two version of R2R are here HEAD master

1 files changed, 149 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/petals/completion/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/petals/completion/handler.py
new file mode 100644
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+++ b/.venv/lib/python3.12/site-packages/litellm/llms/petals/completion/handler.py
@@ -0,0 +1,149 @@
+import time
+from typing import Callable, Optional, Union
+
+import litellm
+from litellm.litellm_core_utils.prompt_templates.factory import (
+    custom_prompt,
+    prompt_factory,
+)
+from litellm.llms.custom_httpx.http_handler import (
+    AsyncHTTPHandler,
+    HTTPHandler,
+    _get_httpx_client,
+)
+from litellm.utils import ModelResponse, Usage
+
+from ..common_utils import PetalsError
+
+
+def completion(
+    model: str,
+    messages: list,
+    api_base: Optional[str],
+    model_response: ModelResponse,
+    print_verbose: Callable,
+    encoding,
+    logging_obj,
+    optional_params: dict,
+    stream=False,
+    litellm_params=None,
+    logger_fn=None,
+    client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
+):
+    ## Load Config
+    config = litellm.PetalsConfig.get_config()
+    for k, v in config.items():
+        if (
+            k not in optional_params
+        ):  # completion(top_k=3) > petals_config(top_k=3) <- allows for dynamic variables to be passed in
+            optional_params[k] = v
+
+    if model in litellm.custom_prompt_dict:
+        # check if the model has a registered custom prompt
+        model_prompt_details = litellm.custom_prompt_dict[model]
+        prompt = custom_prompt(
+            role_dict=model_prompt_details["roles"],
+            initial_prompt_value=model_prompt_details["initial_prompt_value"],
+            final_prompt_value=model_prompt_details["final_prompt_value"],
+            messages=messages,
+        )
+    else:
+        prompt = prompt_factory(model=model, messages=messages)
+
+    output_text: Optional[str] = None
+    if api_base:
+        ## LOGGING
+        logging_obj.pre_call(
+            input=prompt,
+            api_key="",
+            additional_args={
+                "complete_input_dict": optional_params,
+                "api_base": api_base,
+            },
+        )
+        data = {"model": model, "inputs": prompt, **optional_params}
+
+        ## COMPLETION CALL
+        if client is None or not isinstance(client, HTTPHandler):
+            client = _get_httpx_client()
+        response = client.post(api_base, data=data)
+
+        ## LOGGING
+        logging_obj.post_call(
+            input=prompt,
+            api_key="",
+            original_response=response.text,
+            additional_args={"complete_input_dict": optional_params},
+        )
+
+        ## RESPONSE OBJECT
+        try:
+            output_text = response.json()["outputs"]
+        except Exception as e:
+            PetalsError(
+                status_code=response.status_code,
+                message=str(e),
+                headers=response.headers,
+            )
+
+    else:
+        try:
+            from petals import AutoDistributedModelForCausalLM  # type: ignore
+            from transformers import AutoTokenizer
+        except Exception:
+            raise Exception(
+                "Importing torch, transformers, petals failed\nTry pip installing petals \npip install git+https://github.com/bigscience-workshop/petals"
+            )
+
+        model = model
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model, use_fast=False, add_bos_token=False
+        )
+        model_obj = AutoDistributedModelForCausalLM.from_pretrained(model)
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input=prompt,
+            api_key="",
+            additional_args={"complete_input_dict": optional_params},
+        )
+
+        ## COMPLETION CALL
+        inputs = tokenizer(prompt, return_tensors="pt")["input_ids"]
+
+        # optional params: max_new_tokens=1,temperature=0.9, top_p=0.6
+        outputs = model_obj.generate(inputs, **optional_params)
+
+        ## LOGGING
+        logging_obj.post_call(
+            input=prompt,
+            api_key="",
+            original_response=outputs,
+            additional_args={"complete_input_dict": optional_params},
+        )
+        ## RESPONSE OBJECT
+        output_text = tokenizer.decode(outputs[0])
+
+    if output_text is not None and len(output_text) > 0:
+        model_response.choices[0].message.content = output_text  # type: ignore
+
+    prompt_tokens = len(encoding.encode(prompt))
+    completion_tokens = len(
+        encoding.encode(model_response["choices"][0]["message"].get("content"))
+    )
+
+    model_response.created = int(time.time())
+    model_response.model = model
+    usage = Usage(
+        prompt_tokens=prompt_tokens,
+        completion_tokens=completion_tokens,
+        total_tokens=prompt_tokens + completion_tokens,
+    )
+    setattr(model_response, "usage", usage)
+    return model_response
+
+
+def embedding():
+    # logic for parsing in - calling - parsing out model embedding calls
+    pass