Update fine-tuning guide: title, improve readibility in code blocks, …

…fix typos (ROCm#3222)

* Fix typo

* Add torchtune link

* Add newlines before comments in code blocks for readability

* Update title

docs/how-to/fine-tuning-llms/index.rst

@@ -2,9 +2,9 @@
    :description: How to fine-tune LLMs with ROCm
    :keywords: ROCm, LLM, fine-tuning, usage, tutorial
 
-**************************
-Fine-tuning LLMs with ROCm
-**************************
+*******************************************
+Fine-tuning LLMs and inference optimization
+*******************************************
 
 ROCm empowers the fine-tuning and optimization of large language models, making them accessible and efficient for
 specialized tasks. ROCm supports the broader AI ecosystem to ensure seamless integration with open frameworks,

docs/how-to/fine-tuning-llms/llm-inference-frameworks.rst

@@ -32,7 +32,7 @@ Installing vLLM
 
    .. code-block:: shell
 
-      # Install from the source
+      # Install from source
       git clone https://github.com/ROCm/vllm.git    
       cd vllm
       PYTORCH_ROCM_ARCH=gfx942 python setup.py install #MI300 series

docs/how-to/fine-tuning-llms/model-acceleration-libraries.rst

@@ -40,7 +40,7 @@ ROCm provides two different implementations of Flash Attention 2 modules. They c
 
       .. code-block:: shell
 
-         # Install from the source
+         # Install from source
          git clone https://github.com/ROCm/flash-attention.git
          cd flash-attention/
          GPU_ARCHS=gfx942 python setup.py install #MI300 series
@@ -156,7 +156,7 @@ of the PyTorch compilation.
 
 .. code-block:: python
 
-   # Sample script to run LLM with the static key-value cache and pytorch compilation
+   # Sample script to run LLM with the static key-value cache and PyTorch compilation
    from transformers import AutoModelForCausalLM, AutoTokenizer, StaticCache
    import torch
    from typing import Optional
@@ -180,7 +180,8 @@ of the PyTorch compilation.
        return new_token
    
    batch_size, seq_length = inputs["input_ids"].shape
-   # static key-value cache
+
+   # Static key-value cache
    max_cache_length = 1024
    max_new_tokens = 10
    model._setup_cache(StaticCache, batch_size, max_cache_len=max_cache_length)
@@ -190,6 +191,7 @@ of the PyTorch compilation.
    
    logits = model(**inputs, cache_position=cache_position, return_dict=False, use_cache=True)[0]
    next_token = torch.argmax(logits[:, -1], dim=-1)[:, None]
+
    # torch compilation
    decode_one_tokens = torch.compile(decode_one_tokens, mode="max-autotune-no-cudagraphs",fullgraph=True)
    
@@ -221,10 +223,10 @@ page describes the options.
 
 .. code-block:: python
 
-   # To turn on TunableOps, simply set this environmental variable
+   # To turn on TunableOp, simply set this environment variable
    export PYTORCH_TUNABLEOP_ENABLED=1
    
-   # python
+   # Python
    import torch
    import torch.nn as nn
    import torch.nn.functional as F

docs/how-to/fine-tuning-llms/model-quantization.rst

@@ -32,19 +32,19 @@ The AutoGPTQ library implements the GPTQ algorithm.
 
    .. code-block:: shell
 
-      # This will install pre-built wheel for a specific ROCm version  
+      # This will install pre-built wheel for a specific ROCm version.
       
       pip install auto-gptq --no-build-isolation --extra-index-url https://huggingface.github.io/autogptq-index/whl/rocm573/
 
    Or, install AutoGPTQ from source for the appropriate ROCm version (for example, ROCm 6.1).
 
    .. code-block:: shell
 
-      # Clone the source code
+      # Clone the source code.
       git clone https://github.com/AutoGPTQ/AutoGPTQ.git
       cd AutoGPTQ
       
-      # Speed up the compilation by specifying PYTORCH_ROCM_ARCH to target device 
+      # Speed up the compilation by specifying PYTORCH_ROCM_ARCH to target device.
       PYTORCH_ROCM_ARCH=gfx942 ROCM_VERSION=6.1 pip install .
       
       # Show the package after the installation 
@@ -93,12 +93,14 @@ Using GPTQ with AutoGPTQ
 
    .. code-block:: python
 
-      # import auto_gptq class
+      # Import auto_gptq class.
       from auto_gptq import AutoGPTQForCausalLM
-      # load non-quantized model
+
+      # Load non-quantized model.
       base_model = AutoGPTQForCausalLM.from_pretrained(base_model_name, quantize_config, device_map = "auto")
       base_model.quantize(examples)
-      # save quantized model
+
+      # Save quantized model.
       base_model.save_quantized(quantized_model_name)
 
 Using GPTQ with Hugging Face Transformers
@@ -201,7 +203,7 @@ Installing bitsandbytes
 Using bitsandbytes primitives
 -----------------------------
 
-To get started with bitsandbytes primitives, use the following code a reference.
+To get started with bitsandbytes primitives, use the following code as reference.
 
