add the ability to have multi-round conversation with llama (#6769)

* update llama runner to decode single token

Pull Request resolved: #6703

Right now, we don't print the generated response in the eager runner until all tokens are generated. This is not good experience as we need to wait until all tokens are generated to see the response.

This PR updates it to decode each new token immediately after it is generated.
ghstack-source-id: 252924039

Differential Revision: [D65578306](https://our.internmc.facebook.com/intern/diff/D65578306/)

* add the ability to have multi-round conversation with llama

Ad the ability to have multi-round conversations with LLM. This will be helpful for testing long context length.

Differential Revision: [D65771122](https://our.internmc.facebook.com/intern/diff/D65771122/)

ghstack-source-id: 252934165
Pull Request resolved: #6758

---------

Co-authored-by: Lunwen He <[email protected]>

examples/models/llama/runner/eager.py

@@ -54,7 +54,7 @@ def build_args_parser() -> argparse.ArgumentParser:
     parser.add_argument(
         "--prompt",
         type=str,
-        default="Hello",
+        default=None,
     )
 
     parser.add_argument(
@@ -70,6 +70,13 @@ def build_args_parser() -> argparse.ArgumentParser:
         help="Show the tokens that were generated",
     )
 
+    parser.add_argument(
+        "--chat",
+        action="store_true",
+        default=False,
+        help="Have multi-turn chat with the model",
+    )
+
     return parser
 
 
@@ -78,9 +85,13 @@ def main() -> None:
     args = parser.parse_args()
 
     runner = EagerLlamaRunner(args)
-    generated_tokens = runner.text_completion(
-        prompt=args.prompt,
-        temperature=args.temperature,
+    generated_tokens = (
+        runner.chat_completion(temperature=args.temperature)
+        if args.chat
+        else runner.text_completion(
+            prompt=args.prompt,
+            temperature=args.temperature,
+        )
     )
     if args.show_tokens:
         print(f"Tokens: {generated_tokens}")

examples/models/llama/runner/generation.py

@@ -67,12 +67,13 @@ def generate(  # noqa: C901
         temperature: float = 0.8,
         top_p: float = 0.9,
         echo: bool = False,
+        pos_base: int = 0,
     ) -> List[int]:
         # prefill
         logits = self.forward(
             tokens=torch.tensor([prompt_tokens], dtype=torch.long, device=self.device),
             input_pos=(
-                torch.tensor([0], dtype=torch.long, device=self.device)
+                torch.tensor([pos_base], dtype=torch.long, device=self.device)
                 if self.params.use_kv_cache
                 else None
             ),
@@ -89,7 +90,9 @@ def generate(  # noqa: C901
                         [[current_token]], dtype=torch.long, device=self.device
                     ),
                     input_pos=torch.tensor(
-                        [len(tokens) - 1], dtype=torch.long, device=self.device
+                        [pos_base + len(tokens) - 1],
+                        dtype=torch.long,
+                        device=self.device,
                     ),
                 )
             else:
@@ -136,3 +139,49 @@ def text_completion(
             top_p=top_p,
             echo=echo,
         )
+
+    def chat_completion(
+        self,
+        temperature: float = 0.6,
+        top_p: float = 0.9,
+    ) -> List[int]:
+        """
+        Perform multi-turn chat with the language model.
+
+            Args:
+                prompt (str): Text prompt for completion.
+                temperature (float, optional): Temperature value for controlling randomness in sampling. Defaults to 0.6.
+                top_p (float, optional): Top-p probability threshold for nucleus sampling. Defaults to 0.9.
+                echo (bool, optional): Flag indicating whether to include prompt tokens in the generated output. Defaults to False.
+
+            Returns:
+                Generated list of tokens.
+
+            Note:
+                This method generates text completion for the provided prompt, employing nucleus sampling to introduce controlled randomness.
+        """
+        exit_prompt = "exit"
+        tokens = []
+        prompt = input("Me: ")
+        while prompt and prompt != exit_prompt:
+            print("LLM: ", end="", flush=True)
+            new_tokens = self.generate(
+                prompt_tokens=self.tokenizer.encode(
+                    self._format_prompt(prompt), bos=True, eos=False
+                ),
+                temperature=temperature,
+                top_p=top_p,
+                echo=True,
+                pos_base=len(tokens),
+            )
+            tokens.extend(new_tokens)
+            prompt = input("Me: ")
+        return tokens
+
+    def _format_prompt(self, prompt: str) -> str:
+        return f"""
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+You are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""