Move clearml tests to appropriate place to unblock GHA

.github/workflows/test-check.yaml

@@ -140,9 +140,14 @@ jobs:
       - name: "🔬 Running onnx tests"
         run: make test TARGETS=onnx
   pytorch-tests:
-    runs-on: ubuntu-22.04
+    runs-on: aws-avx2-64G
     env:
       SPARSEZOO_TEST_MODE: "true"
+      CLEARML_WEB_HOST: ${{ secrets.CLEARML_WEB_HOST }}
+      CLEARML_API_HOST: ${{ secrets.CLEARML_API_HOST }}
+      CLEARML_API_ACCESS_KEY: ${{ secrets.CLEARML_API_ACCESS_KEY }}
+      CLEARML_FILES_HOST:  ${{ secrets.CLEARML_FILES_HOST }}
+      CLEARML_API_SECRET_KEY:  ${{ secrets.CLEARML_API_SECRET_KEY }}
     needs: test-setup
     if: ${{needs.test-setup.outputs.pytorch == 1}}
     steps:
@@ -164,9 +169,14 @@ jobs:
       - name: "🔬 Running pytorch tests"
         run: make test TARGETS=pytorch
   compat-pytorch-1_9-pytorch-tests:
-    runs-on: ubuntu-22.04
+    runs-on: aws-avx2-64G
     env:
       SPARSEZOO_TEST_MODE: "true"
+      CLEARML_WEB_HOST: ${{ secrets.CLEARML_WEB_HOST }}
+      CLEARML_API_HOST: ${{ secrets.CLEARML_API_HOST }}
+      CLEARML_API_ACCESS_KEY: ${{ secrets.CLEARML_API_ACCESS_KEY }}
+      CLEARML_FILES_HOST:  ${{ secrets.CLEARML_FILES_HOST }}
+      CLEARML_API_SECRET_KEY:  ${{ secrets.CLEARML_API_SECRET_KEY }}
     needs: test-setup
     if: ${{needs.test-setup.outputs.pytorch == 1}}
     steps:
@@ -212,9 +222,14 @@ jobs:
       - name: "🔬 Running onnx tests"
         run: make test TARGETS=onnx
   transformers-tests:
-    runs-on: ubuntu-22.04
+    runs-on: aws-avx2-64G
     env:
       SPARSEZOO_TEST_MODE: "true"
+      CLEARML_WEB_HOST: ${{ secrets.CLEARML_WEB_HOST }}
+      CLEARML_API_HOST: ${{ secrets.CLEARML_API_HOST }}
+      CLEARML_API_ACCESS_KEY: ${{ secrets.CLEARML_API_ACCESS_KEY }}
+      CLEARML_FILES_HOST:  ${{ secrets.CLEARML_FILES_HOST }}
+      CLEARML_API_SECRET_KEY:  ${{ secrets.CLEARML_API_SECRET_KEY }}
     needs: test-setup
     if: ${{needs.test-setup.outputs.transformers == 1}}
     steps:

tests/sparseml/transformers/finetune/test_finetune.py

@@ -141,7 +141,7 @@ def test_oneshot_then_finetune(tmp_path: Path):
     )
 
 
-def test_finetune_wout_recipe(tmp_path: Path):
+def test_finetune_without_recipe(tmp_path: Path):
     recipe_str = None
     model = "Xenova/llama2.c-stories15M"
     device = "cuda:0"

tests/sparseml/transformers/finetune/test_finetune_helpers.py

@@ -26,7 +26,7 @@ def test_apply_recipe_structure():
     model = AutoModelForCausalLM.from_pretrained(model_path)
     assert not qat_active(model)
 
-    recipe_with_quant = "tests/sparseml/transformers/obcq/test_tiny.yaml"
+    recipe_with_quant = "tests/sparseml/transformers/obcq/quant_and_sparse.yaml"
     apply_recipe_structure_to_model(model, recipe_with_quant, model_path)
 
     assert qat_active(model)

tests/sparseml/test_clear_ml.py → tests/sparseml/transformers/test_clear_ml.py

@@ -14,46 +14,29 @@
 
 from pathlib import Path
 
-from clearml import Task
-from sparseml.transformers import apply
-from sparseml.utils import is_package_available
-
-
-is_torch_available = is_package_available("torch")
-if is_torch_available:
-    import torch
+import torch
 
-    torch_err = None
-else:
-    torch = object
-    torch_err = ModuleNotFoundError(
-        "`torch` is not installed, use `pip install torch` to log to Weights and Biases"
-    )
+from clearml import Task
+from sparseml.transformers import train
 
 
-def test_oneshot_and_finetune(tmp_path: Path):
-    recipe_str = "tests/sparseml/transformers/finetune/test_alternate_recipe.yaml"
+def test_finetune_wout_recipe(tmp_path: Path):
+    recipe_str = None
     model = "Xenova/llama2.c-stories15M"
     device = "cuda:0"
-    if is_torch_available and not torch.cuda.is_available():
+    if not torch.cuda.is_available():
         device = "cpu"
-    dataset = "wikitext"
-    dataset_config_name = "wikitext-2-raw-v1"
-    concatenate_data = True
-    run_stages = True
+    dataset = "open_platypus"
+    concatenate_data = False
     output_dir = tmp_path
     max_steps = 50
-    splits = {"train": "train[:50%]", "calibration": "train[50%:60%]"}
+    splits = "train"
 
-    # clearML will automatically log default capturing entries without
-    # explicitly calling logger. Logs accessible in https://app.clear.ml/
     Task.init(project_name="test", task_name="test_oneshot_and_finetune")
 
-    apply(
+    train(
         model=model,
         dataset=dataset,
-        dataset_config_name=dataset_config_name,
-        run_stages=run_stages,
         output_dir=output_dir,
         recipe=recipe_str,
         max_steps=max_steps,