|
| 1 | +import argparse |
| 2 | + |
| 3 | +import json |
| 4 | +import os |
| 5 | +from typing import Dict |
| 6 | + |
| 7 | +import torch |
| 8 | +from safetensors.torch import load_file |
| 9 | + |
| 10 | +from torchtune.models.convert_weights import get_mapped_key |
| 11 | + |
| 12 | + |
| 13 | +# Weight mappings from Gemma's checkpoint to ExecuTorch's transformer parameters. |
| 14 | +_GEMMA_TO_EXECUTORCH = { |
| 15 | + "model.embed_tokens.weight": "tok_embeddings.weight", |
| 16 | + "model.norm.weight": "norm.weight", |
| 17 | + "model.layers.{}.self_attn.k_proj.weight": "layers.{}.attention.wk.weight", |
| 18 | + "model.layers.{}.self_attn.q_proj.weight": "layers.{}.attention.wq.weight", |
| 19 | + "model.layers.{}.self_attn.v_proj.weight": "layers.{}.attention.wv.weight", |
| 20 | + "model.layers.{}.self_attn.o_proj.weight": "layers.{}.attention.wo.weight", |
| 21 | + "model.layers.{}.input_layernorm.weight": "layers.{}.attention_norm.weight", |
| 22 | + "model.layers.{}.post_attention_layernorm.weight": "layers.{}.ffn_norm.weight", |
| 23 | + "model.layers.{}.mlp.gate_proj.weight": "layers.{}.feed_forward.w1.weight", |
| 24 | + "model.layers.{}.mlp.down_proj.weight": "layers.{}.feed_forward.w2.weight", |
| 25 | + "model.layers.{}.mlp.up_proj.weight": "layers.{}.feed_forward.w3.weight", |
| 26 | +} |
| 27 | + |
| 28 | + |
| 29 | +def gemma_to_executorch( |
| 30 | + state_dict: Dict[str, torch.Tensor] |
| 31 | +) -> Dict[str, torch.Tensor]: |
| 32 | + """ |
| 33 | + Convert the state dict so that it matches what ExecuTorch's transformer definition expects. |
| 34 | + """ |
| 35 | + converted_state_dict = {} |
| 36 | + for key, value in state_dict.items(): |
| 37 | + new_key = get_mapped_key(key, _GEMMA_TO_EXECUTORCH) |
| 38 | + converted_state_dict[new_key] = value |
| 39 | + converted_state_dict["output.weight"] = converted_state_dict[ |
| 40 | + "tok_embeddings.weight" |
| 41 | + ] |
| 42 | + return converted_state_dict |
| 43 | + |
| 44 | + |
| 45 | +def load_checkpoint_from_safetensors(input_dir: str) -> Dict: |
| 46 | + index_path = os.path.join(input_dir, "model.safetensors.index.json") |
| 47 | + if os.path.exists(index_path): |
| 48 | + # Sharded checkpoint. |
| 49 | + with open(index_path, "r") as f: |
| 50 | + index = json.load(f) |
| 51 | + weight_map = index["weight_map"] |
| 52 | + checkpoint_shards = sorted(set(weight_map.values())) |
| 53 | + |
| 54 | + # Load all the shards into memory |
| 55 | + shard_to_weights = {} |
| 56 | + for shard in checkpoint_shards: |
| 57 | + shard_to_weights[shard] = load_file(os.path.join(input_dir, shard)) |
| 58 | + |
| 59 | + # Merge tensors into consolidated state dict. |
| 60 | + merged_state_dict = {} |
| 61 | + for weight_name, shard in weight_map.items(): |
| 62 | + tensor = shard_to_weights[shard][weight_name] |
| 63 | + merged_state_dict[weight_name] = tensor |
| 64 | + return merged_state_dict |
| 65 | + else: |
| 66 | + # Single checkpoint. |
| 67 | + state_dict = load_file(os.path.join(input_dir, "model.safetensors")) |
| 68 | + return state_dict |
| 69 | + |
| 70 | + |
| 71 | +def load_checkpoint(input_dir: str) -> Dict: |
| 72 | + pytorch_path = os.path.join(input_dir, "pytorch_model.bin") |
| 73 | + if os.path.exists(pytorch_path): |
| 74 | + print("Loading checkpoint from PyTorch .bin file") |
| 75 | + return torch.load(pytorch_path, map_location="cpu", weights_only=True) |
| 76 | + print("Loading checkpoint from safetensors directory") |
| 77 | + return load_checkpoint_from_safetensors(input_dir) |
| 78 | + |
| 79 | + |
| 80 | +def convert_weights(input_dir: str, output_file: str) -> None: |
| 81 | + print("Loading checkpoint...") |
| 82 | + sd = load_checkpoint(input_dir) |
| 83 | + print("Converting checkpoint...") |
| 84 | + sd = gemma_to_executorch(sd) |
| 85 | + print("Saving checkpoint...") |
| 86 | + torch.save(sd, output_file) |
| 87 | + print("Done.") |
| 88 | + |
| 89 | + |
| 90 | +def main(): |
| 91 | + parser = argparse.ArgumentParser( |
| 92 | + description="Convert Gemma weights to ExecuTorch transformer format." |
| 93 | + ) |
| 94 | + parser.add_argument( |
| 95 | + "input_dir", |
| 96 | + type=str, |
| 97 | + help="Path to directory containing safetensor checkpoint files, or PyTorch checkpoint file.", |
| 98 | + ) |
| 99 | + parser.add_argument("output", type=str, help="Path to the output checkpoint") |
| 100 | + |
| 101 | + args = parser.parse_args() |
| 102 | + convert_weights(args.input_dir, args.output) |
| 103 | + |
| 104 | + |
| 105 | +if __name__ == "__main__": |
| 106 | + main() |
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