Added load_meta_model() to export.py

export.py

@@ -19,6 +19,9 @@ import gzip
 import shutil
 import struct
 import argparse
+import json
+from pathlib import Path
+
 import numpy as np
 import torch
 from torch import nn
@@ -30,7 +33,7 @@ from model import ModelArgs, Transformer
 
 def serialize_fp32(file, tensor):
     """ writes one fp32 tensor to file that is open in wb mode """
-    d = tensor.detach().cpu().view(-1).numpy().astype(np.float32)
+    d = tensor.detach().cpu().view(-1).to(torch.float32).numpy()
     b = struct.pack(f'{len(d)}f', *d)
     file.write(b)
 
@@ -281,6 +284,71 @@ def load_checkpoint(checkpoint):
     model.eval()
     return model
 
+def load_meta_model(model_path):
+    params_path = os.path.join(model_path, 'params.json')
+    with open(params_path) as f:
+        params = json.load(f)
+        print(params)
+
+    model_paths = sorted(list(Path(model_path).glob('consolidated.*.pth')))
+    models = [torch.load(p, map_location='cpu') for p in model_paths]
+
+    def concat_weights(models):
+        state_dict = {}
+        for name in list(models[0]):
+            tensors = [model[name] for model in models]
+            if len(tensors) == 1 or len(tensors[0].shape) == 1:
+                state_dict[name] = tensors[0]
+                continue
+            is_axis_1 = (
+                name.startswith('tok_embeddings.')
+                or name.endswith('.attention.wo.weight')
+                or name.endswith('.feed_forward.w2.weight')
+            )
+            axis = 1 if is_axis_1 else 0
+            state_dict[name] = torch.cat(tensors, dim=axis)
+            for model in models:
+                del model[name]
+        return state_dict
+
+    state_dict = concat_weights(models)
+    del models
+
+    # set ModelArgs
+    config = ModelArgs()
+    config.dim = params["dim"]
+    config.n_layers = params["n_layers"]
+    config.n_heads = params["n_heads"]
+    config.n_kv_heads = params.get('n_kv_heads') or params['n_heads']
+    config.multiple_of = params["multiple_of"]
+    config.norm_eps = params["norm_eps"]
+
+    config.vocab_size = 32000
+    config.max_seq_len = 2048
+
+    # create a new Transformer object and set weights
+    model = Transformer(config)
+
+    model.tok_embeddings.weight = nn.Parameter(state_dict['tok_embeddings.weight'])
+    model.norm.weight = nn.Parameter(state_dict['norm.weight'])
+
+    for layer in model.layers:
+        i = layer.layer_id
+        layer.attention_norm.weight = nn.Parameter(state_dict[f'layers.{i}.attention_norm.weight'])
+        layer.attention.wq.weight = nn.Parameter(state_dict[f'layers.{i}.attention.wq.weight'])
+        layer.attention.wk.weight = nn.Parameter(state_dict[f'layers.{i}.attention.wk.weight'])
+        layer.attention.wv.weight = nn.Parameter(state_dict[f'layers.{i}.attention.wv.weight'])
+        layer.attention.wo.weight = nn.Parameter(state_dict[f'layers.{i}.attention.wo.weight'])
+        layer.ffn_norm.weight = nn.Parameter(state_dict[f'layers.{i}.ffn_norm.weight'])
+        layer.feed_forward.w1.weight = nn.Parameter(state_dict[f'layers.{i}.feed_forward.w1.weight'])
+        layer.feed_forward.w2.weight = nn.Parameter(state_dict[f'layers.{i}.feed_forward.w2.weight'])
+        layer.feed_forward.w3.weight = nn.Parameter(state_dict[f'layers.{i}.feed_forward.w3.weight'])
+
+    # final classifier
+    model.output.weight = nn.Parameter(state_dict['output.weight'])
+    model.eval()
+    return model
+
 def load_hf_model(model_path):
 
     try:
@@ -381,17 +449,19 @@ if __name__ == "__main__":
 
     parser = argparse.ArgumentParser()
     parser.add_argument("filepath", type=str, help="the output filepath")
-    parser.add_argument("--checkpoint", type=str, help="model checkpoint, .pt file")
-    parser.add_argument("--hf", type=str, help="huggingface model")
     parser.add_argument("--version", default=0, type=int, help="the version to export with")
+    group = parser.add_mutually_exclusive_group(required=True)
+    group.add_argument("--checkpoint", type=str, help="model checkpoint, .pt file")
+    group.add_argument("--meta-llama", type=str, help="meta llama model path")
+    group.add_argument("--hf", type=str, help="huggingface model path")
     args = parser.parse_args()
 
     if args.checkpoint:
         model = load_checkpoint(args.checkpoint)
+    elif args.meta_llama:
+        model = load_meta_model(args.meta_llama)
     elif args.hf:
         model = load_hf_model(args.hf)
-    else:
-        parser.error("Input model missing: --checkpoint or --hf is required")
 
     if model is None:
         parser.error("Can't load input model!")