Add SmolLM (smollm2)

examples/models/llama/export_llama_lib.py

@@ -94,6 +94,7 @@
     "static_llama",
     "qwen2_5",
     "phi-4-mini",
+    "smolllm2",
 ]
 TORCHTUNE_DEFINED_MODELS = ["llama3_2_vision"]
 

examples/models/smollm/135M_config.json

@@ -0,0 +1,14 @@
+{
+    "dim": 576,
+    "ffn_dim_multiplier": 1,
+    "hidden_dim": 576,
+    "n_heads": 9,
+    "n_kv_heads": 3,
+    "n_layers": 30,
+    "norm_eps": 1e-05,
+    "rope_theta": 10000.0,
+    "use_scaled_rope": false,
+    "vocab_size": 49152,
+    "use_hf_rope": true,
+    "attention_qkv_bias": false
+  }

examples/models/smollm/__init__

@@ -0,0 +1,14 @@
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from executorch.example.models.llama.model import Llama2Model
+
+
+class SmolLMModel(Llama2Model):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+
+__all__ = [
+    "SmolLMModel",
+]

examples/models/smollm/convert_weights.py

@@ -0,0 +1,80 @@
+import argparse
+from typing import Dict
+
+import torch
+
+from torchtune.models.convert_weights import get_mapped_key
+
+from torchtune.training import FullModelHFCheckpointer
+
+# Standard _FROM_META weight mapping of Meta weights to TorchTune + additional bias weight mappings.
+_SMOLLM_FROM_META = {
+    "tok_embeddings.weight": "tok_embeddings.weight",
+    "norm.weight": "norm.scale",
+    "output.weight": "output.weight",
+    "layers.{}.attention.wk.weight": "layers.{}.attn.k_proj.weight",
+    "layers.{}.attention.wq.weight": "layers.{}.attn.q_proj.weight",
+    "layers.{}.attention.wv.weight": "layers.{}.attn.v_proj.weight",
+    "layers.{}.attention.wo.weight": "layers.{}.attn.output_proj.weight",
+    "layers.{}.attention_norm.weight": "layers.{}.sa_norm.scale",
+    "layers.{}.ffn_norm.weight": "layers.{}.mlp_norm.scale",
+    "layers.{}.feed_forward.w1.weight": "layers.{}.mlp.w1.weight",
+    "layers.{}.feed_forward.w2.weight": "layers.{}.mlp.w2.weight",
+    "layers.{}.feed_forward.w3.weight": "layers.{}.mlp.w3.weight",
+}
+
+
+def smollm_tune_to_meta(state_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+    """
+    Convert a state dict from torchtune's format to Meta's format. This function
+    doesn't handle any sharding or splitting of state dicts. It follows the
+    state_dict IN -> state_dict OUT pattern.
+
+    Args:
+        state_dict (Dict[str, torch.Tensor]): State dict in torchtune's format.
+
+    Returns:
+        Dict[str, torch.Tensor]: State dict in Meta's format.
+    """
+    converted_state_dict = {}
+    inverted_mapping_dict = {v: k for k, v in _SMOLLM_FROM_META.items()}
+    for key, value in state_dict.items():
+        new_key = get_mapped_key(key, inverted_mapping_dict)
+        converted_state_dict[new_key] = value
+
+    return converted_state_dict
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Convert SmolLM weights to Meta format."
+    )
+    parser.add_argument(
+        "input_dir",
+        type=str,
+        help="Path to directory containing checkpoint files",
+    )
+    parser.add_argument("output", type=str, help="Path to the output checkpoint")
+
+    args = parser.parse_args()
+
+    # Don't necessarily need to use TorchTune checkpointer, can just aggregate checkpoint files by ourselves.
+    checkpointer = FullModelHFCheckpointer(
+        checkpoint_dir=args.input_dir,
+        checkpoint_files=["model.safetensors"],
+        output_dir=".",
+        model_type="LLAMA",
+    )
+
+    print("Loading checkpoint...")
+    sd = checkpointer.load_checkpoint()
+
+    print("Converting checkpoint...")
+    sd = smollm_tune_to_meta(sd["model"])
+
+    torch.save(sd, args.output)
+    print(f"Checkpoint saved to {args.output}")
+
+
+if __name__ == "__main__":
+    main()