 .. code-block:: python
 
@@ -230,7 +232,7 @@ To load a Transformers model in 4-bit, set ``load_int_4bt=true`` in ``BitsAndByt
            device_map="auto", 
            quantization_config=quantization_config)
    
-   # check the memory footprint with get_memory_footprint method
+   # Check the memory footprint with get_memory_footprint method
    print(bnb_model_4bit.get_memory_footprint())
 
 To load a model in 8-bit for inference, use the ``load_in_8bit`` option.

docs/how-to/fine-tuning-llms/multi-gpu-fine-tuning-and-inference.rst

@@ -130,8 +130,8 @@ After loading the model in this way, the model is fully ready to use the resourc
 torchtune for fine-tuning and inference
 =============================================
 
-torchtune is a PyTorch-native library for easy single and multi-accelerator or GPU model fine-tuning and inference with
-LLMs.
+`torchtune <https://pytorch.org/torchtune/main/>`_ is a PyTorch-native library for easy single and multi-accelerator or
+GPU model fine-tuning and inference with LLMs.
 
 #. Install torchtune using pip.
 
@@ -157,80 +157,80 @@ LLMs.
       subcommands:
         {download,ls,cp,run,validate}
 
-torchtune recipes are designed around easily composable components and workable training loops, with minimal abstraction
-getting in the way of fine-tuning. Run ``tune ls`` to show built-in torchtune configuration recipes.
-
-.. code-block:: shell
-
-   RECIPE                                   CONFIG
-   full_finetune_single_device              llama2/7B_full_low_memory
-                                            llama3/8B_full_single_device
-                                            mistral/7B_full_low_memory
-   full_finetune_distributed                llama2/7B_full
-                                            llama2/13B_full
-                                            llama3/8B_full
-                                            mistral/7B_full
-                                            gemma/2B_full
-   lora_finetune_single_device              llama2/7B_lora_single_device
-                                            llama2/7B_qlora_single_device
-                                            llama3/8B_lora_single_device
-                                            llama3/8B_qlora_single_device
-                                            llama2/13B_qlora_single_device
-                                            mistral/7B_lora_single_device
-
-The ``RECIPE`` column shows the easy-to-use and workable fine-tuning and inference recipes for popular fine-tuning
-techniques (such as LoRA). The ``CONFIG`` column lists the YAML configurations for easily configuring training,
-evaluation, quantization, or inference recipes.
-
-The snippet shows the architecture of a model's YAML configuration file:
-
-.. code-block:: yaml
-
-   # Model Arguments
-   model:
-     _component_: torchtune.models.llama2.lora_llama2_7b
-     lora_attn_modules: ['q_proj', 'v_proj']
-     apply_lora_to_mlp: False
-     apply_lora_to_output: False
-     lora_rank: 8
-     lora_alpha: 16
-   
-   tokenizer:
-     _component_: torchtune.models.llama2.llama2_tokenizer
-     path: /tmp/Llama-2-7b-hf/tokenizer.model
-   
-   # Dataset and Sampler
-   dataset:
-     _component_: torchtune.datasets.alpaca_cleaned_dataset
-     train_on_input: True
+#. torchtune recipes are designed around easily composable components and workable training loops, with minimal abstraction
+   getting in the way of fine-tuning. Run ``tune ls`` to show built-in torchtune configuration recipes.
+
+   .. code-block:: shell
+
+      RECIPE                                   CONFIG
+      full_finetune_single_device              llama2/7B_full_low_memory
+                                               llama3/8B_full_single_device
+                                               mistral/7B_full_low_memory
+      full_finetune_distributed                llama2/7B_full
+                                               llama2/13B_full
+                                               llama3/8B_full
+                                               mistral/7B_full
+                                               gemma/2B_full
+      lora_finetune_single_device              llama2/7B_lora_single_device
+                                               llama2/7B_qlora_single_device
+                                               llama3/8B_lora_single_device
+                                               llama3/8B_qlora_single_device
+                                               llama2/13B_qlora_single_device
+                                               mistral/7B_lora_single_device
+
+   The ``RECIPE`` column shows the easy-to-use and workable fine-tuning and inference recipes for popular fine-tuning
+   techniques (such as LoRA). The ``CONFIG`` column lists the YAML configurations for easily configuring training,
+   evaluation, quantization, or inference recipes.
+
+   The snippet shows the architecture of a model's YAML configuration file:
+
+   .. code-block:: yaml
+
+      # Model arguments
+      model:
+        _component_: torchtune.models.llama2.lora_llama2_7b
+        lora_attn_modules: ['q_proj', 'v_proj']
+        apply_lora_to_mlp: False
+        apply_lora_to_output: False
+        lora_rank: 8
+        lora_alpha: 16
+      
+      tokenizer:
+        _component_: torchtune.models.llama2.llama2_tokenizer
+        path: /tmp/Llama-2-7b-hf/tokenizer.model
+      
+      # Dataset and sampler
+      dataset:
+        _component_: torchtune.datasets.alpaca_cleaned_dataset
+        train_on_input: True
 
