Support special test parametrizations

tests/accelerators/test_ddp.py

@@ -109,16 +109,8 @@ def setup(self, stage: Optional[str] = None) -> None:
 
 
 @RunIf(min_gpus=2, min_torch="1.8.1", special=True)
-def test_ddp_wrapper_16(tmpdir):
-    _test_ddp_wrapper(tmpdir, precision=16)
-
-
-@RunIf(min_gpus=2, min_torch="1.8.1", special=True)
-def test_ddp_wrapper_32(tmpdir):
-    _test_ddp_wrapper(tmpdir, precision=32)
-
-
-def _test_ddp_wrapper(tmpdir, precision):
+@pytest.mark.parametrize("precision", (16, 32))
+def test_ddp_wrapper(tmpdir, precision):
     """Test parameters to ignore are carried over for DDP."""
 
     class WeirdModule(torch.nn.Module):

tests/callbacks/test_pruning.py

@@ -161,27 +161,18 @@ def test_pruning_callback(
 
 
 @RunIf(special=True, min_gpus=2)
-def test_pruning_callback_ddp_0(tmpdir):
+@pytest.mark.parametrize("parameters_to_prune", (False, True))
+@pytest.mark.parametrize("use_global_unstructured", (False, True))
+def test_pruning_callback_ddp(tmpdir, parameters_to_prune, use_global_unstructured):
     train_with_pruning_callback(
-        tmpdir, parameters_to_prune=False, use_global_unstructured=False, strategy="ddp", gpus=2
+        tmpdir,
+        parameters_to_prune=parameters_to_prune,
+        use_global_unstructured=use_global_unstructured,
+        strategy="ddp",
+        gpus=2,
     )
 
 
-@RunIf(special=True, min_gpus=2)
-def test_pruning_callback_ddp_1(tmpdir):
-    train_with_pruning_callback(tmpdir, parameters_to_prune=False, use_global_unstructured=True, strategy="ddp", gpus=2)
-
-
-@RunIf(special=True, min_gpus=2)
-def test_pruning_callback_ddp_2(tmpdir):
-    train_with_pruning_callback(tmpdir, parameters_to_prune=True, use_global_unstructured=False, strategy="ddp", gpus=2)
-
-
-@RunIf(special=True, min_gpus=2)
-def test_pruning_callback_ddp_3(tmpdir):
-    train_with_pruning_callback(tmpdir, parameters_to_prune=True, use_global_unstructured=True, strategy="ddp", gpus=2)
-
-
 @RunIf(min_gpus=2, skip_windows=True)
 def test_pruning_callback_ddp_spawn(tmpdir):
     train_with_pruning_callback(tmpdir, use_global_unstructured=True, strategy="ddp_spawn", gpus=2)

tests/callbacks/test_tqdm_progress_bar.py

@@ -513,20 +513,11 @@ def test_tqdm_progress_bar_can_be_pickled():
 
 
 @RunIf(min_gpus=2, special=True)
-def test_tqdm_progress_bar_max_val_check_interval_0(tmpdir):
-    _test_progress_bar_max_val_check_interval(
-        tmpdir, total_train_samples=8, train_batch_size=4, total_val_samples=2, val_batch_size=1, val_check_interval=0.2
-    )
-
-
-@RunIf(min_gpus=2, special=True)
-def test_tqdm_progress_bar_max_val_check_interval_1(tmpdir):
-    _test_progress_bar_max_val_check_interval(
-        tmpdir, total_train_samples=8, train_batch_size=4, total_val_samples=2, val_batch_size=1, val_check_interval=0.5
-    )
-
-
-def _test_progress_bar_max_val_check_interval(
+@pytest.mark.parametrize(
+    ["total_train_samples", "train_batch_size", "total_val_samples", "val_batch_size", "val_check_interval"],
+    [(8, 4, 2, 1, 0.2), (8, 4, 2, 1, 0.5)],
+)
+def test_progress_bar_max_val_check_interval(
     tmpdir, total_train_samples, train_batch_size, total_val_samples, val_batch_size, val_check_interval
 ):
     world_size = 2

tests/checkpointing/test_checkpoint_callback_frequency.py

@@ -88,17 +88,8 @@ def training_step(self, batch, batch_idx):
 
