Added huggingface model loader to export.py

export.py

@@ -16,8 +16,9 @@ This script aspires to provide all of these conversions.
 """
 import struct
 import argparse
-import torch
 import numpy as np
+import torch
+from torch import nn
 
 from model import ModelArgs, Transformer
 
@@ -72,6 +73,10 @@ def legacy_export(model, filepath):
     # first write out the header
     hidden_dim = model.layers[0].feed_forward.w1.weight.shape[0]
     p = model.params
+    shared_classifier = torch.equal(model.tok_embeddings.weight, model.output.weight)
+    # legacy format uses negative/positive vocab size as a shared classifier flag
+    if not shared_classifier:
+        p.vocab_size = -p.vocab_size
     n_kv_heads = p.n_heads if p.n_kv_heads is None else p.n_kv_heads
     header = struct.pack('iiiiiii', p.dim, hidden_dim, p.n_layers, p.n_heads,
                                     n_kv_heads, p.vocab_size, p.max_seq_len)
@@ -103,11 +108,14 @@ def legacy_export(model, filepath):
         serialize_fp32(out_file, layer.feed_forward.w3.weight)
     # final rmsnorm
     serialize_fp32(out_file, model.norm.weight)
-    # note: no need to write final classifier weights due to weight sharing
     # freqs_cis
     serialize_fp32(out_file, model.freqs_cos[:p.max_seq_len])
     serialize_fp32(out_file, model.freqs_sin[:p.max_seq_len])
 
+    # final classifier weights
+    if not shared_classifier:
+        serialize_fp32(out_file, model.output.weight)
+
     # write to binary file
     out_file.close()
     print(f"wrote {filepath}")
@@ -136,8 +144,8 @@ def version1_export(model, filepath):
                                     n_kv_heads, p.vocab_size, p.max_seq_len)
     out_file.write(header)
     # 4) write some other flags
-    shared_classifier = 1 # we do share a classifier, write flag as a byte
+    shared_classifier = torch.equal(model.tok_embeddings.weight, model.output.weight)
-    out_file.write(struct.pack('B', shared_classifier))
+    out_file.write(struct.pack('B', int(shared_classifier)))
     pad = 256 - out_file.tell() # pad rest with zeros; tell returns current pos
     assert pad >= 0
     out_file.write(b'\0' * pad)
@@ -156,6 +164,8 @@ def version1_export(model, filepath):
         *[layer.feed_forward.w2.weight for layer in model.layers],
         *[layer.feed_forward.w3.weight for layer in model.layers],
     ]
+    if not shared_classifier:
+        weights.append(model.output.weight)
     for w in weights:
         serialize_fp32(out_file, w)
 
@@ -187,6 +197,9 @@ def version2_export(model, filepath, group_size=64):
         *[layer.feed_forward.w2.weight for layer in model.layers],
         *[layer.feed_forward.w3.weight for layer in model.layers],
     ]
+    shared_classifier = torch.equal(model.tok_embeddings.weight, model.output.weight)
+    if not shared_classifier:
+        weights.append(model.output.weight)
     for w in weights:
         assert w.numel() % group_size == 0, f"weight {i} has numel {w.numel()}, not a multiple of group_size {group_size}"
 
@@ -205,8 +218,7 @@ def version2_export(model, filepath, group_size=64):
                                     n_kv_heads, p.vocab_size, p.max_seq_len)
     out_file.write(header)
     # 4) write some other flags
-    shared_classifier = 1 # we do share a classifier, write flag as a byte
+    out_file.write(struct.pack('B', int(shared_classifier)))
-    out_file.write(struct.pack('B', shared_classifier))
     out_file.write(struct.pack('i', group_size)) # group size used for quantization
     pad = 256 - out_file.tell() # pad rest with zeros; tell returns current pos
     assert pad >= 0
@@ -247,6 +259,68 @@ def version2_export(model, filepath, group_size=64):
     out_file.close()
     print(f"wrote {filepath}")
 
