patch.diff

diff --git a/src/transformers/models/llama/convert_llama_weights_to_hf.py b/src/transformers/models/llama/convert_llama_weights_to_hf.py
index a0fbe4680..50c7ed738 100644
--- a/src/transformers/models/llama/convert_llama_weights_to_hf.py
+++ b/src/transformers/models/llama/convert_llama_weights_to_hf.py
@@ -17,10 +17,10 @@ import json
 import os
 import shutil
 import warnings
-
+from typing import List
 import torch
 
-from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer, PreTrainedTokenizerFast
+from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer, PreTrainedTokenizerFast, GenerationConfig
 from transformers.convert_slow_tokenizer import TikTokenConverter
 
 
@@ -85,8 +85,12 @@ NUM_SHARDS = {
     "65B": 8,
     "70B": 8,
     "70Bf": 8,
+    "405B": 8,
+    "405B-MP16": 16,
 }
 
+CONTEXT_LENGTH_FOR_VERSION = {"3.1": 131072, "3": 8192, "2": 4096, "1": 2048}
+
 
 def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
     return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
@@ -107,9 +111,10 @@ def write_model(
     input_base_path,
     model_size=None,
     safe_serialization=True,
-    llama_version=1,
+    llama_version="1",
     vocab_size=None,
     num_shards=None,
+    instruct=False,
 ):
     os.makedirs(model_path, exist_ok=True)
     tmp_model_path = os.path.join(model_path, "tmp")
@@ -125,18 +130,11 @@ def write_model(
     dims_per_head = dim // n_heads
     base = params.get("rope_theta", 10000.0)
     inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
-    if base > 10000.0 and llama_version != 3:
+    if base > 10000.0 and float(llama_version) < 3:
         max_position_embeddings = 16384
     else:
-        # Depending on the Llama version, the default max_position_embeddings has different values.
-        if llama_version == 1:
-            max_position_embeddings = 2048
-        elif llama_version == 2:
-            max_position_embeddings = 4096
-        elif llama_version == 3:
-            max_position_embeddings = 8192
-
-    vocab_size = vocab_size if vocab_size is not None else 32000
+        max_position_embeddings = CONTEXT_LENGTH_FOR_VERSION[llama_version]
+
     if params.get("n_kv_heads", None) is not None:
         num_key_value_heads = params["n_kv_heads"]  # for GQA / MQA
         num_key_value_heads_per_shard = num_key_value_heads // num_shards
@@ -144,8 +142,7 @@ def write_model(
     else:  # compatibility with other checkpoints
         num_key_value_heads = n_heads
         num_key_value_heads_per_shard = n_heads_per_shard
-        key_value_dim = dims_per_head * num_key_value_heads
-    print(num_shards, num_key_value_heads, num_key_value_heads_per_shard, key_value_dim)
+        key_value_dim = dim
 
     # permute for sliced rotary
     def permute(w, n_heads, dim1=dim, dim2=dim):
@@ -159,11 +156,9 @@ def write_model(
         loaded = torch.load(os.path.join(input_base_path, "consolidated.00.pth"), map_location="cpu")
     else:
         # Sharded
-        loaded = [
-            torch.load(os.path.join(input_base_path, file), map_location="cpu")
-            for file in os.listdir(input_base_path)
-            if file.endswith(".pth")
-        ]
+        checkpoint_list = sorted([file for file in os.listdir(input_base_path) if file.endswith(".pth")])
+        print("Loading in order:", checkpoint_list)
+        loaded = [torch.load(os.path.join(input_base_path, file), map_location="cpu") for file in checkpoint_list]
     param_count = 0
     index_dict = {"weight_map": {}}
     for layer_i in range(n_layers):
@@ -263,7 +258,7 @@ def write_model(
             "lm_head.weight": loaded["output.weight"],
         }
     else:
-        concat_dim = 0 if llama_version == 3 else 1
+        concat_dim = 0 if llama_version in ['3', '3.1'] else 1
         state_dict = {
             "model.norm.weight": loaded[0]["norm.weight"],
             "model.embed_tokens.weight": torch.cat(
@@ -282,6 +277,18 @@ def write_model(
     write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
     ffn_dim_multiplier = params["ffn_dim_multiplier"] if "ffn_dim_multiplier" in params else 1
     multiple_of = params["multiple_of"] if "multiple_of" in params else 256
+
+    if llama_version in ['3', '3.1']:
+        bos_token_id = 128000
+
+        if instruct:
+            eos_token_id = [128001, 128009]
+        else:
+            eos_token_id = 128001
+    else:
+        bos_token_id = 1
+        eos_token_id = 2
+
     config = LlamaConfig(
         hidden_size=dim,
         intermediate_size=compute_intermediate_size(dim, ffn_dim_multiplier, multiple_of),
@@ -292,11 +299,21 @@ def write_model(
         vocab_size=vocab_size,
         rope_theta=base,
         max_position_embeddings=max_position_embeddings,
-        bos_token_id=128000 if llama_version == 3 else 1,
-        eos_token_id=128001 if llama_version == 3 else 2,
+        bos_token_id=bos_token_id,
+        eos_token_id=eos_token_id,
     )
     config.save_pretrained(tmp_model_path)
 
