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-poisoned]

Author: helunwencser

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|>"""