Add a functional test to exercise our split out APIs

This adds a functional test to test the entire end-to-end flow of
`ilab data generate` exercised purely from the individual pieces of
the split out SDG APIs. No actual LLM inference happens, and instead
we just mock out all the responses for the sake of testing speed /
hardware since we don't need real LLM responses to verify our APIs.

Signed-off-by: Ben Browning <[email protected]>

Author: bbrowning

tests/functional/__init__.py

@@ -0,0 +1,101 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# Standard
+from datetime import datetime
+from unittest.mock import MagicMock
+import glob
+import pathlib
+
+# Third Party
+import git
+
+# First Party
+from instructlab.sdg import BlockRegistry
+from instructlab.sdg.generate_data import (
+    generate_taxonomy,
+    mix_datasets,
+    postprocess_taxonomy,
+    preprocess_taxonomy,
+)
+
+# Local
+from ..mockllmblock import MockLLMBlock
+
+
+def _clone_instructlab_taxonomy(taxonomy_dir):
+    taxonomy_repo_url = "https://github.com/instructlab/taxonomy"
+    taxonomy_commit = "dfa3afaf26f40f923cf758389719619ec9b1ddb1"
+    repo = git.Repo.clone_from(taxonomy_repo_url, taxonomy_dir, no_checkout=True)
+    repo.git.checkout(taxonomy_commit)
+
+
+def test_granular_api_end_to_end(testdata_path: pathlib.Path, tmp_path: pathlib.Path):
+    # Registry our mock block so we can reference it in pipelines
+    BlockRegistry.register("MockLLMBlock")(MockLLMBlock)
+
+    # Clone a taxonomy and edit 1 file in it
+    taxonomy_dir = tmp_path.joinpath("taxonomy")
+    _clone_instructlab_taxonomy(taxonomy_dir)
+    changed_qna_yaml = taxonomy_dir.joinpath(
+        "knowledge", "science", "animals", "birds", "black_capped_chickadee", "qna.yaml"
+    )
+    with open(changed_qna_yaml, "a", encoding="utf-8") as file:
+        file.write("")
+
+    pipeline_dir = testdata_path.joinpath("mock_pipelines")
+    date_suffix = datetime.now().replace(microsecond=0).isoformat().replace(":", "_")
+
+    preprocessed_dir = tmp_path.joinpath("preprocessed")
+    preprocess_taxonomy(
+        taxonomy_dir=taxonomy_dir,
+        output_dir=preprocessed_dir,
+    )
+    chickadee_docs = glob.glob(
+        str(
+            preprocessed_dir.joinpath(
+                "documents", "knowledge_science_*", "chickadee.md"
+            )
+        )
+    )
+    assert chickadee_docs
+    chickadee_samples_path = preprocessed_dir.joinpath(
+        "knowledge_science_animals_birds_black_capped_chickadee.jsonl"
+    )
+    assert chickadee_samples_path.is_file()
+
+    client = MagicMock()
+    client.server_supports_batched = False
+    generated_dir = tmp_path.joinpath("generated")
+    generate_taxonomy(
+        client=client,
+        input_dir=preprocessed_dir,
+        output_dir=generated_dir,
+        pipeline=pipeline_dir,
+    )
+    generated_chickadee_samples_path = generated_dir.joinpath(
+        "knowledge_science_animals_birds_black_capped_chickadee.jsonl"
+    )
+    assert generated_chickadee_samples_path.is_file()
+
+    postprocessed_dir = tmp_path.joinpath("postprocessed")
+    postprocess_taxonomy(
+        input_dir=generated_dir,
+        output_dir=postprocessed_dir,
+        date_suffix=date_suffix,
+        pipeline=pipeline_dir,
+    )
+    knowledge_recipe_file = postprocessed_dir.joinpath(
+        f"knowledge_recipe_{date_suffix}.yaml"
+    )
+    assert knowledge_recipe_file.is_file()
+    skills_recipe_file = postprocessed_dir.joinpath(f"skills_recipe_{date_suffix}.yaml")
+    assert skills_recipe_file.is_file()
+
+    mixed_skills_output_file = (
+        f"{postprocessed_dir}/skills_train_msgs_{date_suffix}.jsonl"
+    )
+    mix_datasets(
+        recipe_file=f"{postprocessed_dir}/skills_recipe_{date_suffix}.yaml",
+        output_file=mixed_skills_output_file,
+    )
+    assert pathlib.Path(mixed_skills_output_file).is_file()

tests/functional/test_granular_api.py

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+# SPDX-License-Identifier: Apache-2.0
+
+# Standard
+import random
+import string
+
+# Third Party
+from datasets import Dataset
+
+# First Party
+from instructlab.sdg import LLMBlock
+
+
+def _random_string(size):
+    return "".join(random.choices(string.ascii_lowercase, k=size))
+
+
+def _add_mocked_cols(sample, block_name):
+    match block_name:
+        case "gen_questions" | "gen_grounded_questions":
+            sample["question"] = f"Is this a question {_random_string(8)}?"
+        case "eval_questions" | "eval_grounded_questions":
+            sample["evaluation"] = "This is an evaluation."
+            sample["score"] = "1"
+        case "gen_responses" | "gen_grounded_responses":
+            sample["response"] = "This is a response."
+        case "evaluate_qa_pair" | "evaluate_grounded_qa_pair":
+            sample["evaluation"] = "This is an evaluation."
+            sample["score"] = "2"
+        case "gen_contexts":
+            sample["context"] = f"This is a context {_random_string(8)}."
+        case "gen_spellcheck":
+            sample["spellcheck"] = sample["document"]
+        case "gen_knowledge":
+            sample["question"] = f"Is this a question {_random_string(8)}?"
+            sample["response"] = "This is a response."
+        case "eval_faithfulness_qa_pair":
+            sample["explanation"] = "This is an explanation."
+            sample["judgment"] = "YES"
+        case "eval_relevancy_qa_pair":
+            sample["feedback"] = "This is some feedback."
+            sample["score"] = "2"
+        case "eval_verify_question":
+            sample["explanation"] = "This is an explanation."
+            sample["rating"] = "1"
+        case _:
+            raise Exception(
+                f"Received an un-mocked LLMBlock: {block_name}. Add code in {__file__} to handle this block."
+            )
+    return sample
+
+
+class MockLLMBlock(LLMBlock):
+    def generate(self, samples: Dataset):
+        return samples.map(_add_mocked_cols, fn_kwargs={"block_name": self.block_name})