Use inference APIs for executing Llama Guard (#121)

We should use Inference APIs to execute Llama Guard instead of directly needing to use HuggingFace modeling related code. The actual inference consideration is handled by Inference.

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

@@ -7,12 +7,13 @@
 from typing import Optional
 
 from llama_models.datatypes import *  # noqa: F403
-from llama_models.sku_list import all_registered_models, resolve_model
+from llama_models.sku_list import resolve_model
 
 from llama_stack.apis.inference import *  # noqa: F401, F403
-
 from pydantic import BaseModel, Field, field_validator
 
+from llama_stack.providers.utils.inference import supported_inference_models
+
 
 class MetaReferenceImplConfig(BaseModel):
     model: str = Field(
@@ -27,12 +28,7 @@ class MetaReferenceImplConfig(BaseModel):
     @field_validator("model")
     @classmethod
     def validate_model(cls, model: str) -> str:
-        permitted_models = [
-            m.descriptor()
-            for m in all_registered_models()
-            if m.model_family in {ModelFamily.llama3_1, ModelFamily.llama3_2}
-            or m.core_model_id == CoreModelId.llama_guard_3_8b
-        ]
+        permitted_models = supported_inference_models()
         if model not in permitted_models:
             model_list = "\n\t".join(permitted_models)
             raise ValueError(

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

@@ -52,7 +52,7 @@ def model_checkpoint_dir(model) -> str:
         checkpoint_dir = checkpoint_dir / "original"
 
     assert checkpoint_dir.exists(), (
-        f"Could not find checkpoint dir: {checkpoint_dir}."
+        f"Could not find checkpoints in: {model_local_dir(model.descriptor())}. "
         f"Please download model using `llama download --model-id {model.descriptor()}`"
     )
     return str(checkpoint_dir)

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

@@ -88,10 +88,10 @@ class MetaReferenceSafetyImpl(Safety):
             assert (
                 cfg is not None
             ), "Cannot use LlamaGuardShield since not present in config"
-            model_dir = resolve_and_get_path(cfg.model)
 
             return LlamaGuardShield(
-                model_dir=model_dir,
+                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,

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

@@ -9,17 +9,10 @@ import re
 from string import Template
 from typing import List, Optional
 
-import torch
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    MllamaForConditionalGeneration,
-    MllamaProcessor
-)
-
+from llama_models.llama3.api.datatypes import *  # noqa: F403
+from llama_stack.apis.inference import *  # noqa: F403
 
 from .base import CANNED_RESPONSE_TEXT, OnViolationAction, ShieldBase, ShieldResponse
-from llama_models.llama3.api.datatypes import *  # noqa: F403
 
 
 SAFE_RESPONSE = "safe"
@@ -75,20 +68,17 @@ DEFAULT_LG_V3_SAFETY_CATEGORIES = [
     CAT_ELECTIONS,
 ]
 
-# model names
-LG_3_8B = "Llama-Guard-3-8B"
-LG_3_1B = "Llama-Guard-3-1B"
-LG_3_11B_VISION = "Llama-Guard-3-11B-Vision"
-
 
 MODEL_TO_SAFETY_CATEGORIES_MAP = {
-    LG_3_8B: DEFAULT_LG_V3_SAFETY_CATEGORIES + [CAT_CODE_INTERPRETER_ABUSE],
-    LG_3_1B: DEFAULT_LG_V3_SAFETY_CATEGORIES,
-    LG_3_11B_VISION: DEFAULT_LG_V3_SAFETY_CATEGORIES,
+    CoreModelId.llama_guard_3_8b.value: (
+        DEFAULT_LG_V3_SAFETY_CATEGORIES + [CAT_CODE_INTERPRETER_ABUSE]
+    ),
+    CoreModelId.llama_guard_3_1b.value: DEFAULT_LG_V3_SAFETY_CATEGORIES,
+    CoreModelId.llama_guard_3_11b_vision.value: DEFAULT_LG_V3_SAFETY_CATEGORIES,
 }
 
 
-PROMPT_TASK = "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nTask: Check if there is unsafe content in '$agent_type' messages in conversations according our safety policy with the below categories."
+PROMPT_TASK = "Task: Check if there is unsafe content in '$agent_type' messages in conversations according our safety policy with the below categories."
 
 SAFETY_CATEGORIES = """
 
@@ -109,7 +99,7 @@ $conversations
 PROMPT_INSTRUCTIONS = """
 Provide your safety assessment for ONLY THE LAST $agent_type message in the above conversation:
  - First line must read 'safe' or 'unsafe'.
- - If unsafe, a second line must include a comma-separated list of violated categories. <|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
+ - If unsafe, a second line must include a comma-separated list of violated categories."""
 
