Upstream merge

.github/scripts/spellcheck_conf/wordlist.txt

@@ -1451,4 +1451,7 @@ openhathi
 sarvam
 subtask
 acc
+OCRVQA
+OCRVQADataCollator
+ocrvqa
 langchain

README.md

@@ -1,47 +1,29 @@
 # Llama Recipes: Examples to get started using the Llama models from Meta
 <!-- markdown-link-check-disable -->
-The 'llama-recipes' repository is a companion to the [Meta Llama](https://github.com/meta-llama/llama-models) models. We support the latest version, [Llama 3.1](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), in this repository. The goal is to provide a scalable library for fine-tuning Meta Llama models, along with some example scripts and notebooks to quickly get started with using the models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama and other tools in the LLM ecosystem. The examples here showcase how to run Llama locally, in the cloud, and on-prem.
+The 'llama-recipes' repository is a companion to the [Meta Llama](https://github.com/meta-llama/llama-models) models. We support the latest version, [Llama 3.2 Vision](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD_VISION.md) and [Llama 3.2 Text](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md), in this repository. This repository contains example scripts and notebooks to get started with the models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama and other tools in the LLM ecosystem. The examples here use Llama locally, in the cloud, and on-prem.
 
 <!-- markdown-link-check-enable -->
 > [!IMPORTANT]
-> Meta Llama 3.1 has a new prompt template and special tokens.
+> Llama 3.2 follows the same prompt template as Llama 3.1, with a new special token `<|image|>` representing the input image for the multimodal models.
+> 
 > | Token | Description |
 > |---|---|
 > `<\|begin_of_text\|>` | Specifies the start of the prompt. |
+> `<\|image\|>` | Represents the image tokens passed as an input to Llama. |
 > `<\|eot_id\|>` | This token signifies the end of a turn i.e. the end of the model's interaction either with the user or tool executor. |
 > `<\|eom_id\|>` | End of Message. A message represents a possible stopping point where the model can inform the execution environment that a tool call needs to be made. |
 > `<\|python_tag\|>` | A special tag used in the model’s response to signify a tool call. |
 > `<\|finetune_right_pad_id\|>` | Used for padding text sequences in a batch to the same length. |
 > `<\|start_header_id\|>{role}<\|end_header_id\|>` | These tokens enclose the role for a particular message. The possible roles can be: system, user, assistant and ipython. |
 > `<\|end_of_text\|>` | This is equivalent to the EOS token. For multiturn-conversations it's usually unused, this token is expected to be generated only by the base models. |
 >
-> A multiturn-conversation with Meta Llama 3.1 that includes tool-calling follows this structure:
-> ```
-> <|begin_of_text|><|start_header_id|>system<|end_header_id|>
->
-> {{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
->
-> {{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
->
-> <|python_tag|>{{ model_tool_call_1 }}<|eom_id|><|start_header_id|>ipython<|end_header_id|>
->
-> {{ tool_response }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
->
-> {{model_response_based_on_tool_response}}<|eot_id|>
-> ```
-> Each message gets trailed by an `<|eot_id|>` token before a new header is started, signaling a role change.
->
-> More details on the new tokenizer and prompt template can be found [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1).
+> More details on the prompt templates for image reasoning, tool-calling and code interpreter can be found [on the documentation website](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_2).
+
 
->
-> [!NOTE]
-> The llama-recipes repository was recently refactored to promote a better developer experience of using the examples. Some files have been moved to new locations. The `src/` folder has NOT been modified, so the functionality of this repo and package is not impacted.
->
-> Make sure you update your local clone by running `git pull origin main`
 
 ## Table of Contents
 
-- [Llama Recipes: Examples to get started using the Meta Llama models from Meta](#llama-recipes-examples-to-get-started-using-the-llama-models-from-meta)
+- [Llama Recipes: Examples to get started using the Llama models from Meta](#llama-recipes-examples-to-get-started-using-the-llama-models-from-meta)
   - [Table of Contents](#table-of-contents)
   - [Getting Started](#getting-started)
     - [Prerequisites](#prerequisites)
@@ -117,23 +99,21 @@ pip install -e .[tests,auditnlg,vllm]
 ```
 
 
-### Getting the Meta Llama models
-You can find Meta Llama models on Hugging Face hub [here](https://huggingface.co/meta-llama), **where models with `hf` in the name are already converted to Hugging Face checkpoints so no further conversion is needed**. The conversion step below is only for original model weights from Meta that are hosted on Hugging Face model hub as well.
+### Getting the Llama models
+You can find Llama models on Hugging Face hub [here](https://huggingface.co/meta-llama), **where models with `hf` in the name are already converted to Hugging Face checkpoints so no further conversion is needed**. The conversion step below is only for original model weights from Meta that are hosted on Hugging Face model hub as well.
 
 #### Model conversion to Hugging Face
-The recipes and notebooks in this folder are using the Meta Llama model definition provided by Hugging Face's transformers library.
-
-Given that the original checkpoint resides under models/7B you can install all requirements and convert the checkpoint with:
+If you have the model checkpoints downloaded from the Meta website, you can convert it to the Hugging Face format with:
 
 ```bash
 ## Install Hugging Face Transformers from source
-pip freeze | grep transformers ## verify it is version 4.31.0 or higher
+pip freeze | grep transformers ## verify it is version 4.45.0 or higher
 
 git clone [email protected]:huggingface/transformers.git
 cd transformers
 pip install protobuf
 python src/transformers/models/llama/convert_llama_weights_to_hf.py \
-   --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir /output/path
+   --input_dir /path/to/downloaded/llama/weights --model_size 3B --output_dir /output/path
 ```
 
 
@@ -196,6 +176,8 @@ Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduc
 ## License
 <!-- markdown-link-check-disable -->
 
+See the License file for Meta Llama 3.2 [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) and Acceptable Use Policy [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/USE_POLICY.md)
+
 See the License file for Meta Llama 3.1 [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE) and Acceptable Use Policy [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/USE_POLICY.md)
 
 See the License file for Meta Llama 3 [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3/LICENSE) and Acceptable Use Policy [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3/USE_POLICY.md)

recipes/quickstart/finetuning/datasets/ocrvqa_dataset.py

@@ -0,0 +1,90 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the Llama 3 Community License Agreement.
+
+
+import copy
+from datasets import load_dataset
+import itertools
+import torch
+
+# check system prompt token seq or user prompt token seq is in the current token list
+def check_header(targets,seq):
+    for i in range(len(seq)-3):
+        if seq[i:i+3] in targets:
+            return True
+    return False
+def replace_target(target,seq):
+    for i in range(len(seq)-3):
+        if seq[i:i+3] == target:
+            seq[i],seq[i+1],seq[i+2] = -100,-100,-100
+    return seq
+def tokenize_dialogs(dialogs, images, processor):
+    text_prompt = processor.apply_chat_template(dialogs)
+    batch = processor(images=images, text=text_prompt,padding = True, return_tensors="pt")
+    label_list = []
+    for i in range(len(batch["input_ids"])):
+        dialog_tokens = batch["input_ids"][i].tolist()
+        labels = copy.copy(dialog_tokens)
+        eot_indices = [i for i,n in enumerate(labels) if n == 128009]
+        last_idx = 0
+        # system prompt header "<|start_header_id|>system<|end_header_id|>" has been tokenized to [128006, 9125, 128007]
+        # user prompt header "<|start_header_id|>user<|end_header_id|>" has been tokenized to [128006, 882, 128007]
+        prompt_header_seqs = [[128006, 9125, 128007],[128006, 882, 128007]]
+        for n, idx in enumerate(eot_indices):
+            current_seq = labels[last_idx:idx+1]
+            if check_header(prompt_header_seqs,current_seq):
+                # found prompt header, indicating that this seq should be masked
+                labels[last_idx:idx+1] = [-100] * (idx-last_idx+1)
+            else:
+                last_idx = idx+1
+            #  Mask all the assistant header prompt <|start_header_id|>assistant<|end_header_id|>, which has been tokenized to [128006, 78191, 128007]
+        assistant_header_seq = [128006, 78191, 128007]
+        labels = replace_target(assistant_header_seq,labels)
+        # Mask the padding token and image token 128256 
+        for i in range(len(labels)):
+            if labels[i] == processor.tokenizer.pad_token_id or labels[i] == 128256: #  128256 is image token index
+                labels[i] = -100
+        label_list.append(labels)
+    batch["labels"] = torch.tensor(label_list)
+    return batch
+
+
+def get_custom_dataset(dataset_config, processor, split, split_ratio=0.9):
+    # load_dataset will return DatasetDict that contains all the data in the train set
+    dataset_dict = load_dataset("HuggingFaceM4/the_cauldron", name="ocrvqa")
+    dataset = dataset_dict['train']
+    # Comment out the following line to use the full dataset, for quick testing only use 2000 samples
+    dataset = dataset.select(range(2000))
+    dataset = dataset.train_test_split(test_size=1-split_ratio, shuffle=True, seed=42)[split]
+    return dataset
+
+class OCRVQADataCollator:
+    def __init__(self, processor):
+        self.processor = processor
+        self.processor.tokenizer.padding_side = "right" # during training, one always uses padding on the right
+    def __call__(self, samples):
+        dialogs,images = [],[]
+        for sample in samples:
+            image_list,sample_list = sample["images"],sample["texts"]
+            if len(image_list) > 1:
+                raise ValueError("Only support one image per sample")
+            image = image_list[0].convert("RGB") # only use the first image
+            dialog = []
+            for sample_dict in sample_list:
+                if not dialog:
+                    # only append image to the first sentence
+                    dialog += [
+                    {"role":"user","content":[{"type": "image"},{"type": "text", "text": sample_dict["user"].strip()}]},
+                    {"role":"assistant","content":[{"type": "text", "text": sample_dict["assistant"].strip()}]}
+                ]
+
+                else:
+                    dialog += [
+                    {"role":"user","content":[{"type": "text", "text": sample_dict["user"].strip()}]},
+                    {"role":"assistant","content":[{"type": "text", "text": sample_dict["assistant"].strip()}]}
+                ]
+            dialogs.append(dialog)
+            images.append([image])
+        return tokenize_dialogs(dialogs,images, self.processor)
+def get_data_collator(processor):
+    return OCRVQADataCollator(processor)

recipes/quickstart/finetuning/finetune_vision_model.md

@@ -0,0 +1,33 @@
+## Fine-Tuning Meta Llama Multi Modal Models recipe
+This recipe steps you through how to finetune a Llama 3.2 vision model on the OCR VQA task using the [OCRVQA](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron/viewer/ocrvqa?row=0) dataset.
+
+**Disclaimer**: As our vision models already have a very good OCR ability, here we just use the OCRVQA dataset only for demonstration purposes of the required steps for fine-tuning our vision models with llama-recipes.
+
+### Fine-tuning steps
+
+We created an example script [ocrvqa_dataset.py](./datasets/ocrvqa_dataset.py) that can load the OCRVQA dataset with `get_custom_dataset` function, then provide OCRVQADataCollator class to process the image dataset.
+
+For **full finetuning with FSDP**, we can run the following code:
+
+```bash
+  torchrun --nnodes 1 --nproc_per_node 4  recipes/quickstart/finetuning/finetuning.py --enable_fsdp --lr 1e-5  --num_epochs 3 --batch_size_training 2 --model_name meta-llama/Llama-3.2-11B-Vision-Instruct --dist_checkpoint_root_folder ./finetuned_model --dist_checkpoint_folder fine-tuned  --use_fast_kernels --dataset "custom_dataset" --custom_dataset.test_split "test" --custom_dataset.file "recipes/quickstart/finetuning/datasets/ocrvqa_dataset.py"  --run_validation True --batching_strategy padding
+```
+
+For **LoRA finetuning with FSDP**, we can run the following code:
+
+```bash
+  torchrun --nnodes 1 --nproc_per_node 4  recipes/quickstart/finetuning/finetuning.py --enable_fsdp --lr 1e-5  --num_epochs 3 --batch_size_training 2 --model_name meta-llama/Llama-3.2-11B-Vision-Instruct --dist_checkpoint_root_folder ./finetuned_model --dist_checkpoint_folder fine-tuned  --use_fast_kernels --dataset "custom_dataset" --custom_dataset.test_split "test" --custom_dataset.file "recipes/quickstart/finetuning/datasets/ocrvqa_dataset.py"  --run_validation True --batching_strategy padding  --use_peft --peft_method lora
+```
+**Note**: `--batching_strategy padding` is needed as the vision model will not work with `packing` method.
+
+For more details about the finetuning configurations, please read the [finetuning readme](./README.md).
+
+### How to use a custom dataset to fine-tune vision model
+
+In order to use a custom dataset, please follow the steps below:
+
+1. Create a new dataset python file under `recipes/quickstart/finetuning/dataset` folder.
+2. In this python file, you need to define a `get_custom_dataset(dataset_config, processor, split, split_ratio=0.9)` function that handles the data loading.
+3. In this python file, you need to define a `get_data_collator(processor)` that returns a custom data collator that can be used by the Pytorch Data Loader.
+4. This custom data collator class must have a `__call__(self, samples)` function that converts the image and text samples into the actual inputs that vision model expects.
+5. Run the `torchrun` commend from above section, please change the `--custom_dataset.file` to the new dataset python file, adjust the learning rate accordingly.

recipes/quickstart/inference/local_inference/README.md

@@ -1,5 +1,12 @@
 # Local Inference
 
+For Multi-Modal inference we have added [multi_modal_infer.py](multi_modal_infer.py) which uses the transformers library
+
+The way to run this would be
+```
+python multi_modal_infer.py --image_path "./resources/image.jpg" --prompt_text "Describe this image" --temperature 0.5 --top_p 0.8 --model_name "meta-llama/Llama-3.2-11B-Vision-Instruct"
+```
+
