Add ModelGenerator for LLama2, driver proxy and Vicuna

databricks/labs/doc_qa/evaluators/templated_evaluator.py

@@ -15,7 +15,7 @@
 
 
 logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+logger = logging.getLogger(__name__.split(".")[0])
 
 
 class ParameterType(Enum):

databricks/labs/doc_qa/llm_providers/openai_provider.py

@@ -10,7 +10,6 @@
 
 
 openai_token = os.getenv('OPENAI_API_KEY')
-openai_org = os.getenv('OPENAI_ORGANIZATION')
 
 class StatusCode429Error(Exception):
     pass
@@ -24,7 +23,6 @@ def request_openai(messages, functions=[], temperature=0.0, model="gpt-4"):
   headers = {
       "Content-Type": "application/json",
       "Authorization": f"Bearer {openai_token}",
-      "OpenAI-Organization": openai_org,
   }
   data = {
       "model": model,

databricks/labs/doc_qa/model_generators/model_generator.py

@@ -5,8 +5,8 @@
 import concurrent.futures
 
 logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
+# Instead of using full name, only use the module name
+logger = logging.getLogger(__name__.split(".")[0])
 
 class RowGenerateResult:
     """
@@ -78,6 +78,7 @@ def __init__(
         self._prompt_formatter = prompt_formatter
         self._batch_size = batch_size
         self._concurrency = concurrency
+        self.input_variables = prompt_formatter.variables
 
     def _generate(
         self, prompts: list, temperature: float, max_tokens=256, system_prompt=None
@@ -95,7 +96,7 @@ def run_tasks(
         Returns:
             EvalResult: the evaluation result
         """
-        prompt_batches = []
+        task_batches = []
         # First, traverse the input dataframe using batch size
         for i in range(0, len(input_df), self._batch_size):
             # Get the current batch
@@ -107,9 +108,13 @@ def run_tasks(
                 # Format the input dataframe into prompts
                 prompt = self._prompt_formatter.format(**row)
                 prompts.append(prompt)
-            prompt_batches.append(prompts)
+            task = {
+                "prompts": prompts,
+                "df": batch_df,
+            }
+            task_batches.append(task)
         logger.info(
-            f"Generated total number of batches for prompts: {len(prompt_batches)}"
+            f"Generated total number of batches for prompts: {len(task_batches)}"
         )
 
         # Call the _generate in parallel using multiple threads, each call with a batch of prompts
@@ -118,15 +123,28 @@ def run_tasks(
         ) as executor:
             future_to_batch = {
                 executor.submit(
-                    self._generate, prompts, temperature, max_tokens, system_prompt
-                ): prompts
-                for prompts in prompt_batches
+                    self._generate,
+                    task["prompts"],
+                    temperature,
+                    max_tokens,
+                    system_prompt,
+                ): task
+                for task in task_batches
             }
             batch_generate_results = []
             for future in concurrent.futures.as_completed(future_to_batch):
-                prompts = future_to_batch[future]
+                task = future_to_batch[future]
                 try:
                     result = future.result()
+                    batch_df = task["df"]
+                    # Add the columns from batch_df where the column name is in the input_variables, add as attribute and value to the RowEvalResult
+                    for index, row in enumerate(result.rows):
+                        for input_variable in self.input_variables:
+                            setattr(
+                                row,
+                                input_variable,
+                                batch_df[input_variable].iloc[index],
+                            )
                     batch_generate_results.append(result)
                 except Exception as exc:
                     logger.error(f"Exception occurred when running the task: {exc}")
@@ -268,7 +286,7 @@ def __init__(
         self._tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
 
     def _format_prompt(self, message: str, system_prompt_opt: str) -> str:
-        if system_prompt_opt is None:
+        if system_prompt_opt is not None:
             texts = [f"[INST] <<SYS>>\n{system_prompt_opt}\n<</SYS>>\n\n"]
             texts.append(f"{message.strip()} [/INST]")
             return "".join(texts)
@@ -323,3 +341,187 @@ def _generate(
             is_successful=True,
             error_msg=None,
         )
+
+
+class VicunaModelGenerator(BaseModelGenerator):
+    def __init__(
+        self,
+        prompt_formatter: PromptTemplate,
+        model_name_or_path: str,
+        batch_size: int = 1,
+        concurrency: int = 1,
+    ) -> None:
+        """
+        Args:
+            prompt_formatter (PromptTemplate): the prompt format to format the input dataframe into prompts row by row according to the column names
+            model_name (str): the model name
+            batch_size (int, optional): Batch size that will be used to run tasks. Defaults to 1, which means it's sequential.
+
+        Recommendations:
+            - for A100 80GB, use batch_size 1 for vicuna-33b
+            - for A100 80GB x 2, use batch_size 64 for vicuna-33b
+        """
+        super().__init__(prompt_formatter, batch_size, concurrency)
+        # require the concurrency to be 1 to avoid race condition during inference
+        if concurrency != 1:
+            raise ValueError(
+                "VicunaModelGenerator currently only supports concurrency 1"
+            )
+        self._model_name_or_path = model_name_or_path
+        import torch
+        from transformers import (
+            AutoModelForCausalLM,
+            AutoTokenizer,
+            TextIteratorStreamer,
+        )
+
+        if torch.cuda.is_available():
+            self._model = AutoModelForCausalLM.from_pretrained(
+                model_name_or_path, torch_dtype=torch.float16, device_map="auto"
+            )
+        else:
+            raise ValueError("VicunaModelGenerator currently only supports GPU")
+        self._tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+
+    def _format_prompt(self, message: str, system_prompt_opt: str) -> str:
+        if system_prompt_opt is not None:
+            return f"""{system_prompt_opt}
+
+          USER: {message}
+          ASSISTANT:
+          """
+        else:
+            return f"""A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
+
+          USER: {message}
+          ASSISTANT:
+          """
+
+    def _generate(
+        self, prompts: list, temperature: float, max_tokens=256, system_prompt=None
+    ) -> BatchGenerateResult:
+        from transformers import pipeline
+
+        all_formatted_prompts = [
+            self._format_prompt(message=message, system_prompt_opt=system_prompt)
+            for message in prompts
+        ]
+
+        top_p = 0.95
+        repetition_penalty = 1.15
+        pipe = pipeline(
+            "text-generation",
+            model=self._model,
+            tokenizer=self._tokenizer,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            repetition_penalty=repetition_penalty,
+            return_full_text=False,
+        )
+        responses = pipe(all_formatted_prompts)
+        rows = []
+        for index, response in enumerate(responses):
+            response_content = response[0]["generated_text"]
+            row_generate_result = RowGenerateResult(
+                is_successful=True,
+                error_msg=None,
+                answer=response_content,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                model_name=self._model_name_or_path,
+                top_p=top_p,
+                repetition_penalty=repetition_penalty,
+                prompts=all_formatted_prompts[index],
+            )
+            rows.append(row_generate_result)
+
+        return BatchGenerateResult(
+            num_rows=len(rows),
+            num_successful_rows=len(rows),
+            rows=rows,
+            is_successful=True,
+            error_msg=None,
+        )
+
+
+class DriverProxyModelGenerator(BaseModelGenerator):
+    def __init__(
+        self,
+        url: str,
+        pat_token: str,
+        prompt_formatter: PromptTemplate,
+        batch_size: int = 32,
+        concurrency: int = 1,
+    ) -> None:
+        """
+        Args:
+            prompt_formatter (PromptTemplate): the prompt format to format the input dataframe into prompts row by row according to the column names
+            model_name (str): the model name
+            batch_size (int, optional): Batch size that will be used to run tasks. Defaults to 1, which means it's sequential.
+
+        Recommendations:
+            - for A100 80GB, use batch_size 16 for llama-2-13b-chat
+        """
+        super().__init__(prompt_formatter, batch_size, concurrency)
+        self._url = url
+        self._pat_token = pat_token
+
+    def _format_prompt(self, message: str, system_prompt_opt: str) -> str:
+        if system_prompt_opt is not None:
+            texts = [f"[INST] <<SYS>>\n{system_prompt_opt}\n<</SYS>>\n\n"]
+            texts.append(f"{message.strip()} [/INST]")
+            return "".join(texts)
+        else:
+            texts = [f"[INST] \n\n"]
+            texts.append(f"{message.strip()} [/INST]")
+            return "".join(texts)
+
+    def _generate(
+        self, prompts: list, temperature: float, max_tokens=256, system_prompt=None
+    ) -> BatchGenerateResult:
+        top_p = 0.95
+
+        all_formatted_prompts = [
+            self._format_prompt(message=message, system_prompt_opt=system_prompt)
+            for message in prompts
+        ]
+
+        import requests
+        import json
+
+        headers = {
+            "Authentication": f"Bearer {self._pat_token}",
+            "Content-Type": "application/json",
+        }
+
+        data = {
+            "prompts": all_formatted_prompts,
+            "temperature": temperature,
+            "max_tokens": max_tokens,
+        }
+
+        response = requests.post(self._url, headers=headers, data=json.dumps(data))
+
+        # Extract the "outputs" as a JSON array from the response
+        outputs = response.json()["outputs"]
+        rows = []
+        for index, response_content in enumerate(outputs):
+            row_generate_result = RowGenerateResult(
+                is_successful=True,
+                error_msg=None,
+                answer=response_content,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                top_p=top_p,
+                prompts=all_formatted_prompts[index],
+            )
+            rows.append(row_generate_result)
+
+        return BatchGenerateResult(
+            num_rows=len(rows),
+            num_successful_rows=len(rows),
+            rows=rows,
+            is_successful=True,
+            error_msg=None,
+        )