+    if instruct:
+        generation_config = GenerationConfig(
+            do_sample=True,
+            temperature=0.6,
+            top_p=0.9,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+        )
+        generation_config.save_pretrained(tmp_model_path)
+
     # Make space so we can load the model properly now.
     del state_dict
     del loaded
@@ -313,7 +330,7 @@ def write_model(
 
 
 class Llama3Converter(TikTokenConverter):
-    def __init__(self, vocab_file, num_reserved_special_tokens=256, **kwargs):
+    def __init__(self, vocab_file, special_tokens=None, instruct=False, model_max_length=None, **kwargs):
         super().__init__(vocab_file, **kwargs)
         tokenizer = self.converted()
         chat_template = (
@@ -327,34 +344,29 @@ class Llama3Converter(TikTokenConverter):
             "{% endfor %}"
             "{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}"
         )
-        num_reserved_special_tokens = 256
-        special_tokens = [
-            "<|begin_of_text|>",
-            "<|end_of_text|>",
-            "<|reserved_special_token_0|>",
-            "<|reserved_special_token_1|>",
-            "<|reserved_special_token_2|>",
-            "<|reserved_special_token_3|>",
-            "<|start_header_id|>",
-            "<|end_header_id|>",
-            "<|reserved_special_token_4|>",
-            "<|eot_id|>",  # end of turn
-        ] + [f"<|reserved_special_token_{i}|>" for i in range(5, num_reserved_special_tokens - 5)]
         tokenizer.add_special_tokens(special_tokens)
 
+        print("MODEL MAX LENGTH", model_max_length)
+
         self.tokenizer = PreTrainedTokenizerFast(
             tokenizer_object=tokenizer,
             bos_token="<|begin_of_text|>",
-            eos_token="<|end_of_text|>",
+            eos_token="<|end_of_text|>" if not instruct else "<|eot_id|>",
             chat_template=chat_template,
             model_input_names=["input_ids", "attention_mask"],
+            model_max_length=model_max_length,
         )
 
 
-def write_tokenizer(tokenizer_path, input_tokenizer_path, llama_version=2):
+def write_tokenizer(tokenizer_path, input_tokenizer_path, llama_version="2", special_tokens=None, instruct=False):
     tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
-    if llama_version == 3:
-        tokenizer = Llama3Converter(input_tokenizer_path).tokenizer
+    if llama_version in ["3", "3.1"]:
+        tokenizer = Llama3Converter(
+            input_tokenizer_path,
+            special_tokens,
+            instruct,
+            model_max_length=CONTEXT_LENGTH_FOR_VERSION[llama_version]
+        ).tokenizer
     else:
         tokenizer = tokenizer_class(input_tokenizer_path)
     print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.")
@@ -362,6 +374,37 @@ def write_tokenizer(tokenizer_path, input_tokenizer_path, llama_version=2):
     return tokenizer
 
