Use inference APIs for running llama guard

Test Plan:

First, start a TGI container with `meta-llama/Llama-Guard-3-8B` model
serving on port 5099. See https://github.com/meta-llama/llama-stack/pull/53 and its
description for how.

Then run llama-stack with the following run config:

```
image_name: safety
docker_image: null
conda_env: safety
apis_to_serve:
- models
- inference
- shields
- safety
api_providers:
  inference:
    providers:
    - remote::tgi
  safety:
    providers:
    - meta-reference
  telemetry:
    provider_id: meta-reference
    config: {}
routing_table:
  inference:
  - provider_id: remote::tgi
    config:
      url: http://localhost:5099
      api_token: null
      hf_endpoint_name: null
    routing_key: Llama-Guard-3-8B
  safety:
  - provider_id: meta-reference
    config:
      llama_guard_shield:
        model: Llama-Guard-3-8B
        excluded_categories: []
        disable_input_check: false
        disable_output_check: false
      prompt_guard_shield: null
    routing_key: llama_guard
```

Now simply run `python -m llama_stack.apis.safety.client localhost
<port>` and check that the llama_guard shield calls run correctly. (The
injection_shield calls fail as expected since we have not set up a
router for them.)

llama_stack/providers/impls/meta_reference/safety/__init__.py

@@ -7,11 +7,11 @@
 from .config import SafetyConfig
 
 
-async def get_provider_impl(config: SafetyConfig, _deps):
+async def get_provider_impl(config: SafetyConfig, deps):
     from .safety import MetaReferenceSafetyImpl
 
     assert isinstance(config, SafetyConfig), f"Unexpected config type: {type(config)}"
 
-    impl = MetaReferenceSafetyImpl(config)
+    impl = MetaReferenceSafetyImpl(config, deps)
     await impl.initialize()
     return impl

llama_stack/providers/impls/meta_reference/safety/safety.py

@@ -7,8 +7,10 @@
 from llama_models.sku_list import resolve_model
 
 from llama_stack.distribution.utils.model_utils import model_local_dir
+from llama_stack.apis.inference import *  # noqa: F403
 from llama_stack.apis.safety import *  # noqa: F403
 from llama_models.llama3.api.datatypes import *  # noqa: F403
+from llama_stack.distribution.datatypes import Api
 
 from llama_stack.providers.impls.meta_reference.safety.shields.base import (
     OnViolationAction,
@@ -34,20 +36,11 @@ def resolve_and_get_path(model_name: str) -> str:
 
 
 class MetaReferenceSafetyImpl(Safety):
-    def __init__(self, config: SafetyConfig) -> None:
+    def __init__(self, config: SafetyConfig, deps) -> None:
         self.config = config
+        self.inference_api = deps[Api.inference]
 
     async def initialize(self) -> None:
-        shield_cfg = self.config.llama_guard_shield
-        if shield_cfg is not None:
-            model_dir = resolve_and_get_path(shield_cfg.model)
-            _ = LlamaGuardShield.instance(
-                model_dir=model_dir,
-                excluded_categories=shield_cfg.excluded_categories,
-                disable_input_check=shield_cfg.disable_input_check,
-                disable_output_check=shield_cfg.disable_output_check,
-            )
-
         shield_cfg = self.config.prompt_guard_shield
         if shield_cfg is not None:
             model_dir = resolve_and_get_path(shield_cfg.model)
@@ -91,11 +84,18 @@ class MetaReferenceSafetyImpl(Safety):
     def get_shield_impl(self, typ: MetaReferenceShieldType) -> ShieldBase:
         cfg = self.config
         if typ == MetaReferenceShieldType.llama_guard:
+            cfg = cfg.llama_guard_shield
             assert (
-                cfg.llama_guard_shield is not None
+                cfg is not None
             ), "Cannot use LlamaGuardShield since not present in config"
-            model_dir = resolve_and_get_path(cfg.llama_guard_shield.model)
-            return LlamaGuardShield.instance(model_dir=model_dir)
+
+            return LlamaGuardShield(
+                model=cfg.model,
+                inference_api=self.inference_api,
+                excluded_categories=cfg.excluded_categories,
+                disable_input_check=cfg.disable_input_check,
+                disable_output_check=cfg.disable_output_check,
+            )
         elif typ == MetaReferenceShieldType.jailbreak_shield:
             assert (
                 cfg.prompt_guard_shield is not None

llama_stack/providers/impls/meta_reference/safety/shields/llama_guard.py

@@ -9,9 +9,8 @@ import re
 from string import Template
 from typing import List, Optional
 
