update generate prompt format

llama_stack/models/llama/prompt_format.py

@@ -35,7 +35,6 @@ from .llama3.template_data import (
     system_message_builtin_tools_only,
     system_message_custom_tools_only,
 )
-from .llama4.datatypes import LLMInput
 
 
 class TextCompletionContent(BaseModel):
@@ -74,21 +73,22 @@ class UseCase(BaseModel):
                 text += dialog
                 text += "\n\n"
                 continue
-
-            elif isinstance(dialog, TextCompletionContent):
-                input_tokens, output_tokens = generator.text_completion_raw(
-                    dialog.content,
-                    temperature=0.1,
-                    top_p=0.95,
-                    max_gen_len=64,
-                )
             else:
-                input_tokens, output_tokens = generator.chat_completion_raw(
-                    dialog,
-                    temperature=0.0,
-                    top_p=0.95,
-                    max_gen_len=self.max_gen_len,
+                batch = [dialog]
+                method = (
+                    generator.completion if isinstance(dialog, TextCompletionContent) else generator.chat_completion
                 )
+                input_tokens = []
+                output_tokens = []
+                for token_results in method(batch, echo=True, temperature=0.1, top_p=0.95):
+                    result = token_results[0]
+                    if result.source == "input":
+                        input_tokens.append(result.token)
+                    else:
+                        output_tokens.append(result.token)
+
+                    if result.finished:
+                        break
             text += "##### Input Prompt Format\n"
 
             # FIXME: This is added to undo the hack in chat_formatter where
@@ -124,27 +124,27 @@ class Llama4UseCase(UseCase):
 
         text = ""
         tokenizer = Tokenizer.get_instance()
-        temperature = 0.0
         for dialog in self.dialogs:
             if isinstance(dialog, str):
                 text += dialog
                 text += "\n\n"
                 continue
-
-            elif isinstance(dialog, TextCompletionContent):
-                # TODO pass the raw input and do the encoding in the text completion function
-                input_tokens = tokenizer.encode(dialog.content, bos=True, eos=False)
-                llm_input = LLMInput(tokens=input_tokens)
-                output_tokens, decoded_tokens, token_logprobs = generator.text_completion_raw(
-                    llm_input, temperature=temperature, max_gen_len=self.max_gen_len
-                )
-
             else:
-                input_tokens, output_tokens = generator.chat_completion_raw(
-                    dialog,
-                    temperature=temperature,
-                    max_gen_len=self.max_gen_len,
+                batch = [dialog]
+                method = (
+                    generator.completion if isinstance(dialog, TextCompletionContent) else generator.chat_completion
                 )
+                input_tokens = []
+                output_tokens = []
+                for token_results in method(batch, echo=True, temperature=0.0):
+                    result = token_results[0]
+                    if result.source == "input":
+                        input_tokens.append(result.token)
+                    else:
+                        output_tokens.append(result.token)
+
+                    if result.finished:
+                        break
 
             text += "##### Input Prompt Format\n"
             text += _code_block(tokenizer.decode(input_tokens))

scripts/generate_prompt_format.py

@@ -5,13 +5,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# top-level folder for each specific model found within the models/ directory at
-# the top-level of this source tree.
-
 # Run this script:
 # torchrun --nproc_per_node=8 scripts/generate_prompt_format.py meta-llama/Llama-4-17B-Omni-Instruct-BF16-16E ~/.llama/checkpoints/Llama-4-17B-Omni-Instruct-BF16-16E/ llama_stack.models.llama.llama4.prompts llama_stack/models/llama/llama4/prompt_format.md
 
@@ -22,16 +15,9 @@ from pathlib import Path
 
 import fire
 
+from llama_stack.models.llama.llama3.generation import Llama3
+from llama_stack.models.llama.llama4.generation import Llama4
 from llama_stack.models.llama.sku_list import resolve_model
-from llama_stack.providers.inline.inference.meta_reference.config import (
-    MetaReferenceInferenceConfig,
-)
-from llama_stack.providers.inline.inference.meta_reference.llama3.generation import (
-    Llama3,
-)
-from llama_stack.providers.inline.inference.meta_reference.llama4.generation import (
-    Llama4,
-)
 
 THIS_DIR = Path(__file__).parent.resolve()
 
@@ -50,24 +36,12 @@ def run_main(
     if not llama_model:
         raise ValueError(f"Model {model_id} not found")
 
-    if not llama4:
-        config = MetaReferenceInferenceConfig(
-            model=model_id,
-            max_seq_len=4096,
-            max_batch_size=1,
-            checkpoint_dir=checkpoint_dir,
-        )
-        generator = Llama3.build(
-            config=config,
-            model_id=model_id,
-            llama_model=llama_model,
-        )
-    else:
-        generator = Llama4.build(
-            ckpt_dir=checkpoint_dir,
-            max_seq_len=4096,
-            max_batch_size=1,
-        )
+    cls = Llama4 if llama4 else Llama3
+    generator = cls.build(
+        ckpt_dir=checkpoint_dir,
+        max_seq_len=4096,
+        max_batch_size=1,
+    )
 
     use_cases = module.usecases()
     text = ""