Merge branch 'main' into readme-fix

.github/scripts/spellcheck_conf/wordlist.txt

@@ -1400,6 +1400,19 @@ sqlite
 customerservice
 fn
 ExecuTorch
+LLMScore
+RecursiveCharacterTextSplitter
+TPD
+TPM
+Tianjun
+Zhang
+distractor
+distractors
+frac
+numRefusal
+totalQA
+DirectoryLoader
+SitemapLoader
 nf
 quant
 DLAI

recipes/quickstart/finetuning/datasets/raft_dataset.py

@@ -0,0 +1,97 @@
+# 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
+
+# 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_dialog(dialog, tokenizer):
+    # If vocab size is above 128000, use the chat template to generate the tokens as it is from Llama 3 family models
+    if tokenizer.vocab_size >= 128000:
+        dialog_tokens = tokenizer.apply_chat_template(dialog)
+        eot_indices = [i for i,n in enumerate(dialog_tokens) if n == 128009]
+        labels = copy.copy(dialog_tokens)
+        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
+        # Lastly 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)
+        dialog_tokens = [dialog_tokens]
+        labels_tokens = [labels]
+    else:
+        raise Exception("This raft_dataset only supports Llama 3 family models, please make sure the tokenizer is from Llama 3 family models.")
+
+    combined_tokens = {
+        "input_ids": list(itertools.chain(*(t for t in dialog_tokens))),
+        "labels": list(itertools.chain(*(t for t in labels_tokens))),
+    }
+
+    return dict(combined_tokens, attention_mask=[1]*len(combined_tokens["input_ids"]))
+def raft_tokenize(q_a_pair, tokenizer):
+    end_tag = "</DOCUMENT>"
+    # find the last end_tag in the instruction, the rest is the question
+    try:
+        index =q_a_pair["instruction"].rindex(end_tag)+len(end_tag)
+    except ValueError:
+        print(q_a_pair["instruction"])
+        raise Exception("The instruction does not contain the end tag <\/DOCUMENT>")
+    # all the lines after end_tag are the question
+    question = q_a_pair["instruction"][index:].strip()
+    # all the lines before end_tag are the context
+    documents = q_a_pair["instruction"][:index].strip() 
+    # output is the label
+    answer = q_a_pair["output"]
+    system_prompt = "You are a helpful chatbot who can provide an answer to every questions from the user given a relevant context."
+    user_prompt = """
+        Question: {question}\nContext: {context}\n
+        Answer this question using the information given by multiple documents in the context above. Here are the things to pay attention to:
+        - The context contains many documents, each document starts with <DOCUMENT> and ends </DOCUMENT>.
+        - First provide step-by-step reasoning on how to answer the question.
+        - In the reasoning, if you need to copy paste some sentences from the context, include them in ##begin_quote## and ##end_quote##. This would mean that things outside of ##begin_quote## and ##end_quote## are not directly copy paste from the context.
+        - End your response with final answer in the form <ANSWER>: $answer, the answer should less than 60 words.
+        You MUST begin your final answer with the tag "<ANSWER>:".
+    """.format(question=question, context=documents)
+
+    chat = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": user_prompt},
+    {"role": "assistant", "content": answer}
+    ]
+    return tokenize_dialog(chat, tokenizer)
+
+
+def get_custom_dataset(dataset_config, tokenizer, split, split_ratio=0.9):
+    # load_dataset will return DatasetDict that contains all the data in the train set
+    dataset_dict = load_dataset('json', data_files=dataset_config.data_path)
+    dataset = dataset_dict['train']
+    dataset = dataset.train_test_split(test_size=1-split_ratio, shuffle=True, seed=42)
+
+    dataset = dataset[split].map(lambda sample: {
+        "instruction": sample["instruction"],
+        "output": sample["cot_answer"],
+        },
+        batched=True,
+    )
+    dataset = dataset.map(lambda x: raft_tokenize(x, tokenizer))
+    return dataset

recipes/responsible_ai/prompt_guard/prompt_guard_tutorial.ipynb

@@ -789,7 +789,7 @@
    "metadata": {},
    "source": [
     "\n",
-    "One good way to quickly obtain labeled training data for a use case is to use the original, non-fine tuned model itself to highlight risky examples to label, while drawing random negatives from below a score threshold. This helps address the class imbalance (attacks and risky prompts can be a very small percentage of all prompts) and includes false positive examples (which tend to be very valuable to train on) in the dataset. The use of synthetic data for specific "
+    "One good way to quickly obtain labeled training data for a use case is to use the original, non-fine tuned model itself to highlight risky examples to label, while drawing random negatives from below a score threshold. This helps address the class imbalance (attacks and risky prompts can be a very small percentage of all prompts) and includes false positive examples (which tend to be very valuable to train on) in the dataset. Generating synthetic fine-tuning data for specific use cases can also be an effective strategy."
    ]
   }
  ],

recipes/use_cases/end2end-recipes/RAFT-Chatbot/config.py

@@ -0,0 +1,10 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
+
+import yaml
+
+def load_config(config_path: str = "./config.yaml"):
+    # Read the YAML configuration file
+    with open(config_path, "r") as file:
+        config = yaml.safe_load(file)
+    return config