-import torch
 from llama_models.llama3.api.datatypes import Message, Role
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from llama_stack.apis.inference import *  # noqa: F403
 
 from .base import CANNED_RESPONSE_TEXT, OnViolationAction, ShieldBase, ShieldResponse
 
@@ -100,39 +99,17 @@ PROMPT_TEMPLATE = Template(
 
 
 class LlamaGuardShield(ShieldBase):
-    @staticmethod
-    def instance(
-        on_violation_action=OnViolationAction.RAISE,
-        model_dir: str = None,
-        excluded_categories: List[str] = None,
-        disable_input_check: bool = False,
-        disable_output_check: bool = False,
-    ) -> "LlamaGuardShield":
-        global _INSTANCE
-        if _INSTANCE is None:
-            _INSTANCE = LlamaGuardShield(
-                on_violation_action,
-                model_dir,
-                excluded_categories,
-                disable_input_check,
-                disable_output_check,
-            )
-        return _INSTANCE
-
     def __init__(
         self,
-        on_violation_action: OnViolationAction = OnViolationAction.RAISE,
-        model_dir: str = None,
+        model: str,
+        inference_api: Inference,
         excluded_categories: List[str] = None,
         disable_input_check: bool = False,
         disable_output_check: bool = False,
+        on_violation_action: OnViolationAction = OnViolationAction.RAISE,
     ):
         super().__init__(on_violation_action)
 
-        dtype = torch.bfloat16
-
-        assert model_dir is not None, "Llama Guard model_dir is None"
-
         if excluded_categories is None:
             excluded_categories = []
 
@@ -140,18 +117,12 @@ class LlamaGuardShield(ShieldBase):
             x in SAFETY_CATEGORIES_TO_CODE_MAP.values() for x in excluded_categories
         ), "Invalid categories in excluded categories. Expected format is ['S1', 'S2', ..]"
 
-        self.device = "cuda"
+        self.model = model
+        self.inference_api = inference_api
         self.excluded_categories = excluded_categories
         self.disable_input_check = disable_input_check
         self.disable_output_check = disable_output_check
 
-        # load model
-        torch_dtype = torch.bfloat16
-        self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
-        self.model = AutoModelForCausalLM.from_pretrained(
-            model_dir, torch_dtype=torch_dtype, device_map=self.device
-        )
-
     def check_unsafe_response(self, response: str) -> Optional[str]:
         match = re.match(r"^unsafe\n(.*)$", response)
         if match:
@@ -212,26 +183,21 @@ class LlamaGuardShield(ShieldBase):
             )
         else:
             prompt = self.build_prompt(messages)
-            llama_guard_input = {
-                "role": "user",
-                "content": prompt,
-            }
-            input_ids = self.tokenizer.apply_chat_template(
-                [llama_guard_input], return_tensors="pt", tokenize=True
-            ).to(self.device)
-            prompt_len = input_ids.shape[1]
-            output = self.model.generate(
-                input_ids=input_ids,
-                max_new_tokens=20,
-                output_scores=True,
-                return_dict_in_generate=True,
-                pad_token_id=0,
-            )
-            generated_tokens = output.sequences[:, prompt_len:]
 
-            response = self.tokenizer.decode(
-                generated_tokens[0], skip_special_tokens=True
-            )
-            response = response.strip()
-            shield_response = self.get_shield_response(response)
+            # TODO: llama-stack inference protocol has issues with non-streaming inference code
+            content = ""
+            async for chunk in self.inference_api.chat_completion(
+                model=self.model,
+                messages=[
+                    UserMessage(content=prompt),
+                ],
+                stream=True,
+            ):
+                event = chunk.event
+                if event.event_type == ChatCompletionResponseEventType.progress:
+                    assert isinstance(event.delta, str)
+                    content += event.delta
+
+            content = content.strip()
+            shield_response = self.get_shield_response(content)
             return shield_response