Fix llama 3 (#1671)

* fix llama 3

* fix black

Author: minhthuc2502

examples/llama3/README.md

@@ -0,0 +1,17 @@
+# Chat with Llama 3
+
+In addition to the Llama 2 example, this is an example how to implement an interactive chat session with Llama 3. This script updates the template used by Llama 3.
+
+## Installation
+You can follow the README in llama2 to setup the environment.
+
+## Start a chat session
+```
+python3 chat.py llama-3-7b-chat-ct2/
+```
+
+You can also set a system prompt on the command line:
+
+```
+python3 chat.py llama-3-7b-chat-ct2/ ["System prompt..."]
+```

examples/llama3/chat.py

@@ -0,0 +1,116 @@
+import os
+import sys
+
+import ctranslate2
+from transformers import AutoTokenizer
+
+
+def main():
+    model_dir = sys.argv[1]
+    system_prompt = sys.argv[2] if len(sys.argv) > 2 else None
+
+    print("Loading the model...")
+    generator = ctranslate2.Generator(model_dir, device="cuda")
+    tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
+
+    context_length = 4096
+    max_generation_length = 512
+    max_prompt_length = context_length - max_generation_length
+
+    dialog = []
+
+    if system_prompt:
+        dialog.append({"role": "system", "content": system_prompt})
+
+    while True:
+        print("")
+
+        user_prompt = input("You: ")
+
+        dialog.append({"role": "user", "content": user_prompt})
+
+        while True:
+            prompt_tokens = build_prompt(tokenizer, dialog)
+            if len(prompt_tokens) <= max_prompt_length:
+                break
+            # Remove old conversations to reduce the prompt size.
+            if system_prompt:
+                dialog = [dialog[0]] + dialog[3:]
+            else:
+                dialog = dialog[2:]
+
+        step_results = generator.generate_tokens(
+            prompt_tokens,
+            max_length=max_generation_length,
+            sampling_temperature=0.6,
+            sampling_topk=20,
+            sampling_topp=1,
+        )
+
+        print("")
+        print("Llama3: ", end="", flush=True)
+
+        text_output = ""
+
+        for word in generate_words(tokenizer, step_results):
+            print(word, end="", flush=True)
+            text_output += word
+
+        print("")
+
+        dialog.append({"role": "assistant", "content": text_output.strip()})
+
+
+def generate_words(tokenizer, step_results):
+    tokens_buffer = []
+
+    for step_result in step_results:
+        is_new_word = step_result.token.startswith("Ġ")
+
+        if is_new_word and tokens_buffer:
+            word = tokenizer.decode(tokens_buffer)
+            if word:
+                yield word
+            tokens_buffer = []
+
+        tokens_buffer.append(step_result.token_id)
+
+    if tokens_buffer:
+        word = tokenizer.decode(tokens_buffer)
+        if word:
+            yield word
+
+
+B_ID, E_ID, E_INST = "<|start_header_id|>", "<|end_header_id|>", "<|eot_id|>"
+
+
+def build_prompt(tokenizer, dialog):
+    begin_pos = 0
+    if dialog[0]["role"] == "system":
+        begin_pos = 1
+    assert all([msg["role"] == "user" for msg in dialog[begin_pos::2]]) and all(
+        [msg["role"] == "assistant" for msg in dialog[begin_pos + 1::2]]
+    ), (
+        "model only supports 'system', 'user' and 'assistant' roles, "
+        "starting with 'system', then 'user' and alternating (u/a/u/a/u...)"
+    )
+
+    dialog_tokens = sum([
+            tokenizer.tokenize(
+                f"{B_ID} {(item['role'])} {E_ID} {(item['content']).strip()} {E_INST}"
+                )
+            for item in dialog
+        ], [])
+    dialog_tokens = ["<|begin_of_text|>"] + dialog_tokens + tokenizer.tokenize(
+                f"{B_ID} assistant {E_ID}"
+                )
+
+    assert (
+        dialog[-1]["role"] == "user"
+    ), f"Last message must be from user, got {dialog[-1]['role']}"
+
+    return dialog_tokens
+
+
+if __name__ == "__main__":
+    main()

examples/llama3/requirements.txt

@@ -0,0 +1,3 @@
+ctranslate2>=4.3.0
+transformers[torch]==4.40.*
+accelerate

python/ctranslate2/converters/transformers.py

@@ -1436,7 +1436,9 @@ def set_vocabulary(self, spec, tokens):
     def set_config(self, config, model, tokenizer):
         config.bos_token = tokenizer.bos_token
         config.eos_token = tokenizer.eos_token
-        config.unk_token = tokenizer.unk_token
+        config.unk_token = (
+            tokenizer.unk_token if tokenizer.unk_token is not None else ""
+        )
         config.layer_norm_epsilon = model.config.rms_norm_eps
 
     def set_layer_norm(self, spec, layer_norm):