feat: create bash library to handle install/run/test of python backends (#2286)

* feat: create bash library to handle install/run/test of python backends

Signed-off-by: Chris Jowett <421501+cryptk@users.noreply.github.com>

* chore: minor cleanup

Signed-off-by: Chris Jowett <421501+cryptk@users.noreply.github.com>

* fix: remove incorrect LIMIT_TARGETS from parler-tts

Signed-off-by: Chris Jowett <421501+cryptk@users.noreply.github.com>

* fix: update runUnitests to handle running tests from a custom test file

Signed-off-by: Chris Jowett <421501+cryptk@users.noreply.github.com>

* chore: document runUnittests

Signed-off-by: Chris Jowett <421501+cryptk@users.noreply.github.com>

---------

Signed-off-by: Chris Jowett <421501+cryptk@users.noreply.github.com>

backend/python/petals/backend.py

@@ -0,0 +1,140 @@
+#!/usr/bin/env python3
+from concurrent import futures
+import time
+import argparse
+import signal
+import sys
+import os
+
+import backend_pb2
+import backend_pb2_grpc
+
+import grpc
+import torch
+from transformers import AutoTokenizer
+from petals import AutoDistributedModelForCausalLM
+
+_ONE_DAY_IN_SECONDS = 60 * 60 * 24
+
+# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
+MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
+
+# Implement the BackendServicer class with the service methods
+class BackendServicer(backend_pb2_grpc.BackendServicer):
+    """
+    A gRPC servicer that implements the Backend service defined in backend.proto.
+    """
+    def Health(self, request, context):
+        """
+        Returns a health check message.
+
+        Args:
+            request: The health check request.
+            context: The gRPC context.
+
+        Returns:
+            backend_pb2.Reply: The health check reply.
+        """
+        return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
+
+    def LoadModel(self, request, context):
+        """
+        Loads a language model.
+
+        Args:
+            request: The load model request.
+            context: The gRPC context.
+
+        Returns:
+            backend_pb2.Result: The load model result.
+        """
+        try:
+            self.tokenizer = AutoTokenizer.from_pretrained(request.Model, use_fast=False, add_bos_token=False)
+            self.model = AutoDistributedModelForCausalLM.from_pretrained(request.Model)
+            self.cuda = False
+            if request.CUDA:
+                self.model = self.model.cuda()
+                self.cuda = True
+
+        except Exception as err:
+            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        return backend_pb2.Result(message="Model loaded successfully", success=True)
+
+    def Predict(self, request, context):
+        """
+        Generates text based on the given prompt and sampling parameters.
+
+        Args:
+            request: The predict request.
+            context: The gRPC context.
+
+        Returns:
+            backend_pb2.Result: The predict result.
+        """
+
+        inputs = self.tokenizer(request.Prompt, return_tensors="pt")["input_ids"]
+        if self.cuda:
+            inputs = inputs.cuda()
+ 
+        if request.Tokens == 0:
+            # Max to max value if tokens are not specified
+            request.Tokens = 8192
+
+        # TODO: kwargs and map all parameters
+        outputs = self.model.generate(inputs, max_new_tokens=request.Tokens)
+
+        generated_text = self.tokenizer.decode(outputs[0])
+        # Remove prompt from response if present
+        if request.Prompt in generated_text:
+            generated_text = generated_text.replace(request.Prompt, "")
+
+        return backend_pb2.Result(message=bytes(generated_text, encoding='utf-8'))
+
+    def PredictStream(self, request, context):
+        """
+        Generates text based on the given prompt and sampling parameters, and streams the results.
+
+        Args:
+            request: The predict stream request.
+            context: The gRPC context.
+
+        Returns:
+            backend_pb2.Result: The predict stream result.
+        """
+        # Implement PredictStream RPC
+        #for reply in some_data_generator():
+        #    yield reply
+        # Not implemented yet
+        return self.Predict(request, context)
+
+def serve(address):
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
+    backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
+    server.add_insecure_port(address)
+    server.start()
+    print("Server started. Listening on: " + address, file=sys.stderr)
+
+    # Define the signal handler function
+    def signal_handler(sig, frame):
+        print("Received termination signal. Shutting down...")
+        server.stop(0)
+        sys.exit(0)
+
+    # Set the signal handlers for SIGINT and SIGTERM
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTERM, signal_handler)
+
+    try:
+        while True:
+            time.sleep(_ONE_DAY_IN_SECONDS)
+    except KeyboardInterrupt:
+        server.stop(0)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run the gRPC server.")
+    parser.add_argument(
+        "--addr", default="localhost:50051", help="The address to bind the server to."
+    )
+    args = parser.parse_args()
+
+    serve(args.addr)