[AIC-py] hf machine translation (text to text) model parser (#753)

[AIC-py] hf mt model parser

text translation sans streaming. Streaming is not trivial here

Test:

```
import asyncio
from aiconfig_extension_hugging_face.local_inference.text_translation import HuggingFaceTextTranslationTransformer

from aiconfig import AIConfigRuntime, InferenceOptions, CallbackManager

# Load the aiconfig.

mp = HuggingFaceTextTranslationTransformer()

AIConfigRuntime.register_model_parser(mp, "translation_en_to_fr")

config = AIConfigRuntime.load("/Users/jonathan/Projects/aiconfig/test_hf_transl.aiconfig.json")
config.callback_manager = CallbackManager([])


def print_stream(data, _accumulated_data, _index):
    print(data, end="", flush=True)


async def run():
    # print("Stream")
    # options = InferenceOptions(stream=True, stream_callback=print_stream)
    # out = await config.run("test_hf_trans", options=options)
    # print("Output:\n", out)

    print("no stream")
    options = InferenceOptions(stream=False)
    out = await config.run("test_hf_trans", options=options)
    print("Output:\n", out)


asyncio.run(run())

```

---
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* __->__ #753
* #740

extensions/HuggingFace/python/src/aiconfig_extension_hugging_face/__init__.py

@@ -1,7 +1,16 @@
 from .local_inference.text_2_image import HuggingFaceText2ImageDiffusor
 from .local_inference.text_generation import HuggingFaceTextGenerationTransformer
 from .remote_inference_client.text_generation import HuggingFaceTextGenerationClient
+from .local_inference.text_summarization import HuggingFaceTextSummarizationTransformer
+from .local_inference.text_translation import HuggingFaceTextTranslationTransformer
 
-LOCAL_INFERENCE_CLASSES = ["HuggingFaceText2ImageDiffusor", "HuggingFaceTextGenerationTransformer"]
+# from .remote_inference_client.text_generation import HuggingFaceTextGenerationClient
+
+LOCAL_INFERENCE_CLASSES = [
+    "HuggingFaceText2ImageDiffusor",
+    "HuggingFaceTextGenerationTransformer",
+    "HuggingFaceTextSummarizationTransformer",
+    "HuggingFaceTextTranslationTransformer",
+]
 REMOTE_INFERENCE_CLASSES = ["HuggingFaceTextGenerationClient"]
 __ALL__ = LOCAL_INFERENCE_CLASSES + REMOTE_INFERENCE_CLASSES

extensions/HuggingFace/python/src/aiconfig_extension_hugging_face/local_inference/text_summarization.py

