drop subprocess test

sub-packages/bionemo-esm2/tests/bionemo/esm2/scripts/test_infer_esm2.py

@@ -14,16 +14,11 @@
 # limitations under the License.
 
 import glob
-import os
-import shlex
-import subprocess
-from pathlib import Path
 from typing import get_args
 
 import pandas as pd
 import pytest
 import torch
-from lightning.fabric.plugins.environments.lightning import find_free_network_port
 from torch.utils.data import DataLoader
 
 from bionemo.core.data.load import load
@@ -147,7 +142,12 @@ def test_esm2_fine_tune_data_module_val_dataloader(data_module):
 @pytest.mark.parametrize("prediction_interval", get_args(IntervalT))
 @pytest.mark.skipif(check_gpu_memory(30), reason="Skipping test due to insufficient GPU memory")
 def test_infer_runs(
-    tmpdir, dummy_protein_csv, dummy_protein_sequences, precision, padded_tokenized_sequences, prediction_interval
+    tmpdir,
+    dummy_protein_csv,
+    dummy_protein_sequences,
+    precision,
+    prediction_interval,
+    padded_tokenized_sequences,
 ):
     data_path = dummy_protein_csv
     result_dir = tmpdir / "results"
@@ -188,35 +188,9 @@ def test_infer_runs(
     # token_logits are [sequence, batch, num_tokens]
     assert results["token_logits"].shape[:-1] == (min_seq_len, len(dummy_protein_sequences))
 
-
-@pytest.mark.skipif(check_gpu_memory(40), reason="Skipping test due to insufficient GPU memory")
-@pytest.mark.parametrize("checkpoint_path", [esm2_3b_checkpoint_path, esm2_650m_checkpoint_path])
-def test_infer_cli(tmpdir, dummy_protein_csv, checkpoint_path):
-    # Clear the GPU cache before starting the test
-    torch.cuda.empty_cache()
-
-    result_dir = Path(tmpdir.mkdir("results"))
-    results_path = result_dir / "esm2_infer_results.pt"
-    open_port = find_free_network_port()
-    env = dict(**os.environ)
-    env["MASTER_PORT"] = str(open_port)
-
-    cmd_str = f"""infer_esm2     \
-    --checkpoint-path {checkpoint_path} \
-    --data-path {dummy_protein_csv} \
-    --results-path {results_path} \
-    --precision bf16-mixed \
-    --include-hiddens      \
-    --include-embeddings     \
-    --include-logits     \
-    --include-input-ids
-    """.strip()
-
-    cmd = shlex.split(cmd_str)
-    result = subprocess.run(
-        cmd,
-        cwd=tmpdir,
-        env=env,
-        capture_output=True,
-    )
-    assert result.returncode == 0, f"Failed with: {cmd_str}"
+    # test 1:1 mapping between input sequence and results
+    # this does not apply to "batch" prediction_interval mode since the order of batches may not be consistent
+    # due distributed processing. To address this, we optionally include input_ids in the predictions, allowing
+    # for accurate mapping post-inference.
+    if prediction_interval == "epoch":
+        assert torch.equal(padded_tokenized_sequences, results["input_ids"])