI Whisper model as an example
+    from ipex_llm import optimize_model
+    model = whisper.load_model('tiny') # Load whisper model under pytorch framework
+    model = optimize_model(model) # With only one line code change
+    # Use the optimized model without other API change
+    result = model.transcribe(audio, verbose=True, language="English")
+    # (Optional) you can also save the optimized model by calling 'save_low_bit'
+    model.save_low_bit(saved_dir)
+    ```
+
+## Load Optimized Model
+
+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.
+
+### `ipex_llm.optimize.load_low_bit`_`(model, model_path)`_
+
+Load the optimized pytorch model.
+
+- **Parameters**:
+
+  - **model**: The PyTorch model instance.
+  
+  - **model_path**: The path of saved optimized model.
+
+
+- **Returns**: The optimized model.
+
+- **Example**:
+    ```python
+    # Example 1:
+    # Take ChatGLM2-6B model as an example
+    # Make sure you have saved the optimized model by calling 'save_low_bit'
+    from ipex_llm.optimize import low_memory_init, load_low_bit
+    with low_memory_init(): # Fast and low cost by loading model on meta device
+        model = AutoModel.from_pretrained(saved_dir,
+                                          torch_dtype="auto",
+                                          trust_remote_code=True)
+    model = load_low_bit(model, saved_dir) # Load the optimized model
+    ```
+
+    ```python
+    # Example 2:
+    # If the model doesn't fit 'low_memory_init' method,
+    # alternatively, you can obtain the model instance through traditional loading method.
+    # Take OpenAI Whisper model as an example
+    # Make sure you have saved the optimized model by calling 'save_low_bit'
+    from ipex_llm.optimize import load_low_bit
+    model = whisper.load_model('tiny') # A model instance through traditional loading method
+    model = load_low_bit(model, saved_dir) # Load the optimized model
+    ```