 
 PROMPT_TEMPLATE = Template(
@@ -120,7 +110,8 @@ PROMPT_TEMPLATE = Template(
 class LlamaGuardShield(ShieldBase):
     def __init__(
         self,
-        model_dir: str,
+        model: str,
+        inference_api: Inference,
         excluded_categories: List[str] = None,
         disable_input_check: bool = False,
         disable_output_check: bool = False,
@@ -128,12 +119,6 @@ class LlamaGuardShield(ShieldBase):
     ):
         super().__init__(on_violation_action)
 
-        dtype = torch.bfloat16
-        self.model_dir = model_dir
-        self.device = "cuda"
-
-        assert self.model_dir is not None, "Llama Guard model_dir is None"
-
         if excluded_categories is None:
             excluded_categories = []
 
@@ -141,27 +126,15 @@ 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', ..]"
 
+        if model not in MODEL_TO_SAFETY_CATEGORIES_MAP:
+            raise ValueError(f"Unsupported model: {model}")
+
+        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
 
-        torch_dtype = torch.bfloat16
-
-        self.model_dir = f"meta-llama/{self.get_model_name()}"
-
-        if self.is_lg_vision():
-
-            self.model = MllamaForConditionalGeneration.from_pretrained(
-                self.model_dir, device_map=self.device, torch_dtype=torch_dtype
-            )
-            self.processor = MllamaProcessor.from_pretrained(self.model_dir)
-        else:
-
-            self.tokenizer = AutoTokenizer.from_pretrained(self.model_dir)
-            self.model = AutoModelForCausalLM.from_pretrained(
-                self.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:
@@ -177,7 +150,8 @@ class LlamaGuardShield(ShieldBase):
             excluded_categories = []
 
         final_categories = []
-        all_categories = MODEL_TO_SAFETY_CATEGORIES_MAP[self.get_model_name()]
+
+        all_categories = MODEL_TO_SAFETY_CATEGORIES_MAP[self.model]
         for cat in all_categories:
             cat_code = SAFETY_CATEGORIES_TO_CODE_MAP[cat]
             if cat_code in excluded_categories:
@@ -186,11 +160,99 @@ class LlamaGuardShield(ShieldBase):
 
         return final_categories
 
+    def validate_messages(self, messages: List[Message]) -> None:
+        if len(messages) == 0:
+            raise ValueError("Messages must not be empty")
+        if messages[0].role != Role.user.value:
+            raise ValueError("Messages must start with user")
+
+        if len(messages) >= 2 and (
+            messages[0].role == Role.user.value and messages[1].role == Role.user.value
+        ):
+            messages = messages[1:]
+
+        for i in range(1, len(messages)):
+            if messages[i].role == messages[i - 1].role:
+                raise ValueError(
+                    f"Messages must alternate between user and assistant. Message {i} has the same role as message {i-1}"
+                )
+        return messages
+
+    async def run(self, messages: List[Message]) -> ShieldResponse:
+        messages = self.validate_messages(messages)
+        if self.disable_input_check and messages[-1].role == Role.user.value:
+            return ShieldResponse(is_violation=False)
+        elif self.disable_output_check and messages[-1].role == Role.assistant.value:
+            return ShieldResponse(
+                is_violation=False,
+            )
+
+        if self.model == CoreModelId.llama_guard_3_11b_vision.value:
+            shield_input_message = self.build_vision_shield_input(messages)
+        else:
+            shield_input_message = self.build_text_shield_input(messages)
+
+        # 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=[shield_input_message],
+            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
+
+    def build_text_shield_input(self, messages: List[Message]) -> UserMessage:
+        return UserMessage(content=self.build_prompt(messages))
+
+    def build_vision_shield_input(self, messages: List[Message]) -> UserMessage:
+        conversation = []
+        most_recent_img = None
+
+        for m in messages[::-1]:
+            if isinstance(m.content, str):
+                conversation.append(m)
+            elif isinstance(m.content, ImageMedia):
+                if most_recent_img is None and m.role == Role.user.value:
+                    most_recent_img = m.content
+                    conversation.append(m)
+            elif isinstance(m.content, list):
+                content = []
+                for c in m.content:
+                    if isinstance(c, str):
+                        content.append(c)
+                    elif isinstance(c, ImageMedia):
+                        if most_recent_img is None and m.role == Role.user.value:
+                            most_recent_img = c
+                            content.append(c)
+                    else:
+                        raise ValueError(f"Unknown content type: {c}")
+
+                conversation.append(UserMessage(content=content))
+            else:
+                raise ValueError(f"Unknown content type: {m.content}")
+
+        prompt = []
+        if most_recent_img is not None:
+            prompt.append(most_recent_img)
+        prompt.append(self.build_prompt(conversation[::-1]))
+
+        return UserMessage(content=prompt)
+
     def build_prompt(self, messages: List[Message]) -> str:
         categories = self.get_safety_categories()
         categories_str = "\n".join(categories)
         conversations_str = "\n\n".join(
-            [f"{m.role.capitalize()}: {m.content}" for m in messages]
+            [
+                f"{m.role.capitalize()}: {interleaved_text_media_as_str(m.content)}"
+                for m in messages
+            ]
         )
         return PROMPT_TEMPLATE.substitute(
             agent_type=messages[-1].role.capitalize(),
@@ -214,134 +276,3 @@ class LlamaGuardShield(ShieldBase):
             )
 
