feat: Add support for jinja based template rendering of the dataset

docs/advanced-data-preprocessing.md

@@ -210,6 +210,8 @@ This library currently supports the following [preexisting data handlers](https:
     Formats a dataset by appending an EOS token to a specified field.
  - `apply_custom_data_formatting_template`:
     Applies a custom template (e.g., Alpaca style) to format dataset elements.
+ - `apply_custom_data_formatting_jinja_template`:
+    Applies a custom jinja template (e.g., Alpaca style) to format dataset elements.
  - `apply_tokenizer_chat_template`:
     Uses a tokenizer's chat template to preprocess dataset elements, good for single/multi turn chat templates.
 

tests/artifacts/predefined_data_configs/__init__.py

@@ -22,6 +22,9 @@
 DATA_CONFIG_APPLY_CUSTOM_TEMPLATE_YAML = os.path.join(
     PREDEFINED_DATA_CONFIGS, "apply_custom_template.yaml"
 )
+DATA_CONFIG_APPLY_CUSTOM_JINJA_TEMPLATE_YAML = os.path.join(
+    PREDEFINED_DATA_CONFIGS, "apply_custom_jinja_template.yaml"
+)
 DATA_CONFIG_PRETOKENIZE_JSON_DATA_YAML = os.path.join(
     PREDEFINED_DATA_CONFIGS, "pretokenized_json_data.yaml"
 )

tests/artifacts/predefined_data_configs/apply_custom_jinja_template.yaml

@@ -0,0 +1,14 @@
+dataprocessor:
+    type: default
+datasets:
+  - name: apply_custom_data_jinja_template
+    data_paths:
+      - "FILE_PATH"
+    data_handlers:
+      - name: apply_custom_data_formatting_jinja_template
+        arguments:
+          remove_columns: all
+          batched: false
+          fn_kwargs:
+            dataset_text_field: "dataset_text_field"
+            template: "dataset_template"

tests/data/test_data_handlers.py

@@ -25,6 +25,7 @@
 
 # Local
 from tuning.data.data_handlers import (
+    apply_custom_data_formatting_jinja_template,
     apply_custom_data_formatting_template,
     combine_sequence,
 )
@@ -57,6 +58,32 @@ def test_apply_custom_formatting_template():
     assert formatted_dataset["train"][0][formatted_dataset_field] == expected_response
 
 
+def test_apply_custom_formatting_jinja_template():
+    json_dataset = datasets.load_dataset(
+        "json", data_files=TWITTER_COMPLAINTS_DATA_JSONL
+    )
+    template = "### Input: {{Tweet text}} \n\n ### Response: {{text_label}}"
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    formatted_dataset_field = "formatted_data_field"
+    formatted_dataset = json_dataset.map(
+        apply_custom_data_formatting_jinja_template,
+        fn_kwargs={
+            "tokenizer": tokenizer,
+            "dataset_text_field": formatted_dataset_field,
+            "template": template,
+        },
+    )
+    # First response from the data file that is read.
+    expected_response = (
+        "### Input: @HMRCcustomers No this is my first job"
+        + " \n\n ### Response: no complaint"
+        + tokenizer.eos_token
+    )
+
+    assert formatted_dataset_field in formatted_dataset["train"][0]
+    assert formatted_dataset["train"][0][formatted_dataset_field] == expected_response
+
+
 def test_apply_custom_formatting_template_gives_error_with_wrong_keys():
     """Tests that the formatting function will throw error if wrong keys are passed to template"""
     json_dataset = datasets.load_dataset(
@@ -76,6 +103,25 @@ def test_apply_custom_formatting_template_gives_error_with_wrong_keys():
         )
 