 
+DEFAULT_LLAMA_SPECIAL_TOKENS = {
+    "3": [
+        "<|begin_of_text|>",
+        "<|end_of_text|>",
+        "<|reserved_special_token_0|>",
+        "<|reserved_special_token_1|>",
+        "<|reserved_special_token_2|>",
+        "<|reserved_special_token_3|>",
+        "<|start_header_id|>",
+        "<|end_header_id|>",
+        "<|reserved_special_token_4|>",
+        "<|eot_id|>",  # end of turn
+    ]
+    + [f"<|reserved_special_token_{i}|>" for i in range(5, 256 - 5)],
+    "3.1": [
+        "<|begin_of_text|>",
+        "<|end_of_text|>",
+        "<|reserved_special_token_0|>",
+        "<|reserved_special_token_1|>",
+        "<|finetune_right_pad_id|>",
+        "<|reserved_special_token_2|>",
+        "<|start_header_id|>",
+        "<|end_header_id|>",
+        "<|eom_id|>",  # end of message
+        "<|eot_id|>",  # end of turn
+        "<|python_tag|>",
+    ]
+    + [f"<|reserved_special_token_{i}|>" for i in range(3, 256 - 8)],
+}
+
+
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -383,9 +426,9 @@ def main():
     # Different Llama versions used different default values for max_position_embeddings, hence the need to be able to specify which version is being used.
     parser.add_argument(
         "--llama_version",
-        choices=[1, 2, 3],
-        default=1,
-        type=int,
+        choices=["1", "2", "3", "3.1"],
+        default="1",
+        type=str,
         help="Version of the Llama model to convert. Currently supports Llama1 and Llama2. Controls the context size",
     )
     parser.add_argument(
@@ -394,11 +437,34 @@ def main():
         type=int,
         help="The number of individual shards used for the model. Does not have to be the same as the number of consolidated_xx.pth",
     )
+    parser.add_argument(
+        "--special_tokens",
+        default=None,
+        type=List[str],
+        help="The list of special tokens that should be added to the model.",
+    )
+    parser.add_argument(
+        "--instruct",
+        default=False,
+        type=bool,
+        help="Whether the model is an instruct model or not. Will affect special tokens for llama 3.1.",
+    )
     args = parser.parse_args()
     if args.model_size is None and args.num_shards is None:
         raise ValueError("You have to set at least `num_shards` if you are not giving the `model_size`")
+    if args.special_tokens is None:
+        args.special_tokens = DEFAULT_LLAMA_SPECIAL_TOKENS[str(args.llama_version)]
+
     spm_path = os.path.join(args.input_dir, "tokenizer.model")
-    vocab_size = len(write_tokenizer(args.output_dir, spm_path, llama_version=args.llama_version))
+    vocab_size = len(
+        write_tokenizer(
+            args.output_dir,
+            spm_path,
+            llama_version=args.llama_version,
+            special_tokens=args.special_tokens,
+            instruct=args.instruct
+        )
+    )
     if args.model_size != "tokenizer_only":
         write_model(
             model_path=args.output_dir,
@@ -408,6 +474,7 @@ def main():
             llama_version=args.llama_version,
             vocab_size=vocab_size,
             num_shards=args.num_shards,
+            instruct=args.instruct
         )
 
 
diff --git a/src/transformers/models/llama/modeling_llama.py b/src/transformers/models/llama/modeling_llama.py
index 8cbe8fe35..65b4bb56b 100644
--- a/src/transformers/models/llama/modeling_llama.py
+++ b/src/transformers/models/llama/modeling_llama.py
@@ -90,6 +90,29 @@ class LlamaRMSNorm(nn.Module):
 
 ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)
 
+def apply_scaling(freqs: torch.Tensor):
+    # Values obtained from grid search
+    scale_factor = 8
+    low_freq_factor = 1
+    high_freq_factor = 4
+    old_context_len = 8192  # original llama3 length
+
+    low_freq_wavelen = old_context_len / low_freq_factor
+    high_freq_wavelen = old_context_len / high_freq_factor
+    new_freqs = []
+    for freq in freqs:
+        wavelen = 2 * math.pi / freq
+        if wavelen < high_freq_wavelen:
+            new_freqs.append(freq)
+        elif wavelen > low_freq_wavelen:
+            new_freqs.append(freq / scale_factor)
+        else:
+            assert low_freq_wavelen != high_freq_wavelen
+            smooth = (old_context_len / wavelen - low_freq_factor) / (
+                high_freq_factor - low_freq_factor
+            )
+            new_freqs.append((1 - smooth) * freq / scale_factor + smooth * freq)
+    return torch.tensor(new_freqs, dtype=freqs.dtype, device=freqs.device)
 
 class LlamaRotaryEmbedding(nn.Module):
     def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
@@ -99,6 +122,7 @@ class LlamaRotaryEmbedding(nn.Module):
         self.max_position_embeddings = max_position_embeddings
         self.base = base
         inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
+        inv_freq = apply_scaling(inv_freq)
         self.register_buffer("inv_freq", inv_freq, persistent=False)
         # For BC we register cos and sin cached
         self.max_seq_len_cached = max_position_embeddings