Allow overridding checkpoint_dir via config

llama_stack/providers/impls/meta_reference/inference/config.py

@@ -29,6 +29,10 @@ class MetaReferenceInferenceConfig(BaseModel):
     # (including our testing code) who might be using llama-stack as a library.
     create_distributed_process_group: bool = True
 
+    # By default, the implementation will look at ~/.llama/checkpoints/<model> but you
+    # can override by specifying the directory explicitly
+    checkpoint_dir: Optional[str] = None
+
     @field_validator("model")
     @classmethod
     def validate_model(cls, model: str) -> str:

llama_stack/providers/impls/meta_reference/inference/generation.py

@@ -98,7 +98,10 @@ class Llama:
             sys.stdout = open(os.devnull, "w")
 
         start_time = time.time()
-        ckpt_dir = model_checkpoint_dir(model)
+        if config.checkpoint_dir:
+            ckpt_dir = config.checkpoint_dir
+        else:
+            ckpt_dir = model_checkpoint_dir(model)
 
         checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
         assert len(checkpoints) > 0, f"no checkpoint files found in {ckpt_dir}"
@@ -119,9 +122,7 @@ class Llama:
             **params,
         )
 
-        tokenizer_path = os.path.join(ckpt_dir, "tokenizer.model")
-        tokenizer = Tokenizer(model_path=tokenizer_path)
-
+        tokenizer = Tokenizer.get_instance()
         assert (
             model_args.vocab_size == tokenizer.n_words
         ), f"model_args vocab = {model_args.vocab_size} but tokenizer vocab = {tokenizer.n_words}"
@@ -170,14 +171,16 @@ class Llama:
         logprobs: bool = False,
         echo: bool = False,
         include_stop_token: bool = False,
+        print_input_tokens: bool = False,
     ) -> Generator:
         params = self.model.params
 
-        # input_tokens = [
-        #     self.formatter.vision_token if t == 128256 else t
-        #     for t in model_input.tokens
-        # ]
-        # cprint("Input to model -> " + self.tokenizer.decode(input_tokens), "red")
+        if print_input_tokens:
+            input_tokens = [
+                self.formatter.vision_token if t == 128256 else t
+                for t in model_input.tokens
+            ]
+            cprint("Input to model -> " + self.tokenizer.decode(input_tokens), "red")
         prompt_tokens = [model_input.tokens]
 
         bsz = 1