refactor(test): move tools, evals, datasetio, scoring and post training tests (#1401)

All of the tests from `llama_stack/providers/tests/` are now moved to
`tests/integration`.

I converted the `tools`, `scoring` and `datasetio` tests to use API.
However, `eval` and `post_training` proved to be a bit challenging to
leaving those. I think `post_training` should be relatively
straightforward also.

As part of this, I noticed that `wolfram_alpha` tool wasn't added to
some of our commonly used distros so I added it. I am going to remove a
lot of code duplication from distros next so while this looks like a
one-off right now, it will go away and be there uniformly for all
distros.

tests/integration/post_training/test_post_training.py

@@ -0,0 +1,101 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+from typing import List
+
+import pytest
+
+from llama_stack.apis.common.job_types import JobStatus
+from llama_stack.apis.post_training import (
+    Checkpoint,
+    DataConfig,
+    LoraFinetuningConfig,
+    OptimizerConfig,
+    PostTrainingJob,
+    PostTrainingJobArtifactsResponse,
+    PostTrainingJobStatusResponse,
+    TrainingConfig,
+)
+
+# How to run this test:
+#
+# pytest llama_stack/providers/tests/post_training/test_post_training.py
+#   -m "torchtune_post_training_huggingface_datasetio"
+#   -v -s --tb=short --disable-warnings
+
+
+@pytest.mark.skip(reason="FIXME FIXME @yanxi0830 this needs to be migrated to use the API")
+class TestPostTraining:
+    @pytest.mark.asyncio
+    async def test_supervised_fine_tune(self, post_training_stack):
+        algorithm_config = LoraFinetuningConfig(
+            type="LoRA",
+            lora_attn_modules=["q_proj", "v_proj", "output_proj"],
+            apply_lora_to_mlp=True,
+            apply_lora_to_output=False,
+            rank=8,
+            alpha=16,
+        )
+
+        data_config = DataConfig(
+            dataset_id="alpaca",
+            batch_size=1,
+            shuffle=False,
+        )
+
+        optimizer_config = OptimizerConfig(
+            optimizer_type="adamw",
+            lr=3e-4,
+            lr_min=3e-5,
+            weight_decay=0.1,
+            num_warmup_steps=100,
+        )
+
+        training_config = TrainingConfig(
+            n_epochs=1,
+            data_config=data_config,
+            optimizer_config=optimizer_config,
+            max_steps_per_epoch=1,
+            gradient_accumulation_steps=1,
+        )
+        post_training_impl = post_training_stack
+        response = await post_training_impl.supervised_fine_tune(
+            job_uuid="1234",
+            model="Llama3.2-3B-Instruct",
+            algorithm_config=algorithm_config,
+            training_config=training_config,
+            hyperparam_search_config={},
+            logger_config={},
+            checkpoint_dir="null",
+        )
+        assert isinstance(response, PostTrainingJob)
+        assert response.job_uuid == "1234"
+
+    @pytest.mark.asyncio
+    async def test_get_training_jobs(self, post_training_stack):
+        post_training_impl = post_training_stack
+        jobs_list = await post_training_impl.get_training_jobs()
+        assert isinstance(jobs_list, List)
+        assert jobs_list[0].job_uuid == "1234"
+
+    @pytest.mark.asyncio
+    async def test_get_training_job_status(self, post_training_stack):
+        post_training_impl = post_training_stack
+        job_status = await post_training_impl.get_training_job_status("1234")
+        assert isinstance(job_status, PostTrainingJobStatusResponse)
+        assert job_status.job_uuid == "1234"
+        assert job_status.status == JobStatus.completed
+        assert isinstance(job_status.checkpoints[0], Checkpoint)
+
+    @pytest.mark.asyncio
+    async def test_get_training_job_artifacts(self, post_training_stack):
+        post_training_impl = post_training_stack
+        job_artifacts = await post_training_impl.get_training_job_artifacts("1234")
+        assert isinstance(job_artifacts, PostTrainingJobArtifactsResponse)
+        assert job_artifacts.job_uuid == "1234"
+        assert isinstance(job_artifacts.checkpoints[0], Checkpoint)
+        assert job_artifacts.checkpoints[0].identifier == "Llama3.2-3B-Instruct-sft-0"
+        assert job_artifacts.checkpoints[0].epoch == 0
+        assert "/.llama/checkpoints/Llama3.2-3B-Instruct-sft-0" in job_artifacts.checkpoints[0].path