@@ -0,0 +1,305 @@
+import copy
+import json
+import threading
+from typing import TYPE_CHECKING, Any, Dict, List, Optional
+from transformers import (
+    AutoTokenizer,
+    Pipeline,
+    pipeline,
+    TextIteratorStreamer,
+)
+
+from aiconfig.default_parsers.parameterized_model_parser import ParameterizedModelParser
+from aiconfig.model_parser import InferenceOptions
+from aiconfig.schema import (
+    ExecuteResult,
+    Output,
+    OutputDataWithValue,
+    Prompt,
+    PromptMetadata,
+)
+from aiconfig.util.params import resolve_prompt
+
+# Circuluar Dependency Type Hints
+if TYPE_CHECKING:
+    from aiconfig.Config import AIConfigRuntime
+
+
+# Step 1: define Helpers
+def refine_chat_completion_params(model_settings: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Refines the completion params for the HF text summarization api. Removes any unsupported params.
+    The supported keys were found by looking at the HF text summarization api. `huggingface_hub.InferenceClient.text_summarization()`
+    """
+
+    supported_keys = {
+        "max_length",
+        "max_new_tokens",
+        "min_length",
+        "min_new_tokens",
+        "early_stopping",
+        "max_time",
+        "do_sample",
+        "num_beams",
+        "num_beam_groups",
+        "penalty_alpha",
+        "use_cache",
+        "temperature",
+        "top_k",
+        "top_p",
+        "typical_p",
+        "epsilon_cutoff",
+        "eta_cutoff",
+        "diversity_penalty",
+        "repetition_penalty",
+        "encoder_repetition_penalty",
+        "length_penalty",
+        "no_repeat_ngram_size",
+        "bad_words_ids",
+        "force_words_ids",
+        "renormalize_logits",
+        "constraints",
+        "forced_bos_token_id",
+        "forced_eos_token_id",
+        "remove_invalid_values",
+        "exponential_decay_length_penalty",
+        "suppress_tokens",
+        "begin_suppress_tokens",
+        "forced_decoder_ids",
+        "sequence_bias",
+        "guidance_scale",
+        "low_memory",
+        "num_return_sequences",
+        "output_attentions",
+        "output_hidden_states",
+        "output_scores",
+        "return_dict_in_generate",
+        "pad_token_id",
+        "bos_token_id",
+        "eos_token_id",
+        "encoder_no_repeat_ngram_size",
+        "decoder_start_token_id",
+        "num_assistant_tokens",
+        "num_assistant_tokens_schedule",
+    }
+
+    completion_data = {}
+    for key in model_settings:
+        if key.lower() in supported_keys:
+            completion_data[key.lower()] = model_settings[key]
+
+    return completion_data
+
+
+def construct_regular_output(result: Dict[str, str], execution_count: int) -> Output:
+    """
+    Construct regular output per response result, without streaming enabled
+    """
+    output = ExecuteResult(
+        **{
+            "output_type": "execute_result",
+            "data": result["summary_text"],
+            "execution_count": execution_count,
+            "metadata": {},
+        }
+    )
+    return output
+
+
+def construct_stream_output(
+    streamer: TextIteratorStreamer,
+    options: InferenceOptions,
+) -> Output:
+    """
+    Constructs the output for a stream response.
+
+    Args:
+        streamer (TextIteratorStreamer): Streams the output. See:
+            https://huggingface.co/docs/transformers/v4.35.2/en/internal/summarization_utils#transformers.TextIteratorStreamer
+        options (InferenceOptions): The inference options. Used to determine
+            the stream callback.
+
+    """
+    output = ExecuteResult(
+        **{
+            "output_type": "execute_result",
+            "data": "",  # We update this below
+            "execution_count": 0,  # Multiple outputs are not supported for streaming
+            "metadata": {},
+        }
+    )
+    accumulated_message = ""
+    for new_text in streamer:
+        if isinstance(new_text, str):
+            accumulated_message += new_text
+            options.stream_callback(new_text, accumulated_message, 0)
+
+            output.data = accumulated_message
+    return output
+
+
+class HuggingFaceTextSummarizationTransformer(ParameterizedModelParser):
+    """
+    A model parser for HuggingFace models of type text summarization task using transformers.
+    """
+
+    def __init__(self):
+        """
+        Returns:
+            HuggingFaceTextSummarizationTransformer
+
+        Usage:
+        1. Create a new model parser object with the model ID of the model to use.
+                parser = HuggingFaceTextSummarizationTransformer()
+        2. Add the model parser to the registry.
+                config.register_model_parser(parser)
+        """
+        super().__init__()
+        self.summarizers: dict[str, Pipeline] = {}
+
+    def id(self) -> str:
+        """
+        Returns an identifier for the Model Parser
+        """
+        return "HuggingFaceTextSummarizationTransformer"
+
+    async def serialize(
+        self,
+        prompt_name: str,
+        data: Any,
+        ai_config: "AIConfigRuntime",
+        parameters: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ) -> List[Prompt]:
+        """
+        Defines how a prompt and model inference settings get serialized in the .aiconfig.
+
+        Args:
