Retry with single core in geneformer

src/methods/geneformer/script.py

@@ -23,22 +23,24 @@
 
 print(">>> Reading input...", flush=True)
 sys.path.append(meta["resources_dir"])
-from read_anndata_partial import read_anndata
 from exit_codes import exit_non_applicable
+from read_anndata_partial import read_anndata
 
 adata = read_anndata(par["input"], X="layers/counts", obs="obs", var="var", uns="uns")
 
 if adata.uns["dataset_organism"] != "homo_sapiens":
     exit_non_applicable(
         f"Geneformer can only be used with human data "
-        f"(dataset_organism == \"{adata.uns['dataset_organism']}\")"
+        f'(dataset_organism == "{adata.uns["dataset_organism"]}")'
     )
 
 # Set adata.var_names to gene IDs
 adata.var_names = adata.var["feature_id"]
 is_ensembl = all(var_name.startswith("ENSG") for var_name in adata.var_names)
 if not is_ensembl:
-    raise ValueError(f"Geneformer requires adata.var_names to contain ENSEMBL gene ids")
+    exit_non_applicable(
+        "Geneformer requires adata.var_names to contain ENSEMBL gene ids"
+    )
 
 print(f">>> Getting settings for model '{par['model']}'...", flush=True)
 model_split = par["model"].split("-")
@@ -97,18 +99,42 @@
 adata.write_h5ad(os.path.join(input_dir, "input.h5ad"))
 print(adata)
 
+
+# Function to try parallel execution and fall batch to a single processor if it fails
+def tryParallelFunction(fun, label):
+    try:
+        fun(nproc=n_processors)
+    except RuntimeError as e:
+        # Retry with nproc=1 if error message contains "One of the subprocesses has abruptly died"
+        if "subprocess" in str(e) and "died" in str(e):
+            print(f"{label} failed. Error message: {e}", flush=True)
+            print("Retrying with nproc=1", flush=True)
+            fun(nproc=1)
+        else:
+            raise e
+
+
 print(">>> Tokenizing data...", flush=True)
 special_token = model_details["dataset"] == "95M"
 print(f"Input size: {model_details['input_size']}, Special token: {special_token}")
-tokenizer = TranscriptomeTokenizer(
-    nproc=n_processors,
-    model_input_size=model_details["input_size"],
-    special_token=special_token,
-    gene_median_file=dictionary_files["gene_median"],
-    token_dictionary_file=dictionary_files["token"],
-    gene_mapping_file=dictionary_files["ensembl_mapping"],
-)
-tokenizer.tokenize_data(input_dir, tokenized_dir, "tokenized", file_format="h5ad")
+
+
+def tokenize_data(nproc):
+    tokenizer = TranscriptomeTokenizer(
+        nproc=nproc,
+        model_input_size=model_details["input_size"],
+        special_token=special_token,
+        gene_median_file=dictionary_files["gene_median"],
+        token_dictionary_file=dictionary_files["token"],
+        gene_mapping_file=dictionary_files["ensembl_mapping"],
+    )
+
+    tokenizer.tokenize_data(input_dir, tokenized_dir, "tokenized", file_format="h5ad")
+
+    return tokenizer
+
+
+tokenizer = tryParallelFunction(tokenize_data, "Tokenizing data")
 
 print(f">>> Getting model files for model '{par['model']}'...", flush=True)
 model_files = {