 @mock.patch("torch.save")
 @RunIf(special=True, min_gpus=2)
-def test_top_k_ddp_0(save_mock, tmpdir):
-    _top_k_ddp(save_mock, tmpdir, k=1, epochs=1, val_check_interval=1.0, expected=1)
-
-
-@mock.patch("torch.save")
-@RunIf(special=True, min_gpus=2)
-def test_top_k_ddp_1(save_mock, tmpdir):
-    _top_k_ddp(save_mock, tmpdir, k=2, epochs=2, val_check_interval=0.3, expected=4)
-
-
-def _top_k_ddp(save_mock, tmpdir, k, epochs, val_check_interval, expected):
+@pytest.mark.parametrize(["k", "epochs", "val_check_interval", "expected"], [(1, 1, 1.0, 1), (2, 2, 0.3, 4)])
+def test_top_k_ddp(save_mock, tmpdir, k, epochs, val_check_interval, expected):
     class TestModel(BoringModel):
         def training_step(self, batch, batch_idx):
             local_rank = int(os.getenv("LOCAL_RANK"))

tests/conftest.py

@@ -154,3 +154,16 @@ def single_process_pg():
         torch.distributed.destroy_process_group()
         os.environ.clear()
         os.environ.update(orig_environ)
+
+
+def pytest_collection_modifyitems(items):
+    if os.getenv("PL_RUNNING_SPECIAL_TESTS", "0") != "1":
+        return
+    # filter out non-special tests
+    items[:] = [
+        item
+        for item in items
+        for marker in item.own_markers
+        # has `@RunIf(special=True)`
+        if marker.name == "skipif" and marker.kwargs.get("special")
+    ]

tests/helpers/runif.py

@@ -150,6 +150,8 @@ def __new__(
             env_flag = os.getenv("PL_RUNNING_SPECIAL_TESTS", "0")
             conditions.append(env_flag != "1")
             reasons.append("Special execution")
+            # used in tests/conftest.py::pytest_collection_modifyitems
+            kwargs["special"] = True
 
         if fairscale:
             conditions.append(not _FAIRSCALE_AVAILABLE)

tests/models/test_hooks.py

@@ -423,16 +423,10 @@ def _predict_batch(trainer, model, batches):
 
 
 @RunIf(deepspeed=True, min_gpus=1, special=True)
-def test_trainer_model_hook_system_fit_deepspeed_automatic_optimization(tmpdir):
-    _run_trainer_model_hook_system_fit(
-        dict(gpus=1, precision=16, strategy="deepspeed"), tmpdir, automatic_optimization=True
-    )
-
-
-@RunIf(deepspeed=True, min_gpus=1, special=True)
-def test_trainer_model_hook_system_fit_deepspeed_manual_optimization(tmpdir):
+@pytest.mark.parametrize("automatic_optimization", (True, False))
+def test_trainer_model_hook_system_fit_deepspeed(tmpdir, automatic_optimization):
     _run_trainer_model_hook_system_fit(
-        dict(gpus=1, precision=16, strategy="deepspeed"), tmpdir, automatic_optimization=False
+        dict(gpus=1, precision=16, strategy="deepspeed"), tmpdir, automatic_optimization=automatic_optimization
     )
 
 

tests/special_tests.sh

@@ -17,55 +17,49 @@ set -e
 # this environment variable allows special tests to run
 export PL_RUNNING_SPECIAL_TESTS=1
 # python arguments
-defaults='-m coverage run --source pytorch_lightning --append -m pytest --durations=0 --capture=no --disable-warnings'
+defaults='-m coverage run --source pytorch_lightning --append -m pytest --capture=no'
 
-# find tests marked as `@RunIf(special=True)`
-grep_output=$(grep --recursive --line-number --word-regexp 'tests' 'benchmarks' --regexp 'special=True')
-# file paths
-files=$(echo "$grep_output" | cut -f1 -d:)
-files_arr=($files)
-# line numbers
-linenos=$(echo "$grep_output" | cut -f2 -d:)
-linenos_arr=($linenos)
+# find tests marked as `@RunIf(special=True)`. done manually instead of with pytest because it is faster
+grep_output=$(grep --recursive --word-regexp 'tests' 'benchmarks' --regexp 'special=True' --include '*.py' --exclude 'tests/conftest.py')
+
+# file paths, remove duplicates
+files=$(echo "$grep_output" | cut -f1 -d: | sort | uniq)
+
+# get the list of parametrizations. we need to call them separately. the last two lines are removed.
+# note: if there's a syntax error, this will fail with some garbled output
+if [[ "$OSTYPE" == "darwin"* ]]; then
+  parametrizations=$(pytest $files --collect-only --quiet | tail -r | sed -e '1,3d' | tail -r)
+else
+  parametrizations=$(pytest $files --collect-only --quiet | head -n -2)
+fi
+parametrizations_arr=($parametrizations)
 
 # tests to skip - space separated
-blocklist='test_pytorch_profiler_nested_emit_nvtx'
+blocklist='tests/profiler/test_profiler.py::test_pytorch_profiler_nested_emit_nvtx'
 report=''
 
-for i in "${!files_arr[@]}"; do
-  file=${files_arr[$i]}
-  lineno=${linenos_arr[$i]}
-
-  # get code from `@RunIf(special=True)` line to EOF
-  test_code=$(tail -n +"$lineno" "$file")
+for i in "${!parametrizations_arr[@]}"; do
+  parametrization=${parametrizations_arr[$i]}
 
-  # read line by line
-  while read -r line; do
-    # if it's a test
-    if [[ $line == def\ test_* ]]; then
-      # get the name
-      test_name=$(echo $line | cut -c 5- | cut -f1 -d\()
+  # check blocklist
+  if echo $blocklist | grep -F "${parametrization}"; then
+    report+="Skipped\t$parametrization\n"
+    continue
+  fi
 
-      # check blocklist
-      if echo $blocklist | grep --word-regexp "$test_name" > /dev/null; then
-        report+="Skipped\t$file:$lineno::$test_name\n"
-        break
-      fi
+  # SPECIAL_PATTERN allows filtering the tests to run when debugging.
+  # use as `SPECIAL_PATTERN="foo_bar" ./special_tests.sh` to run only those
+  # test with `foo_bar` in their name
+  if [[ $parametrization != *$SPECIAL_PATTERN* ]]; then
+    report+="Skipped\t$parametrization\n"
+    continue
+  fi
 
-      # SPECIAL_PATTERN allows filtering the tests to run when debugging.
-      # use as `SPECIAL_PATTERN="foo_bar" ./special_tests.sh` to run only those
-      # test with `foo_bar` in their name
-      if [[ $line != *$SPECIAL_PATTERN* ]]; then
-        report+="Skipped\t$file:$lineno::$test_name\n"
-        break
-      fi
+  # run the test
+  echo "Running ${parametrization}"
+  python ${defaults} "${parametrization}"
 
-      # run the test
-      report+="Ran\t$file:$lineno::$test_name\n"
-      python ${defaults} "${file}::${test_name}"
-      break
-    fi
-  done < <(echo "$test_code")
+  report+="Ran\t$parametrization\n"
 done
 
 if nvcc --version; then

tests/trainer/test_trainer.py

@@ -1453,29 +1453,26 @@ def test_trainer_predict_cpu(tmpdir, datamodule, enable_progress_bar):
 
 
 @RunIf(min_gpus=2, special=True)
-@pytest.mark.parametrize("num_gpus", [1, 2])
-def test_trainer_predict_dp(tmpdir, num_gpus):
-    predict(tmpdir, strategy="dp", accelerator="gpu", devices=num_gpus)
-
-
-@RunIf(min_gpus=2, special=True, fairscale=True)
-def test_trainer_predict_ddp(tmpdir):
-    predict(tmpdir, strategy="ddp", accelerator="gpu", devices=2)
-
-
-@RunIf(min_gpus=2, skip_windows=True, special=True)
-def test_trainer_predict_ddp_spawn(tmpdir):
-    predict(tmpdir, strategy="dp", accelerator="gpu", devices=2)
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"strategy": "dp", "devices": 1},
+        {"strategy": "dp", "devices": 2},
+        {"strategy": "ddp", "devices": 2},
+    ],
+)
+def test_trainer_predict_special(tmpdir, kwargs):
+    predict(tmpdir, accelerator="gpu", **kwargs)
 
 
-@RunIf(min_gpus=1, special=True)
+@RunIf(min_gpus=1)
 def test_trainer_predict_1_gpu(tmpdir):
     predict(tmpdir, accelerator="gpu", devices=1)
 
 
 @RunIf(skip_windows=True)
-def test_trainer_predict_ddp_cpu(tmpdir):
-    predict(tmpdir, strategy="ddp_spawn", accelerator="cpu", devices=2)
+def test_trainer_predict_ddp_spawn(tmpdir):
+    predict(tmpdir, strategy="ddp_spawn", accelerator="auto", devices=2)
 
 
 @pytest.mark.parametrize("dataset_cls", [RandomDataset, RandomIterableDatasetWithLen, RandomIterableDataset])