Art/llama3 support

model.py

@@ -44,14 +44,14 @@ def from_name(cls, name: str):
         if name in transformer_configs:
             return cls(**transformer_configs[name])
         # fuzzy search
-        config = [config for config in transformer_configs if config in str(name).upper() or config in str(name)]
+        config = [config for config in transformer_configs if config.lower() in str(name).lower() or config.lower() in str(name).lower()]
 
         # We may have two or more configs matched (e.g. "7B" and "Mistral-7B"). Find the best config match,
         # take longer name (as it have more symbols matched)
         if len(config) > 1:
             config.sort(key=len, reverse=True)
             assert len(config[0]) != len(config[1]), name # make sure only one 'best' match
-
+            
         return cls(**transformer_configs[config[0]])
 
 
@@ -65,7 +65,8 @@ def from_name(cls, name: str):
     "Mistral-7B": dict(n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, vocab_size=32000),
     "stories15M": dict(n_layer=6, n_head=6, dim=288),
     "stories110M": dict(n_layer=12, n_head=12, dim=768),
-    "Llama-3-8B": dict(block_size=8192, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, vocab_size=128256),
+    "llama-3-8b": dict(block_size=8192, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, vocab_size=128256),
+    "llama-3-70b": dict(block_size=8192, n_layer=80, n_head=64, n_local_heads=8, dim=8192, intermediate_size=28672, vocab_size=128256),
 }
 
 class KVCache(nn.Module):

requirements.txt

@@ -1,3 +1,4 @@
 torch
 sentencepiece
 tiktoken
+blobfile

scripts/convert_hf_checkpoint.py

@@ -28,62 +28,33 @@ def convert_hf_checkpoint(
     if model_name is None:
         model_name = checkpoint_dir.name
 
-    # Llama 3 8B doesn't need conversion; instead, the original/consolidated.NN.pth files
-    # need to be copied into model.pth.
-    # Llama 3 70B can't be easily merged into one model.pth file, though, since names of the
-    # weights is state dict are the same in each consolidated.NN.pth file. Thus, it is not
-    # currently supported.
-    # Along this, we need to copy the original/tokenizer.model file to tokenizer.model.tiktoken
-    is_llama3 = "Llama-3" in model_name
-    if is_llama3:
-        # Check if we have multiple original/consolidated.NN.pth files and report error
-        # if we do for Llama 3.
-        original_dir = checkpoint_dir / "original"
-        pattern = re.compile(r"^consolidated\.\d{2}\.pth$")
-        bin_files = [bin for bin in original_dir.iterdir() if pattern.match(bin.name)]
-        if len(bin_files) > 1:
-            raise ValueError(
-                f"Multiple consolidated.NN.pth files found in {original_dir}. "
-                "Merging them into one model.pth file is not supported for Llama 3.")
-
-
     config = ModelArgs.from_name(model_name)
     print(f"Model config {config.__dict__}")
 
     # Load the json file containing weight mapping
-    if not is_llama3:
-        model_map_json = checkpoint_dir / "pytorch_model.bin.index.json"
-
-        assert model_map_json.is_file()
-
-        with open(model_map_json) as json_map:
-            bin_index = json.load(json_map)
-
-        weight_map = {
-            "model.embed_tokens.weight": "tok_embeddings.weight",
-            "model.layers.{}.self_attn.q_proj.weight": "layers.{}.attention.wq.weight",
-            "model.layers.{}.self_attn.k_proj.weight": "layers.{}.attention.wk.weight",
-            "model.layers.{}.self_attn.v_proj.weight": "layers.{}.attention.wv.weight",
-            "model.layers.{}.self_attn.o_proj.weight": "layers.{}.attention.wo.weight",
-            'model.layers.{}.self_attn.rotary_emb.inv_freq': None,
-            'model.layers.{}.mlp.gate_proj.weight': 'layers.{}.feed_forward.w1.weight',
-            "model.layers.{}.mlp.up_proj.weight": "layers.{}.feed_forward.w3.weight",
-            "model.layers.{}.mlp.down_proj.weight": "layers.{}.feed_forward.w2.weight",
-            "model.layers.{}.input_layernorm.weight": "layers.{}.attention_norm.weight",
-            "model.layers.{}.post_attention_layernorm.weight": "layers.{}.ffn_norm.weight",
-            "model.norm.weight": "norm.weight",
-            "lm_head.weight": "output.weight",
-        }
-        bin_files = {checkpoint_dir / bin for bin in bin_index["weight_map"].values()}
-    else:
-        # There is no separate pytorch_model.bin.index.json file for llama3.
-        # Instead, we will just use all original/consolidated.NN.pth files.
-        # so, we use model.safetensors.index.json
-        weight_map = None
-        original_dir = checkpoint_dir / "original"
-        pattern = re.compile(r"^consolidated\.\d{2}\.pth$")
-        bin_files = {bin for bin in original_dir.iterdir() if pattern.match(bin.name)}
-
+    model_map_json = checkpoint_dir / "pytorch_model.bin.index.json"
+
+    assert model_map_json.is_file()
+
+    with open(model_map_json) as json_map:
+        bin_index = json.load(json_map)
+
+    weight_map = {
+        "model.embed_tokens.weight": "tok_embeddings.weight",
+        "model.layers.{}.self_attn.q_proj.weight": "layers.{}.attention.wq.weight",
+        "model.layers.{}.self_attn.k_proj.weight": "layers.{}.attention.wk.weight",
+        "model.layers.{}.self_attn.v_proj.weight": "layers.{}.attention.wv.weight",
+        "model.layers.{}.self_attn.o_proj.weight": "layers.{}.attention.wo.weight",
+        'model.layers.{}.self_attn.rotary_emb.inv_freq': None,
+        'model.layers.{}.mlp.gate_proj.weight': 'layers.{}.feed_forward.w1.weight',
+        "model.layers.{}.mlp.up_proj.weight": "layers.{}.feed_forward.w3.weight",
+        "model.layers.{}.mlp.down_proj.weight": "layers.{}.feed_forward.w2.weight",
+        "model.layers.{}.input_layernorm.weight": "layers.{}.attention_norm.weight",
+        "model.layers.{}.post_attention_layernorm.weight": "layers.{}.ffn_norm.weight",
+        "model.norm.weight": "norm.weight",
+        "lm_head.weight": "output.weight",
+    }
+    bin_files = {checkpoint_dir / bin for bin in bin_index["weight_map"].values()}
 