-This configuration file defines the fine-tuning base model path, data set, hyper-parameters for optimizer and scheduler,
-and training data type. To download the base model for fine-tuning, run the following command:
+#. This configuration file defines the fine-tuning base model path, data set, hyper-parameters for optimizer and scheduler,
+   and training data type. To download the base model for fine-tuning, run the following command:
 
-.. code-block:: shell
+   .. code-block:: shell
 
-   tune download meta-llama/Llama-2-7b-hf --output-dir /tmp/Llama-2-7b-hf --hf-token
+      tune download meta-llama/Llama-2-7b-hf --output-dir /tmp/Llama-2-7b-hf --hf-token
 
-The output directory argument for ``--output-dir`` should map the model path specified in YAML config file.
+   The output directory argument for ``--output-dir`` should map the model path specified in YAML config file.
 
-To launch ``lora_finetune_distributed`` on four devices, run the following
-command:
+#. To launch ``lora_finetune_distributed`` on four devices, run the following
+   command:
 
-.. code-block:: shell
+   .. code-block:: shell
 
-   tune run --nnodes 1 --nproc_per_node 4 lora_finetune_distributed --config llama2/7B_lora
+      tune run --nnodes 1 --nproc_per_node 4 lora_finetune_distributed --config llama2/7B_lora
 
-If successful, you should something like the following output:
+   If successful, you should something like the following output:
 
-.. code-block:: shell
+   .. code-block:: shell
 
-   INFO:torchtune.utils.logging:FSDP is enabled. Instantiating Model on CPU for Rank 0 ...
-   INFO:torchtune.utils.logging:Model instantiation took 7.32 secs
-   INFO:torchtune.utils.logging:Memory Stats after model init:
-   {'peak_memory_active': 9.478172672, 'peak_memory_alloc': 8.953868288, 'peak_memory_reserved': 11.112808448}
-   INFO:torchtune.utils.logging:Optimizer and loss are initialized.
-   INFO:torchtune.utils.logging:Dataset and Sampler are initialized.
-   INFO:torchtune.utils.logging:Learning rate scheduler is initialized.
-   1|111|Loss: 1.5790324211120605:   7%|█                                          | 114/1618
+      INFO:torchtune.utils.logging:FSDP is enabled. Instantiating Model on CPU for Rank 0 ...
+      INFO:torchtune.utils.logging:Model instantiation took 7.32 secs
+      INFO:torchtune.utils.logging:Memory Stats after model init:
+      {'peak_memory_active': 9.478172672, 'peak_memory_alloc': 8.953868288, 'peak_memory_reserved': 11.112808448}
+      INFO:torchtune.utils.logging:Optimizer and loss are initialized.
+      INFO:torchtune.utils.logging:Dataset and Sampler are initialized.
+      INFO:torchtune.utils.logging:Learning rate scheduler is initialized.
+      1|111|Loss: 1.5790324211120605:   7%|█                                          | 114/1618
 
 Read more about inference frameworks in :doc:`LLM inference frameworks <llm-inference-frameworks>`.

docs/how-to/fine-tuning-llms/overview.rst

@@ -7,7 +7,7 @@ Conceptual overview of fine-tuning LLMs
 ***************************************
 
 Large language models (LLMs) are trained on massive amounts of text data to generate coherent and fluent text. The
-underlying *transformer* architecture is the fundamental building block of all LLMs. Transformers serve as the
+underlying *transformer* architecture is the fundamental building block of all LLMs. Transformers 
 enable LLMs to understand and generate text by capturing contextual relationships and long-range dependencies. To better
 understand the philosophy of the transformer architecture, review the foundational
 `Attention is all you need <https://arxiv.org/pdf/1706.03762.pdf>`_ paper.
@@ -60,7 +60,7 @@ overcome this issue of high memory consumption.
 LoRA accelerates the adjustment process and reduces related memory costs. To be precise, LoRA decomposes the portion of
 weight changes :math:`ΔW` into high-precision low-rank representations, which do not require the calculations of all
 :math:`ΔW`. It learns the decomposition representation of :math:`ΔW` during training, as shown in
-:ref:`the weight update diagram <fine-tuning-llms-concept-challenge>`. This is how LoRA saves on
+the :ref:`weight update diagram <fine-tuning-llms-concept-challenge>`. This is how LoRA saves on
 computing resources.
 
 LoRA is integrated into the `Hugging Face Parameter-Efficient Fine-Tuning (PEFT)