 For local inference we have provided an [inference script](inference.py). Depending on the type of finetuning performed during training the [inference script](inference.py) takes different arguments.
 To finetune all model parameters the output dir of the training has to be given as --model_name argument.
 In the case of a parameter efficient method like lora the base model has to be given as --model_name and the output dir of the training has to be given as --peft_model argument.
@@ -87,4 +94,4 @@ python inference.py --model_name <training_config.output_dir> --prompt_file <tes
 
 ## Inference on large models like Meta Llama 405B
 The FP8 quantized variants of Meta Llama (i.e. meta-llama/Meta-Llama-3.1-405B-FP8 and meta-llama/Meta-Llama-3.1-405B-Instruct-FP8) can be executed on a single node with 8x80GB H100 using the scripts located in this folder.
-To run the unquantized Meta Llama 405B variants (i.e. meta-llama/Meta-Llama-3.1-405B and meta-llama/Meta-Llama-3.1-405B-Instruct) we need to use a multi-node setup for inference. The llama-recipes inference script currently does not allow multi-node inference. To run this model you can use vLLM with pipeline and tensor parallelism as showed in [this example](../../../3p_integrations/vllm/README.md).
+To run the unquantized Meta Llama 405B variants (i.e. meta-llama/Meta-Llama-3.1-405B and meta-llama/Meta-Llama-3.1-405B-Instruct) we need to use a multi-node setup for inference. The llama-recipes inference script currently does not allow multi-node inference. To run this model you can use vLLM with pipeline and tensor parallelism as showed in [this example](../../../3p_integrations/vllm/README.md).

recipes/quickstart/inference/local_inference/multi_modal_infer.py

@@ -0,0 +1,66 @@
+import os
+import sys
+import argparse
+from PIL import Image as PIL_Image
+import torch
+from transformers import MllamaForConditionalGeneration, MllamaProcessor
+
+
+# Constants
+DEFAULT_MODEL = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+
+
+def load_model_and_processor(model_name: str, hf_token: str):
+    """
+    Load the model and processor based on the 11B or 90B model.
+    """
+    model = MllamaForConditionalGeneration.from_pretrained(model_name, device_map="auto", torch_dtype=torch.bfloat16, token=hf_token)
+    processor = MllamaProcessor.from_pretrained(model_name, token=hf_token)
+    return model, processor
+
+
+def process_image(image_path: str) -> PIL_Image.Image:
+    """
+    Open and convert an image from the specified path.
+    """
+    if not os.path.exists(image_path):
+        print(f"The image file '{image_path}' does not exist.")
+        sys.exit(1)
+    with open(image_path, "rb") as f:
+        return PIL_Image.open(f).convert("RGB")
+
+
+def generate_text_from_image(model, processor, image, prompt_text: str, temperature: float, top_p: float):
+    """
+    Generate text from an image using the model and processor.
+    """
+    conversation = [
+        {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": prompt_text}]}
+    ]
+    prompt = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False)
+    inputs = processor(prompt, image, return_tensors="pt").to(model.device)
+    output = model.generate(**inputs, temperature=temperature, top_p=top_p, max_new_tokens=512)
+    return processor.decode(output[0])[len(prompt):]
+
+
+def main(image_path: str, prompt_text: str, temperature: float, top_p: float, model_name: str, hf_token: str):
+    """
+    Call all the functions. 
+    """
+    model, processor = load_model_and_processor(model_name, hf_token)
+    image = process_image(image_path)
+    result = generate_text_from_image(model, processor, image, prompt_text, temperature, top_p)
+    print("Generated Text: " + result)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Generate text from an image and prompt using the 3.2 MM Llama model.")
+    parser.add_argument("--image_path", type=str, help="Path to the image file")
+    parser.add_argument("--prompt_text", type=str, help="Prompt text to describe the image")
+    parser.add_argument("--temperature", type=float, default=0.7, help="Temperature for generation (default: 0.7)")
+    parser.add_argument("--top_p", type=float, default=0.9, help="Top p for generation (default: 0.9)")
+    parser.add_argument("--model_name", type=str, default=DEFAULT_MODEL, help=f"Model name (default: '{DEFAULT_MODEL}')")
+    parser.add_argument("--hf_token", type=str, required=True, help="Hugging Face token for authentication")
+
+    args = parser.parse_args()
+    main(args.image_path, args.prompt_text, args.temperature, args.top_p, args.model_name, args.hf_token)