+
+# -----------------------------------------------------------------------------
+# Load / import functions
+
+def load_checkpoint(checkpoint):
+
+    # load the provided model checkpoint
+    checkpoint_dict = torch.load(checkpoint, map_location='cpu')
+    gptconf = ModelArgs(**checkpoint_dict['model_args'])
+    model = Transformer(gptconf)
+    state_dict = checkpoint_dict['model']
+    unwanted_prefix = '_orig_mod.'
+    for k,v in list(state_dict.items()):
+        if k.startswith(unwanted_prefix):
+            state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
+    model.load_state_dict(state_dict, strict=False)
+    model.eval()
+    return model
+
+def load_hf_model(model_path):
+
+    from transformers import AutoModelForCausalLM
+
+    # load HF model
+    hf_model = AutoModelForCausalLM.from_pretrained(model_path)
+    hf_dict = hf_model.state_dict()
+
+    # convert LlamaConfig to ModelArgs
+    config = ModelArgs()
+    config.dim = hf_model.config.hidden_size
+    config.n_layers = hf_model.config.num_hidden_layers
+    config.n_heads = hf_model.config.num_attention_heads
+    config.n_kv_heads = hf_model.config.num_attention_heads
+    config.vocab_size = hf_model.config.vocab_size
+    config.hidden_dim = hf_model.config.intermediate_size
+    config.norm_eps = hf_model.config.rms_norm_eps
+    config.max_seq_len = hf_model.config.max_position_embeddings
+
+    # create a new Transformer object and set weights
+    model = Transformer(config)
+
+    model.tok_embeddings.weight = nn.Parameter(hf_dict['model.embed_tokens.weight'])
+    model.norm.weight = nn.Parameter(hf_dict['model.norm.weight'])
+
+    for layer in model.layers:
+        i = layer.layer_id
+        layer.attention_norm.weight = nn.Parameter(hf_dict[f'model.layers.{i}.input_layernorm.weight'])
+        layer.attention.wq.weight = nn.Parameter(hf_dict[f'model.layers.{i}.self_attn.q_proj.weight'])
+        layer.attention.wk.weight = nn.Parameter(hf_dict[f'model.layers.{i}.self_attn.k_proj.weight'])
+        layer.attention.wv.weight = nn.Parameter(hf_dict[f'model.layers.{i}.self_attn.v_proj.weight'])
+        layer.attention.wo.weight = nn.Parameter(hf_dict[f'model.layers.{i}.self_attn.o_proj.weight'])
+        layer.ffn_norm.weight = nn.Parameter(hf_dict[f'model.layers.{i}.post_attention_layernorm.weight'])
+        layer.feed_forward.w1.weight = nn.Parameter(hf_dict[f'model.layers.{i}.mlp.gate_proj.weight'])
+        layer.feed_forward.w2.weight = nn.Parameter(hf_dict[f'model.layers.{i}.mlp.down_proj.weight'])
+        layer.feed_forward.w3.weight = nn.Parameter(hf_dict[f'model.layers.{i}.mlp.up_proj.weight'])
+
+    # final classifier
+    model.output.weight = nn.Parameter(hf_dict['lm_head.weight'])
+    model.eval()
+    return model
+
+
 # -----------------------------------------------------------------------------
 # API entrypoint
 
@@ -267,21 +341,17 @@ if __name__ == "__main__":
 
     parser = argparse.ArgumentParser()
     parser.add_argument("filepath", type=str, help="the output filepath")
-    parser.add_argument("--checkpoint", default="", type=str, help="model checkpoint, .pt file")
+    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")
     args = parser.parse_args()
 
-    # load the provided model checkpoint
+    if args.checkpoint:
-    checkpoint_dict = torch.load(args.checkpoint, map_location='cpu')
+        model = load_checkpoint(args.checkpoint)
-    gptconf = ModelArgs(**checkpoint_dict['model_args'])
+    elif args.hf:
-    model = Transformer(gptconf)
+        model = load_hf_model(args.hf)
-    state_dict = checkpoint_dict['model']
+    else:
-    unwanted_prefix = '_orig_mod.'
+        parser.error("Input model missing: --checkpoint or --hf is required")
-    for k,v in list(state_dict.items()):
-        if k.startswith(unwanted_prefix):
-            state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
-    model.load_state_dict(state_dict, strict=False)
-    model.eval()
 
     # export
     model_export(model, args.filepath, args.version)

model.py

@@ -17,6 +17,7 @@ class ModelArgs:
     n_heads: int = 32
     n_kv_heads: Optional[int] = None
     vocab_size: int = 32000
+    hidden_dim: int = (4 * 4096)
     multiple_of: int = 256  # MLP hidden layer size will be multiple of
     norm_eps: float = 1e-5
     max_seq_len: int = 2048
@@ -186,7 +187,7 @@ class TransformerBlock(nn.Module):
         self.attention = Attention(args)
         self.feed_forward = FeedForward(
             dim=args.dim,
-            hidden_dim=4 * args.dim,
+            hidden_dim=args.hidden_dim,
             multiple_of=args.multiple_of,
             dropout=args.dropout,
         )

requirements.txt

@@ -5,3 +5,4 @@ sentencepiece==0.1.99
 torch==2.0.1
 tqdm==4.64.1
 wandb==0.15.5
+transformers==4.31.0