 
+def test_apply_custom_formatting_jinja_template_gives_error_with_wrong_keys():
+    """Tests that the jinja formatting function will throw error if wrong keys are passed to template"""
+    json_dataset = datasets.load_dataset(
+        "json", data_files=TWITTER_COMPLAINTS_DATA_JSONL
+    )
+    template = "### Input: {{not found}} \n\n ### Response: {{text_label}}"
+    formatted_dataset_field = "formatted_data_field"
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    with pytest.raises(KeyError):
+        json_dataset.map(
+            apply_custom_data_formatting_jinja_template,
+            fn_kwargs={
+                "tokenizer": tokenizer,
+                "dataset_text_field": formatted_dataset_field,
+                "template": template,
+            },
+        )
+
+
 @pytest.mark.parametrize(
     "input_element,output_element,expected_res",
     [

tests/data/test_data_preprocessing_utils.py

@@ -29,6 +29,7 @@
 
 # First Party
 from tests.artifacts.predefined_data_configs import (
+    DATA_CONFIG_APPLY_CUSTOM_JINJA_TEMPLATE_YAML,
     DATA_CONFIG_APPLY_CUSTOM_TEMPLATE_YAML,
     DATA_CONFIG_MULTIPLE_DATASETS_SAMPLING_YAML,
     DATA_CONFIG_PRETOKENIZE_JSON_DATA_YAML,
@@ -693,6 +694,10 @@ def test_process_data_args_throws_error_where_needed(data_args, packing):
         (DATA_CONFIG_APPLY_CUSTOM_TEMPLATE_YAML, TWITTER_COMPLAINTS_DATA_JSONL),
         (DATA_CONFIG_APPLY_CUSTOM_TEMPLATE_YAML, TWITTER_COMPLAINTS_DATA_PARQUET),
         (DATA_CONFIG_APPLY_CUSTOM_TEMPLATE_YAML, TWITTER_COMPLAINTS_DATA_ARROW),
+        (DATA_CONFIG_APPLY_CUSTOM_JINJA_TEMPLATE_YAML, TWITTER_COMPLAINTS_DATA_JSON),
+        (DATA_CONFIG_APPLY_CUSTOM_JINJA_TEMPLATE_YAML, TWITTER_COMPLAINTS_DATA_JSONL),
+        (DATA_CONFIG_APPLY_CUSTOM_JINJA_TEMPLATE_YAML, TWITTER_COMPLAINTS_DATA_PARQUET),
+        (DATA_CONFIG_APPLY_CUSTOM_JINJA_TEMPLATE_YAML, TWITTER_COMPLAINTS_DATA_ARROW),
         (DATA_CONFIG_PRETOKENIZE_JSON_DATA_YAML, TWITTER_COMPLAINTS_TOKENIZED_JSON),
         (DATA_CONFIG_PRETOKENIZE_JSON_DATA_YAML, TWITTER_COMPLAINTS_TOKENIZED_JSONL),
         (DATA_CONFIG_PRETOKENIZE_JSON_DATA_YAML, TWITTER_COMPLAINTS_TOKENIZED_PARQUET),
@@ -731,7 +736,10 @@ def test_process_dataconfig_file(data_config_path, data_path):
 
     # Modify dataset_text_field and template according to dataset
     formatted_dataset_field = "formatted_data_field"
-    if datasets_name == "apply_custom_data_template":
+    if datasets_name in (
+        "apply_custom_data_template",
+        "apply_custom_data_jinja_template",
+    ):
         template = "### Input: {{Tweet text}} \n\n ### Response: {{text_label}}"
         yaml_content["datasets"][0]["data_handlers"][0]["arguments"]["fn_kwargs"] = {
             "dataset_text_field": formatted_dataset_field,
@@ -753,7 +761,10 @@ def test_process_dataconfig_file(data_config_path, data_path):
         assert set(train_set.column_names) == column_names
     elif datasets_name == "pretokenized_dataset":
         assert set(["input_ids", "labels"]).issubset(set(train_set.column_names))
-    elif datasets_name == "apply_custom_data_template":
+    elif datasets_name in (
+        "apply_custom_data_template",
+        "apply_custom_data_jinja_template",
+    ):
         assert formatted_dataset_field in set(train_set.column_names)
 