+            prompt_name (str): The prompt to be serialized.
+            inference_settings (dict): Model-specific inference settings to be serialized.
+
+        Returns:
+            List[Prompt]: Serialized representation of the prompt and inference settings.
+        """
+        data = copy.deepcopy(data)
+
+        # assume data is completion params for HF text summarization
+        prompt_input = data["prompt"]
+
+        # Prompt is handled, remove from data
+        data.pop("prompt", None)
+
+        model_metadata = ai_config.get_model_metadata(data, self.id())
+        prompt = Prompt(
+            name=prompt_name,
+            input=prompt_input,
+            metadata=PromptMetadata(model=model_metadata, parameters=parameters, **kwargs),
+        )
+        return [prompt]
+
+    async def deserialize(
+        self,
+        prompt: Prompt,
+        aiconfig: "AIConfigRuntime",
+        _options,
+        params: Optional[Dict[str, Any]] = {},
+    ) -> Dict[str, Any]:
+        """
+        Defines how to parse a prompt in the .aiconfig for a particular model
+        and constructs the completion params for that model.
+
+        Args:
+            serialized_data (str): Serialized data from the .aiconfig.
+
+        Returns:
+            dict: Model-specific completion parameters.
+        """
+        # Build Completion data
+        model_settings = self.get_model_settings(prompt, aiconfig)
+        completion_data = refine_chat_completion_params(model_settings)
+
+        # Add resolved prompt
+        resolved_prompt = resolve_prompt(prompt, params, aiconfig)
+        completion_data["prompt"] = resolved_prompt
+        return completion_data
+
+    async def run_inference(self, prompt: Prompt, aiconfig: "AIConfigRuntime", options: InferenceOptions, parameters: Dict[str, Any]) -> List[Output]:
+        """
+        Invoked to run a prompt in the .aiconfig. This method should perform
+        the actual model inference based on the provided prompt and inference settings.
+
+        Args:
+            prompt (str): The input prompt.
+            inference_settings (dict): Model-specific inference settings.
+
+        Returns:
+            InferenceResponse: The response from the model.
+        """
+        completion_data = await self.deserialize(prompt, aiconfig, options, parameters)
+        inputs = completion_data.pop("prompt", None)
+
+        model_name: str = aiconfig.get_model_name(prompt)
+        if isinstance(model_name, str) and model_name not in self.summarizers:
+            self.summarizers[model_name] = pipeline("summarization", model=model_name)
+        summarizer = self.summarizers[model_name]
+
+        # if stream enabled in runtime options and config, then stream. Otherwise don't stream.
+        streamer = None
+        should_stream = (options.stream if options else False) and (not "stream" in completion_data or completion_data.get("stream") != False)
+        if should_stream:
+            tokenizer: AutoTokenizer = AutoTokenizer.from_pretrained(model_name)
+            streamer = TextIteratorStreamer(tokenizer)
+            completion_data["streamer"] = streamer
+
+        outputs: List[Output] = []
+        output = None
+
+        def _summarize():
+            return summarizer(inputs, **completion_data)
+
+        if not should_stream:
+            response: List[Any] = _summarize()
+            for count, result in enumerate(response):
+                output = construct_regular_output(result, count)
+                outputs.append(output)
+        else:
+            if completion_data.get("num_return_sequences", 1) > 1:
+                raise ValueError("Sorry, TextIteratorStreamer does not support multiple return sequences, please set `num_return_sequences` to 1")
+            if not streamer:
+                raise ValueError("Stream option is selected but streamer is not initialized")
+
+            # For streaming, cannot call `summarizer` directly otherwise response will be blocking
+            thread = threading.Thread(target=_summarize)
+            thread.start()
+            output = construct_stream_output(streamer, options)
+            if output is not None:
+                outputs.append(output)
+
+        prompt.outputs = outputs
+        return prompt.outputs
+
+    def get_output_text(
+        self,
+        prompt: Prompt,
+        aiconfig: "AIConfigRuntime",
+        output: Optional[Output] = None,
+    ) -> str:
+        if output is None:
+            output = aiconfig.get_latest_output(prompt)
+
+        if output is None:
+            return ""
+
+        # TODO (rossdanlm): Handle multiple outputs in list
+        # https://github.com/lastmile-ai/aiconfig/issues/467
+        if output.output_type == "execute_result":
+            output_data = output.data
+            if isinstance(output_data, str):
+                return output_data
+            if isinstance(output_data, OutputDataWithValue):
+                if isinstance(output_data.value, str):
+                    return output_data.value
+                # HuggingFace Text summarization does not support function
+                # calls so shouldn't get here, but just being safe
+                return json.dumps(output_data.value, indent=2)
+        return ""