feat: better using get_default_tool_prompt_format (#1360)

Summary:
https://github.com/meta-llama/llama-stack/pull/1214 introduced
`get_default_tool_prompt_format` but tried to use it on the raw
identifier.

Here we move calling this func later in the stack and rely on the
inference provider to resolve the raw identifier into llama model, then
call get_default_tool_prompt_format.

Test Plan:
```
LLAMA_STACK_CONFIG=ollama pytest -s -v tests/client-sdk/inference/test_text_inference.py::test_text_chat_completion_with_tool_calling_and_non_streaming --inference-model=llama3.2:3b-instruct-fp16 --vision-inference-model=""
```

Before:

<img width="1288" alt="image"
src="https://github.com/user-attachments/assets/918c7839-1f45-4540-864e-4b842cc367df"
/>

After:
<img width="1522" alt="image"
src="https://github.com/user-attachments/assets/447d78af-b3b9-4837-8cb7-6ac549005efe"
/>

llama_stack/distribution/routers/routers.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import copy
 from typing import Any, AsyncGenerator, Dict, List, Optional
 
 from llama_stack import logcat
@@ -54,7 +53,6 @@ from llama_stack.apis.tools import (
 )
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
 from llama_stack.providers.datatypes import RoutingTable
-from llama_stack.providers.utils.inference.prompt_adapter import get_default_tool_prompt_format
 
 
 class VectorIORouter(VectorIO):
@@ -181,9 +179,6 @@ class InferenceRouter(Inference):
                 params["tool_prompt_format"] = tool_prompt_format
             tool_config = ToolConfig(**params)
 
-        tool_config = copy.copy(tool_config)
-        tool_config.tool_prompt_format = tool_config.tool_prompt_format or get_default_tool_prompt_format(model_id)
-
         tools = tools or []
         if tool_config.tool_choice == ToolChoice.none:
             tools = []

llama_stack/providers/utils/inference/prompt_adapter.py

@@ -15,6 +15,7 @@ from typing import List, Optional, Tuple, Union
 import httpx
 from PIL import Image as PIL_Image
 
+from llama_stack import logcat
 from llama_stack.apis.common.content_types import (
     ImageContentItem,
     InterleavedContent,
@@ -253,7 +254,8 @@ async def chat_completion_request_to_prompt(request: ChatCompletionRequest, llam
 
     formatter = ChatFormat(tokenizer=Tokenizer.get_instance())
     model_input = formatter.encode_dialog_prompt(
-        request.messages, tool_prompt_format=request.tool_config.tool_prompt_format
+        request.messages,
+        tool_prompt_format=request.tool_config.tool_prompt_format or get_default_tool_prompt_format(llama_model),
     )
     return formatter.tokenizer.decode(model_input.tokens)
 
@@ -267,7 +269,8 @@ async def chat_completion_request_to_model_input_info(
 
     formatter = ChatFormat(tokenizer=Tokenizer.get_instance())
     model_input = formatter.encode_dialog_prompt(
-        request.messages, tool_prompt_format=request.tool_config.tool_prompt_format
+        request.messages,
+        tool_prompt_format=request.tool_config.tool_prompt_format or get_default_tool_prompt_format(llama_model),
     )
     return (
         formatter.tokenizer.decode(model_input.tokens),
@@ -461,6 +464,7 @@ def _get_tool_choice_prompt(tool_choice: ToolChoice | str, tools: List[ToolDefin
 def get_default_tool_prompt_format(model: str) -> ToolPromptFormat:
     llama_model = resolve_model(model)
     if llama_model is None:
+        logcat.warning("inference", f"Could not resolve model {model}, defaulting to json tool prompt format")
         return ToolPromptFormat.json
 
     if llama_model.model_family == ModelFamily.llama3_1 or (