chore: import upstream snapshot with attribution

tests/setup/models/create_hf_model.py

@@ -0,0 +1,102 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import json
+import os
+
+from typing import Any, Dict, Optional
+
+import transformers
+
+"""
+Create a randomly initialized HuggingFace model for testing purposes.
+
+Model can be specified by name or path for creating its config and tokenizer using
+HuggingFace transformers AutoConfig and AutoTokenizer functions.
+
+Parameter config_updates can be used to override specific model config fields to make
+it smaller, for example, by changing number of layers or hidden layers dimensionality,
+making it adequate for testing purposes. This parameter should be specified as
+a dictionary that can be parsed using json.loads method.
+
+Example usage for Llama2 model (requires HF login):
+```
+python tests/setup/models/create_tiny_hf_model.py \
+  --model_name_or_path meta-llama/Llama-2-7b-hf \
+  --output_dir tiny_llama2_hf \
+  --config_updates '{"hidden_size": 128, "num_attention_heads": 4, "num_hidden_layers": 2, "num_key_value_heads": 4}'
+```
+"""
+
+
+def get_hf_model_class(hf_config):
+    """Get HuggingFace model class from config."""
+    if len(hf_config.architectures) > 1:
+        print(f"More than one model architecture available, choosing 1st: {hf_config.architectures}")
+    model_name = hf_config.architectures[0]
+    model_class = getattr(transformers, model_name)
+    return model_class
+
+
+def create_hf_model(
+    model_name_or_path: str, output_dir: str, config_updates: Optional[Dict[str, Any]] = None, overwrite: bool = False
+):
+    """Create HuggingFace model with optional config updates."""
+    if os.path.isdir(output_dir) and not overwrite:
+        print(f"Output directory {output_dir} exists and overwrite flag is not set so exiting.")
+        return
+
+    hf_config = transformers.AutoConfig.from_pretrained(model_name_or_path)
+    tokenizer = transformers.AutoTokenizer.from_pretrained(model_name_or_path)
+    model_class = get_hf_model_class(hf_config)
+
+    if config_updates is not None:
+        hf_config.update(config_updates)
+    print(hf_config)
+
+    model = model_class(hf_config)
+    print(model)
+
+    os.makedirs(output_dir, exist_ok=True)
+    print(f"Saving model to {output_dir}...")
+    tokenizer.save_pretrained(output_dir)
+    model.save_pretrained(output_dir)
+    print("OK.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("Create a HuggingFace model (random initialization) for testing purposes.")
+    parser.add_argument(
+        "--model_name_or_path",
+        required=True,
+        help="Model name or local path with model config and tokenizer",
+    )
+    parser.add_argument(
+        "--output_dir",
+        required=True,
+        help="Output directory",
+    )
+    parser.add_argument(
+        "--config_updates",
+        type=json.loads,
+        help="Parameter updates in JSON format to overwrite for model config",
+    )
+    parser.add_argument(
+        "--overwrite",
+        action="store_true",
+        help="Overwrite file if it exists",
+    )
+    args = parser.parse_args()
+    create_hf_model(args.model_name_or_path, args.output_dir, args.config_updates, args.overwrite)