 

tuning/data/data_handlers.py

@@ -19,8 +19,12 @@
 import re
 
 # Third Party
+from jinja2 import Environment, StrictUndefined
 from transformers import AutoTokenizer
 
+# Local
+from tuning.utils.config_utils import process_jinja_placeholders
+
 
 ### Utils for custom masking / manipulating input / output strs, etc
 def combine_sequence(input_element: str, output_element: str, eos_token: str = ""):
@@ -108,6 +112,8 @@ def apply_custom_data_formatting_template(
        Expects to be run as a HF Map API function.
     Args:
         element: the HF Dataset element loaded from a JSON or DatasetDict object.
+        tokenizer: Tokenizer to be used for the EOS token, which will be appended
+            when formatting the data into a single sequence. Defaults to empty.
         template: Template to format data with. Features of Dataset
             should be referred to by {{key}}
         formatted_dataset_field: Dataset_text_field
@@ -137,6 +143,39 @@ def replace_text(match_obj):
     }
 
 
+def apply_custom_data_formatting_jinja_template(
+    element: Dict[str, str],
+    tokenizer: AutoTokenizer,
+    dataset_text_field: str,
+    template: str,
+    **kwargs,
+):
+    """Function to format datasets with jinja templates.
+       Expects to be run as a HF Map API function.
+    Args:
+        element: the HF Dataset element loaded from a JSON or DatasetDict object.
+        tokenizer: Tokenizer to be used for the EOS token, which will be appended
+            when formatting the data into a single sequence. Defaults to empty.
+        dataset_text_field: formatted_dataset_field.
+        template: Template to format data with. Features of Dataset
+            should be referred to by {{key}}.
+    Returns:
+        Formatted HF Dataset
+    """
+
+    template += tokenizer.eos_token
+    template = process_jinja_placeholders(template)
+    env = Environment(undefined=StrictUndefined)
+    jinja_template = env.from_string(template)
+
+    try:
+        rendered_text = jinja_template.render(element=element, **element)
+    except Exception as e:
+        raise KeyError(f"Dataset does not contain field in template. {e}") from e
+
+    return {dataset_text_field: rendered_text}
+
+
 def apply_tokenizer_chat_template(
     element: Dict[str, str],
     tokenizer: AutoTokenizer,
@@ -157,5 +196,6 @@ def apply_tokenizer_chat_template(
     "tokenize_and_apply_input_masking": tokenize_and_apply_input_masking,
     "apply_dataset_formatting": apply_dataset_formatting,
     "apply_custom_data_formatting_template": apply_custom_data_formatting_template,
+    "apply_custom_data_formatting_jinja_template": apply_custom_data_formatting_jinja_template,
     "apply_tokenizer_chat_template": apply_tokenizer_chat_template,
 }

tuning/utils/config_utils.py

@@ -18,6 +18,7 @@
 import json
 import os
 import pickle
+import re
 
 # Third Party
 from peft import LoraConfig, PromptTuningConfig
@@ -135,3 +136,34 @@ def txt_to_obj(txt):
     except UnicodeDecodeError:
         # Otherwise the bytes are a pickled python dictionary
         return pickle.loads(message_bytes)
+
+
+def process_jinja_placeholders(template: str) -> str:
+    """
+    Function to detect all placeholders of the form {{...}}.
+    - If the inside has a space (e.g. {{Tweet text}}),
+      rewrite to {{ element['Tweet text'] }}.
+    - If it doesn't have a space (e.g. {{text_label}}), leave it as is.
+    - If it is already using dictionary-style access ({{ element['xyz'] }}), do nothing.
+
+    Args:
+        template: str
+    Return: template: str
+    """
+
+    pattern = r"\{\{([^}]+)\}\}"
+    matches = re.findall(pattern, template)
+
+    for match in matches:
+        original_placeholder = f"{{{{{match}}}}}"
+        trimmed = match.strip()
+
+        if trimmed.startswith("element["):
+            continue
+
+        # If there's a space in the placeholder name, rewrite it to dictionary-style
+        if " " in trimmed:
+            new_placeholder = f"{{{{ element['{trimmed}'] }}}}"
+            template = template.replace(original_placeholder, new_placeholder)
+
+    return template