     def permute(w, n_head):
         dim = config.dim
@@ -98,36 +69,33 @@ def permute(w, n_head):
         state_dict = torch.load(str(file), map_location="cpu", mmap=True, weights_only=True)
         merged_result.update(state_dict)
     final_result = {}
-    if weight_map is not None:
-        for key, value in merged_result.items():
-            if "layers" in key:
-                abstract_key = re.sub(r'(\d+)', '{}', key)
-                layer_num = re.search(r'\d+', key).group(0)
-                new_key = weight_map[abstract_key]
-                if new_key is None:
-                    continue
-                new_key = new_key.format(layer_num)
-            else:
-                new_key = weight_map[key]
-
-            final_result[new_key] = value
-
-        for key in tuple(final_result.keys()):
-            if "wq" in key:
-                q = final_result[key]
-                k = final_result[key.replace("wq", "wk")]
-                v = final_result[key.replace("wq", "wv")]
-                q = permute(q, config.n_head)
-                k = permute(k, config.n_local_heads)
-                final_result[key.replace("wq", "wqkv")] = torch.cat([q, k, v])
-                del final_result[key]
-                del final_result[key.replace("wq", "wk")]
-                del final_result[key.replace("wq", "wv")]
-    else:
-        final_result = merged_result
+    for key, value in merged_result.items():
+        if "layers" in key:
+            abstract_key = re.sub(r'(\d+)', '{}', key)
+            layer_num = re.search(r'\d+', key).group(0)
+            new_key = weight_map[abstract_key]
+            if new_key is None:
+                continue
+            new_key = new_key.format(layer_num)
+        else:
+            new_key = weight_map[key]
+
+        final_result[new_key] = value
+
+    for key in tuple(final_result.keys()):
+        if "wq" in key:
+            q = final_result[key]
+            k = final_result[key.replace("wq", "wk")]
+            v = final_result[key.replace("wq", "wv")]
+            q = permute(q, config.n_head)
+            k = permute(k, config.n_local_heads)
+            final_result[key.replace("wq", "wqkv")] = torch.cat([q, k, v])
+            del final_result[key]
+            del final_result[key.replace("wq", "wk")]
+            del final_result[key.replace("wq", "wv")]
     print(f"Saving checkpoint to {checkpoint_dir / 'model.pth'}")
     torch.save(final_result, checkpoint_dir / "model.pth")
-    if is_llama3:
+    if 'llama-3' in model_name.lower():
         original_dir = checkpoint_dir / "original"
         tokenizer_model = original_dir / "tokenizer.model"
         tokenizer_model_tiktoken = checkpoint_dir / "tokenizer.model"

tokenizer.py

@@ -105,7 +105,8 @@ def get_tokenizer(tokenizer_model_path, model_name):
     Returns:
     - TokenizerInterface: An instance of a tokenizer.
     """
-    if "Llama-3" in str(model_name):
+
+    if "llama-3" in str(model_name).lower():
         return TiktokenWrapper(tokenizer_model_path)
     else:
         return SentencePieceWrapper(tokenizer_model_path)