         raise ValueError(f"Unexpected response: {response}")
-
-    def build_mm_prompt(self, messages: List[Message]) -> str:
-        conversation = []
-        most_recent_img = None
-
-        for m in messages[::-1]:
-            if isinstance(m.content, str):
-                conversation.append(
-                    {
-                        "role": m.role,
-                        "content": [{"type": "text", "text": m.content}],
-                    }
-                )
-            elif isinstance(m.content, ImageMedia):
-                if most_recent_img is None and m.role == Role.user.value:
-                    most_recent_img = m.content
-                    conversation.append(
-                        {
-                            "role": m.role,
-                            "content": [{"type": "image"}],
-                        }
-                    )
-
-            elif isinstance(m.content, list):
-                content = []
-                for c in m.content:
-                    if isinstance(c, str):
-                        content.append({"type": "text", "text": c})
-                    elif isinstance(c, ImageMedia):
-                        if most_recent_img is None and m.role == Role.user.value:
-                            most_recent_img = c
-                            content.append({"type": "image"})
-                    else:
-                        raise ValueError(f"Unknown content type: {c}")
-
-                conversation.append(
-                    {
-                        "role": m.role,
-                        "content": content,
-                    }
-                )
-            else:
-                raise ValueError(f"Unknown content type: {m.content}")
-
-        return conversation[::-1], most_recent_img
-
-    async def run_lg_mm(self, messages: List[Message]) -> ShieldResponse:
-        formatted_messages, most_recent_img = self.build_mm_prompt(messages)
-        raw_image = None
-        if most_recent_img:
-            raw_image = interleaved_text_media_localize(most_recent_img)
-            raw_image = raw_image.image
-        llama_guard_input_templ_applied = self.processor.apply_chat_template(
-            formatted_messages,
-            add_generation_prompt=True,
-            tokenize=False,
-            skip_special_tokens=False,
-        )
-        inputs = self.processor(
-            text=llama_guard_input_templ_applied, images=raw_image, return_tensors="pt"
-        ).to(self.device)
-        output = self.model.generate(**inputs, do_sample=False, max_new_tokens=50)
-        response = self.processor.decode(
-            output[0][len(inputs["input_ids"][0]) :], skip_special_tokens=True
-        )
-        shield_response = self.get_shield_response(response)
-        return shield_response
-
-    async def run_lg_text(self, messages: List[Message]):
-        prompt = self.build_prompt(messages)
-        input_ids = self.tokenizer.encode(prompt, return_tensors="pt").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)
-
-        shield_response = self.get_shield_response(response)
-        return shield_response
-
-    def get_model_name(self):
-        return self.model_dir.split("/")[-1]
-
-    def is_lg_vision(self):
-        model_name = self.get_model_name()
-        return model_name == LG_3_11B_VISION
-
-    def validate_messages(self, messages: List[Message]) -> None:
-        if len(messages) == 0:
-            raise ValueError("Messages must not be empty")
-        if messages[0].role != Role.user.value:
-            raise ValueError("Messages must start with user")
-
-        if len(messages) >= 2 and (
-            messages[0].role == Role.user.value and messages[1].role == Role.user.value
-        ):
-            messages = messages[1:]
-
-        for i in range(1, len(messages)):
-            if messages[i].role == messages[i - 1].role:
-                raise ValueError(
-                    f"Messages must alternate between user and assistant. Message {i} has the same role as message {i-1}"
-                )
-        return messages
-
-    async def run(self, messages: List[Message]) -> ShieldResponse:
-
-        messages = self.validate_messages(messages)
-        if self.disable_input_check and messages[-1].role == Role.user.value:
-            return ShieldResponse(is_violation=False)
-        elif self.disable_output_check and messages[-1].role == Role.assistant.value:
-            return ShieldResponse(
-                is_violation=False,
-            )
-        else:
-
-            if self.is_lg_vision():
-
-                shield_response = await self.run_lg_mm(messages)
-
-            else:
-
-                shield_response = await self.run_lg_text(messages)
-
-        return shield_response