Delete dead code

Differential Revision: D61166041

Pull Request resolved: #4678

---------

Co-authored-by: helunwencser <[email protected]>

Author: kirklandsign

examples/models/llama2/runner/generation.py

@@ -14,11 +14,7 @@
 import torch.nn.functional as F
 from executorch.examples.models.llama2.llama_transformer import ModelArgs
 
-from executorch.examples.models.llama2.tokenizer.tiktoken import (
-    Dialog,
-    Message,
-    Tokenizer,
-)
+from executorch.examples.models.llama2.tokenizer.tiktoken import Tokenizer
 from executorch.extension.pybindings.portable_lib import _load_for_executorch
 
 
@@ -28,12 +24,6 @@ class CompletionPrediction(TypedDict, total=False):
     logprobs: List[float]  # not required
 
 
-class ChatPrediction(TypedDict, total=False):
-    generation: Message
-    tokens: List[str]  # not required
-    logprobs: List[float]  # not required
-
-
 def sample_top_p(probs, p):
     """
     Perform top-p (nucleus) sampling on a probability distribution.
@@ -225,72 +215,6 @@ def text_completion(
             ]
         return [{"generation": self.tokenizer.decode(t)} for t in generation_tokens]
 
-    def chat_completion(
-        self,
-        dialogs: List[Dialog],
-        temperature: float = 0.6,
-        top_p: float = 0.9,
-        max_gen_len: Optional[int] = None,
-        logprobs: bool = False,
-    ) -> List[ChatPrediction]:
-        """
-        Generate assistant responses for a list of conversational dialogs using the language generation model.
-
-        Args:
-            dialogs (List[Dialog]): List of conversational dialogs, where each dialog is a list of messages.
-            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.
-            max_gen_len (Optional[int], optional): Maximum length of the generated response sequence.
-                If not provided, it's set to the model's maximum sequence length minus 1.
-            logprobs (bool, optional): Flag indicating whether to compute token log probabilities. Defaults to False.
-
-        Returns:
-            List[ChatPrediction]: List of chat predictions, each containing the assistant's generated response.
-
-        Raises:
-            AssertionError: If the last message in a dialog is not from the user.
-            AssertionError: If the dialog roles are not in the required 'user', 'assistant', and optional 'system' order.
-
-        Note:
-            This method generates assistant responses for the provided conversational dialogs.
-            It employs nucleus sampling to introduce controlled randomness in text generation.
-            If logprobs is True, token log probabilities are computed for each generated token.
-        """
-        if max_gen_len is None:
-            max_gen_len = self.model.params.max_seq_len - 1
-
-        prompt_tokens = [
-            self.formatter.encode_dialog_prompt(dialog) for dialog in dialogs
-        ]
-        generation_tokens, generation_logprobs = self.generate(
-            prompt_tokens=prompt_tokens,
-            max_gen_len=max_gen_len,
-            temperature=temperature,
-            top_p=top_p,
-            logprobs=logprobs,
-        )
-        if logprobs:
-            return [
-                {
-                    "generation": {
-                        "role": "assistant",
-                        "content": self.tokenizer.decode(t),
-                    },
-                    "tokens": [self.tokenizer.decode([x]) for x in t],
-                    "logprobs": logprobs_i,
-                }
-                for t, logprobs_i in zip(generation_tokens, generation_logprobs)
-            ]
-        return [
-            {
-                "generation": {
-                    "role": "assistant",
-                    "content": self.tokenizer.decode(t),
-                },
-            }
-            for t in generation_tokens
-        ]
-
 
 def build_args_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser()