Make all methods async def again; add completion() for meta-reference (#270)

PR #201 had made several changes while trying to fix issues with getting the stream=False branches of inference and agents API working. As part of this, it made a change which was slightly gratuitous. Namely, making chat_completion() and brethren "def" instead of "async def".

The rationale was that this allowed the user (within llama-stack) of this to use it as:

```
async for chunk in api.chat_completion(params)
```

However, it causes unnecessary confusion for several folks. Given that clients (e.g., llama-stack-apps) anyway use the SDK methods (which are completely isolated) this choice was not ideal. Let's revert back so the call now looks like:

```
async for chunk in await api.chat_completion(params)
```

Bonus: Added a completion() implementation for the meta-reference provider. Technically should have been another PR :)

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

@@ -23,11 +23,6 @@ from fairscale.nn.model_parallel.initialize import (
 )
 from llama_models.llama3.api.args import ModelArgs
 from llama_models.llama3.api.chat_format import ChatFormat, ModelInput
-from llama_models.llama3.api.datatypes import (
-    InterleavedTextMedia,
-    Message,
-    ToolPromptFormat,
-)
 from llama_models.llama3.api.tokenizer import Tokenizer
 from llama_models.llama3.reference_impl.model import Transformer
 from llama_models.llama3.reference_impl.multimodal.model import (
@@ -38,7 +33,11 @@ from llama_models.sku_list import resolve_model
 from pydantic import BaseModel
 from termcolor import cprint
 
+from llama_stack.apis.inference import *  # noqa: F403
 from llama_stack.distribution.utils.model_utils import model_local_dir
+from llama_stack.providers.utils.inference.prompt_adapter import (
+    chat_completion_request_to_messages,
+)
 
 from .config import MetaReferenceInferenceConfig, MetaReferenceQuantizedInferenceConfig
 
@@ -297,15 +296,12 @@ class Llama:
             if all(eos_reached):
                 break
 
-    def text_completion(
+    def completion(
         self,
-        content: InterleavedTextMedia,
-        temperature: float = 0.6,
-        top_p: float = 0.9,
-        max_gen_len: Optional[int] = None,
-        logprobs: bool = False,
-        echo: bool = False,
+        request: CompletionRequest,
     ) -> Generator:
+        sampling_params = request.sampling_params
+        max_gen_len = sampling_params.max_tokens
         if (
             max_gen_len is None
             or max_gen_len == 0
@@ -313,26 +309,25 @@ class Llama:
         ):
             max_gen_len = self.model.params.max_seq_len - 1
 
-        model_input = self.formatter.encode_content(content)
-
+        model_input = self.formatter.encode_content(request.content)
         yield from self.generate(
             model_input=model_input,
             max_gen_len=max_gen_len,
-            temperature=temperature,
-            top_p=top_p,
-            logprobs=logprobs,
-            echo=echo,
+            temperature=sampling_params.temperature,
+            top_p=sampling_params.top_p,
+            logprobs=bool(request.logprobs),
+            include_stop_token=True,
+            echo=False,
         )
 
     def chat_completion(
         self,
-        messages: List[Message],
-        temperature: float = 0.6,
-        top_p: float = 0.9,
-        max_gen_len: Optional[int] = None,
-        logprobs: bool = False,
-        tool_prompt_format: ToolPromptFormat = ToolPromptFormat.json,
+        request: ChatCompletionRequest,
     ) -> Generator:
+        messages = chat_completion_request_to_messages(request)
+
+        sampling_params = request.sampling_params
+        max_gen_len = sampling_params.max_tokens
         if (
             max_gen_len is None
             or max_gen_len == 0
@@ -343,12 +338,12 @@ class Llama:
         yield from self.generate(
             model_input=self.formatter.encode_dialog_prompt(
                 messages,
-                tool_prompt_format,
+                request.tool_prompt_format,
             ),
             max_gen_len=max_gen_len,
-            temperature=temperature,
-            top_p=top_p,
-            logprobs=logprobs,
+            temperature=sampling_params.temperature,
+            top_p=sampling_params.top_p,
+            logprobs=bool(request.logprobs),
             include_stop_token=True,
         )